Big data: A catalyst for change

Southern University of Denmark, Campus Slagelse Department of Leadership and Strategy

Big Data: A Catalyst for Change - An integration study of Business Intelligence & Change Management

by Rune Palm Hansen Eks. nr. 305573, Cand.merc., Forandringsledelse

6. October 2014

Big Data: A Catalyst for Change - An integration study of Business Intelligence & Change Management Master Thesis in Change Management, MSc in Business Economics and administration

Author: Rune Palm Hansen Supervisor : Anders Bordum

Det erklæres herved på tro og love, at undertegnede egenhændigt og selvstændigt har udformet denne rapport. Alle citater i teksten er markeret som sådanne, og rapporten eller væsentlige dele af den har ikke tidligere været fremlagt i anden bedømmelsessammenhæng.

_________________________________ Rune Palm Hansen

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1. Table of Contents 1.

Table of Contents ............................................................................................................................ 3

2.

Introduction .................................................................................................................................... 8

3.

4.

5.

2.1.

Big Data: A Revolution That Will Transform How We Live, Work and Think ........................... 8

2.2.

The speed of change ............................................................................................................... 9

2.3.

Lack of organisational research on Business Intelligence ........................................................ 9

Research Approach ....................................................................................................................... 10 3.1.

Research goal ........................................................................................................................ 10

3.2.

Research approach ................................................................................................................ 10

Methodology ................................................................................................................................. 12 4.1.

A functionalistic perspective ................................................................................................. 12

4.2.

Practical relevance ................................................................................................................ 13

4.3.

Validity................................................................................................................................... 14

4.4.

Initial literature analysis ........................................................................................................ 14

4.4.1.

Business Intelligence & Big Data.................................................................................... 15

4.4.2.

On the quality and contents of conference proceedings .............................................. 17

4.4.3.

Change Management .................................................................................................... 18

Change Management .................................................................................................................... 21 5.1.1.

Literature Selection ....................................................................................................... 21

5.2.

History of Change Management............................................................................................ 21

5.3.

Defining Change Management .............................................................................................. 22

5.4.

Nature of change ................................................................................................................... 24

5.4.1.

Rate of occurrence ........................................................................................................ 24

5.4.2.

How change comes about ............................................................................................. 25

5.4.3.

The scale of change ....................................................................................................... 26

5.5.

Critique of Change Management literature .......................................................................... 28

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5.5.1.

The myth of a 70% failure rate ...................................................................................... 28

5.5.2.

Lack of internal validity .................................................................................................. 29

5.5.3.

Limited methodological repertoire and replication ....................................................... 29

5.5.4.

Pro-change Bias ............................................................................................................. 30

5.6.

6.

5.6.1.

Resistance to change ..................................................................................................... 31

5.6.2.

Drivers for change ......................................................................................................... 32

5.6.3.

Change outcomes .......................................................................................................... 34

5.6.4.

Sustainability / Stickiness............................................................................................... 35

Business Intelligence & Big Data.................................................................................................... 36 6.1.

Literature selection on BI ...................................................................................................... 36

6.2.

Business Intelligence ............................................................................................................. 37

6.2.1.

The Business Intelligence Literature .............................................................................. 37

6.2.2.

Defining Business Intelligence ....................................................................................... 38

6.2.3.

Business Intelligence technique and process ................................................................ 39

6.2.4.

Business Intelligence Maturity Models .......................................................................... 39

6.2.5.

BI 1.0 & BI 2.0 ................................................................................................................ 40

6.3.

Data, Information & Knowledge ............................................................................................ 40

6.3.1.

Epistemology of data, information & knowledge .......................................................... 40

6.3.2.

Hierarchy of data, information & knowledge ................................................................ 41

6.3.3.

Defining Data ................................................................................................................. 41

6.3.4.

Defining information ..................................................................................................... 41

6.3.5.

Defining knowledge ....................................................................................................... 42

6.3.6.

From data to knowledge ............................................................................................... 42

6.4.

7.

Topics in change management .............................................................................................. 30

Difference between Big Data and Business Intelligence........................................................ 43

Big Data ......................................................................................................................................... 44 7.1.

Literature selection on Big Data ............................................................................................ 44 4

7.2.

What is Big Data? .................................................................................................................. 44

7.3.

The Many V’s of Big Data....................................................................................................... 45

7.4.

Volume .................................................................................................................................. 46

7.4.1.

A cornucopia of data ..................................................................................................... 46

7.4.2.

Scarcity of professionals ................................................................................................ 47

7.4.3.

Collecting all the data .................................................................................................... 47

7.5.

Variety ................................................................................................................................... 47

7.5.1.

Unstructured and structured data................................................................................. 48

7.5.2.

Repetitive and non-repetitive data................................................................................ 48

7.6.

Velocity .................................................................................................................................. 48

7.6.1.

Expanding limits in data representation ........................................................................ 49

7.6.2.

Perceptual filters ........................................................................................................... 49

7.6.3.

Conceptual filters .......................................................................................................... 49

7.6.4.

Preservation filters ........................................................................................................ 50

7.7.

Veracity ................................................................................................................................. 50

7.7.1.

Data Cleansing ............................................................................................................... 50

7.7.2.

Messy data .................................................................................................................... 51

7.7.3.

Friction in data exchanges ............................................................................................. 51

7.8.

Value ..................................................................................................................................... 51

7.8.1.

2 + 2 = 5 ......................................................................................................................... 52

7.8.2.

The half-life of data value .............................................................................................. 52

7.8.3.

Repurposing data .......................................................................................................... 53

7.8.4.

The value personal data ................................................................................................ 54

7.9.

Implications of Big Data ......................................................................................................... 55

7.9.1.

A changing paradigm ..................................................................................................... 55

7.9.2.

Cost of data-driven science ........................................................................................... 56

7.9.3.

Data-driven discovery .................................................................................................... 56 5

8.

7.9.4.

Critique of Big Data ....................................................................................................... 57

7.9.5.

Data-driven science ....................................................................................................... 57

A Data -> Change model ................................................................................................................ 59 8.1.

Tacit and Explicit knowledge ................................................................................................. 59

8.2.

Knowledge creation............................................................................................................... 60

8.3.

Knowledge sharing ................................................................................................................ 60

8.4.

Structured and unstructured knowledge .............................................................................. 61

8.5.

A Data -> Change Model........................................................................................................ 61

9.

Four Modes of Change .................................................................................................................. 63 9.1.

Data Structure ....................................................................................................................... 63

9.2.

Data Collection Methods ....................................................................................................... 64

9.3.

Information Characteristics ................................................................................................... 66

9.4.

Knowledge Criteria ................................................................................................................ 67

9.5.

Decisions Making ................................................................................................................... 69

9.5.1.

Differentiation and Consolidation Theory ..................................................................... 69

9.5.2.

Types of Social Action .................................................................................................... 70

9.5.3.

Decision-making and Business Intelligence ................................................................... 71

9.6.

Organizational interpretation modes .................................................................................... 72

9.7.

Modes of change ................................................................................................................... 73

10.

Conclusion ................................................................................................................................. 77

10.1.

The Monkeys, the Banana and the Shower ....................................................................... 77

10.2.

Big Data: A Change Catalyst............................................................................................... 78

10.3.

Suggestions for further research ....................................................................................... 79

11.

Bibliography............................................................................................................................... 80

12.

Appendix I: Review literature on Change Management .............................................................. 0

13.

Appendix II: Review literature on Business Intelligence .............................................................. 0

14.

Appendix III: Literature on Big Data ............................................................................................. 0 6

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2. Introduction 2.1. Big Data: A Revolution That Will Transform How We Live, Work and Think The above is the title of Viktor Mayer-Schonberger’s new book published in 2013 that has been highly debated, even in academic circles. The book promises huge benefits from the improved forecasting models and advanced algorithms that is said to revolutionize all from health care and finance, to the very way we live, work, and even think. Big Data allows us to process more data, much faster than ever before. It will, however, be on the cost of causality and understating, and the new paradigm might mean the ‘demise of the expert’, leaving decision making in the hands of the computer, warns Mayer-Schonberger (2013).

Big Data has in a few years received incredible attention both by practitioners and researchers. There are several stories of larger companies that have utilized Big Data to get ahead of the competition.

Netflix for example, who have analysed its huge collection of data from its viewers to combine the most popular director, actor and story in the making of its new TV show, House of Cards. The show was deemed a success even before it was launched (Simon, 2014) Big Data is a new area of Business Intelligence (BI), a field that also has been drawing increasing attention for the last decade. Many companies have invested in large data warehouses and larger companies have dedicated business intelligence departments to gather and analyse the huge and increasing amount of data available both inside and outside the company.

Even so, the Gartner Group (leading IT market analyst firm) estimates that up to 70 % or 80 % of business intelligence projects fail due to poor communication between IT and business and poorly analysed or rapidly changing business needs (Goodwin, 2014). And a recent survey of 600 executives in the US and UK showed that only 21 percent said they routinely use analytics very successfully as part of an integrated enterprise-wide approach (14% in 2009), (Wolpe, 2013).

The increasing demand for business intelligence has led to a global shortage of BI professionals. In January 2014, James Goodnight, CEO of SAS Institute, one of the leading BI firms, called the skills shortage the biggest problem for business analytics (Matt Hartley, 2014). A 2011 research from the McKinsey Global Institute estimates a shortage of 140-190.000 people with deep analytic skills as well a 1.5 million ‘data-savvy’ managers and analysts to take full advantage of the big data revolution. And that is only in the US alone (Manyika et al., 2011, p. 3).

For now the benefits of Big Data seem limited to the biggest corporations, who can hire the best datasavvy professionals, and have the competences needed to overcome the organisational and communicational challenges.

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2.2. The speed of change This is a thesis in Change Management, and the interesting thing about the new phenomenon of Big Data, is what consequences and relations it might have to change management and to companies implementing change.

McCarthy & Eastman define the overarching purpose of change management as to â€œâ€Śaccelerate the speed at which people move successfully through the change process so that anticipated benefits are achieved fasterâ€? (McCarthy & Eastman, 2010, p. 4). Given that the purpose of Business Intelligence (and Big Data) is to generate knowledge in order to make better decisions, Change Management and Business Intelligence seem to be heads and tails of the same process.

If Big Data are used by companies to make quicker, more frequent and more accurate decisions, it is likely that Big Data increases the amount of change an organisation experiences, and so Big Data becomes as a catalyst for change in organisations. In a changing environment, better performing companies will equal faster changing organisations.

2.3. Lack of organisational research on Business Intelligence Besides a shortage of both IT professionals and capable analysts, as well as a still increasing complexity in both technologies and the data creation process, the end product of business intelligence remains somewhat undocumented.

According to an academic literature review by Shollo and Kautz (2010), the BI literature is characterized by normative theories of what should happen as the result of a BI process and how it could enable better decision making in organisations, and is built on an assumption that there exists a transformation process from data to information and from information to knowledge that will ultimately lead to making better decisions (Shollo and Kautz 2010).

In a recent study of the use of the BI output in organisations by Shollo (2013), the BI creation process and its output is not only subject to rational decision making, but also subject to political and symbolic uses, and decision makers even showed distrust towards the BI output because of the political incentives involved in the BI creation process (Shollo, 2013, p. 236). Furthermore, the BI output was used in power relations to legitimize the claims of the decision-maker (Shollo, 2013, p. 216).

Not much academic research exists in the actual use of BI and its output in an organisational decision making process, whether or not the use of BI actually leads to better decisions or knowledge, nor how organisations might improve the use and implementation of business intelligence.

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3. Research Approach Big Data is a new phenomenon with some still unclear consequences for science, for business and for society. This thesis will examine Big Data as a new part of Business Intelligence, and how it might be possible to integrate Business Intelligence and Change Management, so to gain an understanding on how Big Data affect changes in organisations.

3.1. Research goal The thesis will therefore examine the following research question:  How can we integrate Business Intelligence and Change Management, in order to get an understanding of how organisations use knowledge creation in their change process?

3.2. Research approach As stated in the introduction, Business Intelligence is a field where most of the literature comes from practitioners and very little academic literature exists. Change Management on the other hand features a long research tradition and a large body of knowledge, but the literature is fragmented and has unclear boundaries to other fields. Big Data has only existed for 3 years, still is published in more articles in 2013 than the rest of Business Intelligence and Change Management combined.

Change Management is a broad field that includes overlaps and theory from fields such as economics, psychology, sociology or engineering human and organisational behaviour (Barends, Janssen, ten Have, & ten Have, 2013, p. 6; Garg & Singh, 2006, p. 46). Most of the literature focus mostly on change implementation and is very vague when it comes to questions of how organisations in practice scan their surroundings in order to change accordingly to their surroundings.

Business Intelligence on the other hand is exactly concerned with how an organisation can scan both its internal and external environment and turn this data into actionable information that in the end can facilitate change. Big Data takes this to new and previously unimagined heights and makes organisations (and parts of society) able to react to changes faster and more precise than ever before.

Even though there exist lots of technical research on these topics, the field is very abundant of academic literature when it comes to organisational research involving Business Intelligence.

In order to answer our research question, it is necessary to examine the body of knowledge on respectively Business Intelligence and Change Management. Furthermore, it is necessary to examine Big Data in particular as it now already a big part of the Business Intelligence literature.

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The thesis will therefore include three literature reviews. But in order to remain within the limited resources of a thesis, this will be done through a meta-review of reviews in the literature. However, as it shows, there is not much review literature on Business Intelligence or Big Data, so here I have taken a different approach.

The literature reviews aim at answering the following questions:  What is Change Management and what do we know about Change Management?  What is Business Intelligence and what do we know about Business Intelligence?  How does Big Data differ from Business Intelligence?

The literature selection and methods will be described in the respective chapters.

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4. Methodology 4.1. A functionalistic perspective In order to create a unified view of Change Management and Business Intelligence, it necessary to adopt a functionalistic perspective on organisation. In sociology there has historically been a division between theories focused on explaining social order and equilibrium and those focused on explaining social conflict and change (Burrell & Morgan, 1979, p. 10).

(Burrell & Morgan, 1979, p. 22)

While it is possible to encompass the order and conflict perspective with the same theoretical framework, the focus on order and functionalism is in line with the view on both Business Intelligence and Change Management as functions or processes within an organisation. Many definitions of Change Management and Business Intelligence focus on its inputs and outputs. This helps to initially create a framework where the output of Business Intelligence (knowledge/decisions) is used as the input in the Change Management process.

Of course, a model of change focuses not on order, but change. Order in this sense is the generation of so called first-order change where the system of the organisation remains unchanged. In the framework, knowledge creation and sharing is implemented to account for higher orders of change.

Had the initial focus been on the conflicts in the organisation, the thesis might have been about the conflicting paradigm of different parts of the organisation, in particular that of the IT department and the management, and how opposing views on change might be an obstacle in both the Business Intelligence and the Change Management Process. Or it could be a more traditional Change

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Management study on the conflicts between the change management and the change resisting employees.

An alternative approach is Shollo (2013), who uses an interpretive perspective to examine how the product of Business Intelligence process is used in the decision-making process (Shollo, 2013, p. 232).

Shollo interestingly finds that besides its informational use, the Business Intelligence product also has symbolic, political and functional implications in organizational decision-making (Shollo, 2013, p. 233).

Also an interesting area for further research, but out of the scope for this thesis.

4.2. Practical relevance In their two well-cited articles, Anderson, Herriot, & Hodgkinson argue for an evaluation of managerial science from a practice-revelant view, in a continuation of long debate of what is called the theory-relevance gap or practioner-researcher gap in scientific knowledge production (Anderson, Herriot, & Hodgkinson, 2001a, 2001b).

This issue stems from an increasing questioning among the various consumers of academic research regarding the applied research policy (Anderson et al., 2001a, p. 42). The critique is a response to an increasing theory-relevance gap in recent years and an identified decline of involvement of practitionerns in the publication process (Anderson et al., 2001a, p. 43). The literature follows a trend of replication-extention studies without testing theory and addresing releveant problem issues.

(Anderson et al., 2001a, p. 43).

Anderson, Herriot, & Hodgkinson present a two-dimensional model in which science is differentiated in its tehoretical/methodological rigour and practical relevance. This gives a typology for categorising research as purely science, popularist science, pedantic science and pragmatic science:

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(Anderson et al., 2001a, p. 42, 2001b, p. 394) This thesis boarders both the areas of Change Management and Business Intelligence. In a review of the change literature Wetzel & Van Gorp conclude that “Astonishingly, It is virtually unknowable how […] theory really is or which of the available theories are applied and which not.” (Wetzel & Van Gorp, 2014, p. 117). The question of Change Management’s practical relevance remains unanswered.

Regarding Business Intelligence, on the other hand, the literature is fragmented and future research is hindered by lack of clear definitions (Azevedo & Santos, 2009, p. 296). The Business Intelligence literature is there not very methodologically or theoretically rigid, but as it is field developed by practitioners and as much of the theory is inspired hereof, it definitely has a more practical relevance.

The goal of this thesis is to create a unified framework for Business Intelligence and Change Management, and the framework therefore aims at drawing on and combining the more practical nature of the Business Intelligence with the more mature research field of Change Management. The framework is built on distinguished theories and encompasses the most prominent aspects of both Business Intelligence and Change Management.

4.3. Validity Studies with a high external validity (generalizability with respect to populations and context), usually suffer from a low internal validity (precision in control and measurement) and vice versa. A balance that Is always a trade-off (Barends et al., 2013, p. 9).

This thesis aims at creating what could be characterised as middle-range theory. Middle-range theory generally has the advantage of an increasing capacity of changing the way we think about the world, and at the same time a higher generalizability (Saunders, Lewis, & Thornhill, 2007, p. 37) As this thesis is an attempt to integrate Business Intelligence and Change Management, it aims at having the highest general applicability. Internal validity is sought by building on well-respected scholars within the fields, as well as conforming to agreed definitions and paradigm, while incorporating major disagreements.

4.4. Initial literature analysis To begin with, I will make a short literature analysis of the key research areas discussed in this thesis, their emergence and trend in the literature. The analysis is done by searching the Scopus search engine, because of its extensive possibilities of analysing and extracting data regarding the search result. The analysis is done by using a broad search criteria and extracting and analysing the data from Scopus.

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4.4.1. Business Intelligence & Big Data Business Intelligence is historically originating from the areas of Competitive Intelligence and Decision Support Systems and is related to the emergent field of Big Data. This thesis is focused on Business Intelligence and Big Data. Competitive Intelligence and Decision Support Systems are included in this analysis only for the purpose of comparison.

In the following I have made a simple search on each of the areas with the area title as a keyword looked up in the article title, abstract or subject. The below table shows the number of articles returned, as well as a cross-search for articles overlapping in two fields in absolute numbers and percentage: Number of articles Decision support systems

Competitive Intelligence

Business Intelligence

Decision support systems

59.316

0,3%

Competitive Intelligence

203

6.209

Business Intelligence

431

928

3.700

Big Data

81

19

81

Big Data

0,7%

0,1%

9,4%

0,1% 0,6% 9.000

Source: Scopus.com

The table shows that of the four areas, Business Intelligence and Competitive Intelligence are the most overlapping, followed by Business Intelligence and Decision Support Systems. A double check have confirmed that the 81 articles shared between Big Data and Business Intelligence, and Big Data and Decision Support Systems are exactly the same 81 articles, meaning that Big Data has as much in overlap with Business Intelligence as with Decision Support Systems.

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Number of articles over time 1200

6000

1000

5000

800

4000

600

3000

400

2000

200

1000

0

0 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 Business Intelligence

Competitive Intelligence

Decision Support Systems

Big Data

Source: Scopus.com

In diagram above I have plotted the number of articles over time, showing the relative popularity trend of the four research areas. Please note that Decision Support Systems and Big Data are adjusted in scale and uses a secondary axis. The figure shows that Competitive Intelligence got hit by a major popularity setback around 2008 (possibly in relation to the financial crisis) and was overtaken by Business Intelligence shortly after. It also shows that Big Data has received an incredible attention and had overtaken an established field like Decision Support Systems in only 2-3 years.

Subject Areas BUSINESS INTELLIGENCE Materials Science Economics, Econometrics and Finance

Decision Sciences

Other

Biochemistry, Genetics and Molecular Biology

Materials BIG DATA Science Other

Medicine Computer Science

Social Sciences Mathematics

Business, Management and Accounting

Computer Science

Medicine Decision Sciences Business, Management and Accounting

Social Sciences Mathematic s

Engineering

Engineering

Source: Scopus.com

The two new circle diagrams above show the top 10 subject areas of Business Intelligence and Big Data. It shows that both areas of Business Intelligence and Big data are dominated by the technical

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subjects such as Computer Science, Engineering and Mathematics. Subjects such as Decision Making, Business, Management and Accounting and Social Sciences have a moderate place in both areas.

Document types

Big Data

31%

Business Intelligence

30%

Competitive Intelligence

Decision Support Systems

Article

55%

4%2% 8%

55%

43%

4% 5% 6%

37%

50%

Conference Paper

12% 0% 8%

39%

Review

Conference Review

5%1%5%

Other

Source: Scopus.com

Another way of looking at the search result is focused on the document types. The main distinction here is between articles published in academic journals and articles published as conference proceedings. It shows a great difference in the areas as to where the articles are published. The established areas of Decisions Support Systems and Competitive Intelligence are primarily published in journals, whereas the areas of Big Data and Business Intelligence are dominated by conference proceeding. This finding leads to a discussion of the quality of conference proceedings and journals’ papers.

4.4.2. On the quality and contents of conference proceedings As a result of the above findings, it is relevant to discuss the quality and conference differences between academic journals and conference proceedings. As it is not the central issue of this thesis, it will only be a short discussion of main issues.

The difficulties in comparing the quality between conference papers and academic journals is that there exists no ranking system for conferences, as there is for journals, for example the ISI impact factor. That is usually the one of the main approaches for assessing the scientific impact of an author or an article. Frayne et al. (2010) take a different approach and examine 8,764 conference and journal papers within Computer Science using Google Scholar Impact Factor. After first establishing a correlation between ISI impact factor and Google Scholar Impact Factor, they use Google Scholar Impact Factor to compare conference and journal papers. They find that “… leading conferences

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compare favourably to mid-ranking journals (Freyne, Coyle, Smyth, & Cunningham, 2010, p. 125). Said in other words, high-ranking journals score significantly higher than conferences.

In a similar fashion, Franceschet (2010) uses Google Scholar h-index, an index to rate an author’s productivity and impact1, to do a bibliometric analysis of publication patterns in computer science.

Franceschet finds “… that high-impact scholars publish significantly less than prolific ones, and more frequently in journals” (Franceschet, 2010). High-impact scholars publish 40% of their articles in journals, where the most prolific scholars publish 33% of their work in journals. Since the h-index is a measure of productivity and impact (e. g. citations), Franceschet findings show that journal articles, within computer science, are roughly getting citted 20% more that conference proceedings. It would however, have been interesting to also see how mid- and low- ranking scholars would get their paper published.

On the positive side, conference papers have a shorter time before going into print (around 7 months), as opposed to 1-2 years for journals (Patterson, Snyder, & Ullman, 1999, p. 2), which might make it more relevant for practitioners. But it also gives journal author more time to polish their work (Patterson et al., 1999, p. 2). Though the internet might have reduced this time significantly, journal papers still go through a significantly longer review-process than conference papers where the review, according to Vardi, often is done “… under extreme time and workload pressures, and it does not rise to the level of careful refereeing” (Vardi, 2009, p. 5).

This assumption is recently confirmed by the work of computer scientist Cyril Labbé that revealed more than 120 computer-generated conference proceedings. The Institute of Electrical and Electronic Engineers (IEEE), who published over 100 of these complete nonsense conference papers, refused to comment on the review process for implicated conferences (Van Noorden, 2014).

4.4.3. Change Management The area of Change Management has origins in Organisational Development and Organisational Transformation and a close relation to Business Process Reengineering. A literature search has been done on Scopus on each of these areas in the same manner as in the previous section, with the subject as keywords in the article abstract, title and subject terms. The results are shown in the table below:

1

The h-index is defined as the number of papers with citation number >=h (Hirsch, 2005, p. 16569) 18

Number of articles and overlapping areas Organisational Development

Organisational Transformation

Business Process Reengineering

Organisational Development

2.873

0,6%

Organisational Transformation

22

710

Business Process Reengineering

7

8

1.428

Change Management

122

37

83

Change Management

0,2%

1,3%

0,4%

0,5% 1,0% 6.692

Source: Scopus.com

Besides the total amount of articles, the table shows overlaps between the areas, in absolute numbers and percentage. It shows that there is only a slight overlap between the areas of Change Management and Organisational Development and between Change Management and Business Process Reengineering.

Number of articles over time 300

800 700

250

600 200

500

150

400 300

100

200 50

100

0

0

1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 Organisational Development

Organisational Transformation

Business Process Reengineering

Change Management

Source: Scopus.com

The results show when the concepts emerge and their relative trend over time. As the figures show, Organisational Development emerges slowly as a concept in the 1960s, complemented by Organisational Transformation in the end of the 1980s, and Change Management and Business Process Reengineering in the mid-1990s. It further shows a remarkable resemblance in popularity rise since 2004. Since the total number of articles published each year increases every year, as seen in the diagram below, it safe to say that the relative flat curves of Organisational Transformation and Business Process Management can be seen as a decline in popularity of the fields.

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(The Economist, 2010, p. 12) The below analysis of document types reveals a large overweight of journal articles in all the areas, especially when compared to areas in the previous chapter. There is also a tendency towards the trend where old and established fields are being published more in journals than in conference proceedings. In the previous chapter it was discussed what the cause might be, and there seems to be a correlation between subject maturity and the source in what articles get published, but more research into the area is required before further conclusions can be reached, and this is out of scope of this thesis.

Document types

Change Management

56%

Business Process Reengineering

26%

49%

Organisational Transformation Organisational Development

Article

20%

30%

Conference Paper

4% 6%

21%

66% 10%

18%

41% 61%

0%

0%

10% 40%

Review

50%

60%

70%

9% 11% 80%

10% 13%

90%

100%

Conference Review

Source: Scopus.com

This chapter has given an overview of the literature within the fields used in this thesis. It gives the indication of a large and mature field of Change Management, a yet not so established but large field of Business Intelligence, and a purely new and booming research field of Big Data.

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5. Change Management This chapter is a review of the Change Management literature and will try to answer the following question:  What is Change Management and what do we know about Change Management?

5.1.1. Literature Selection The literature on (Organisational) Change Management (CM/OCM) is, to say the least, vast. Having been a separate research field for more than 20 years and drawing on literature and research traditions going more than 50 years back, there are now more than 20.000 articles on Change Management and a still increasing interest in the subject (Barends et al., 2013, p. 6).

Several reviews have already been made to gather and organise this huge amount of literature, and this chapter will therefore focus on a meta-review of the newest reviews on change literature. A literature search has been made using several academic search engines (EbscoHost, Scopus, Web of Science) aimed at finding literature reviews on organisational change using the simple keywords ‘change’, ‘organisation’/’organization’ and review, as well as using ´review´ as a type filter where available . The result is 18 literature reviews within the last 15 years (2000-2014). A list of these can be found in appendix I

5.2. History of Change Management Change Management originates from the theories of Organisational Development (OD) emerging in the 1960s. One of the earliest, and yet still often cited article on change is by Lewin Frontiers in group dynamics from 1947. In the beginning, OD was focused primarily on changes ‘within already accepted frameworks’ (Bartunek and Louis 1988 p. 100), often referred to as incremental or first-order change (see chapter Error! Reference source not found.). During the 1980s, focus gradually shifted towards ore radical, second-order change due to increasing environmental changes that led to massive restructurings of half of the top US companies. This approach is often referred to as Organisational Transformation (OT) (Dunphy & Stace, 1993, p. 906).

Change Management differs from OD and OT, not in the type of change in focus, but rather in the scope and extent of areas, tools and professions used in the change effort. Where OD focuses more on the individual and group level, CM has more emphasis on the role of structure and systems, and sees itself “… as only one component of a larger organizational change effort, the other components being strategy, business processes, and technology” (Worren, Ruddle, & Moore, 1999, p. 277).

Worren et al. analyses the ways Change Management is used by leading consulting companies and 21

concludes that Change Management is a synthesis of OD and Business Process Reengeneering movement in the early 1990s, thus integrating OD’s focus on the human dimension with BPR’s focus on strategy, technology and market (Worren et al., 1999, p. 284).

5.3. Defining Change Management The Change Management literature incorporates research from a wide array of disciplines, including areas such as economics, psychology, management science, business administration, sociology, engineering human and organisational behaviour (Barends et al., 2013, p. 6; Garg & Singh, 2006, p.

46). This diverse selection of disciplines could be for the reason that there seems to be very little agreement among the very few who try to define Change Management, and this in turn has made the disciplines’ boundaries fuzzy and difficult to describe (Barends et al., 2013, p. 6). Other explanation.

As noted by Rothwell 2010, is that the term is just quite ‘self-explanatory’ (Rothwell, 2010, p. 19). Not even the abbreviation is consistent as both the terms Organisational Change Management (OCM) and Organisational Change Research (OCR) are used in the literature with each their respective abbreviations, thus complicating literature search on the topic.

Few of the aforementioned 18 reviews take time to define change management and those that do, do it only on a very superficial level. Garg & Singh give a very broad definition of change as “… the effective management of a business-change” (Garg & Singh, 2006, p. 46), and Barends et al defines change management as: “Organizational change management entails interventions intended to influence the taskrelated behavior and associated results of an individual, team or entire organization” (Barends et al., 2013, p. 6)

The definition above might be broad, but it reveals a perception that is common for the largest part of the Change Management literature on change interventions and change as a conscious and intentional process. There exists however, an increasing part of the literature that includes the unintentional aspects of change. One of these is Schimmel & Muntslag, who define change as “… a collective learning process” (Schimmel & Muntslag, 2009, p. 400). As seen by this definition, Change Management is often closely related to the knowledge creation process and he field of Knowledge Management, as seen in the definition of change from Saeed, Bowen, & Sohoni as “focused manufacturing knowledge” (Saeed, Bowen, & Sohoni, 1993, p. 54).

Even though Change Management is broadly defined, there are some aspects which are rarely included. As noted by Lewin in his original text from 1947, a goal of change is to establish group norms, which serves to ‘… stabilize the individual conduct on the new group level’ (K. Lewin, 1947, p.

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36). Alternatively, Tichy provides a definition of change as the ‘.. introduction of new patterns of action, belief and attitudes among substantial segments of a population’ (Tichy, 1983, p. 17). Seen by both, change is definitely something more than things happening on the individual level or simply aggregated actions of individuals. Another definition from Anderson and Anderson describe change as: “… a set of principles, techniques, and prescriptions applied to the human aspects of executing major change initiatives in organizational settings. Its focus is not on ‘what’ is driving change (technology, reorganization plans, mergers/acquisitions, globalization, etc.), but on ‘how’ to orchestrate the human infrastructure that surrounds key projects so that people are better prepared to absorb the implications affecting them.”

(Anderson, L.A., & Anderson, 2010, p. xxviii)

In the above definition, Anderson & Anderson not only define what Change Management is, but also partly what it is not. Most change management literature focuses on the implementation of change and not on the decision to change and what lies before it.

In addition, the commonality that many change management theories share is the implicit assumption that change, development or transformation is necessary as the organisation needs to adapt to changes in its environment in order to survive. Some theories go as far as to see change as a continuous process: “the process of continually renewing an organization’s direction, structure, and capabilities to serve the ever-changing needs of external and internal customers” (Moran and Brightman, 2001: 111)

Most theories view change as a conscious and strategic process. The following definition depicts a common view in the literature where Change Management is portrayed as originating from the tension between the current state and a desired state, from which Change Management guides a change process towards a future state that (hopefully) resembles the desired state.

“In Change Management at least three fields are studied: the current state of the organisation, the state the organisation should reach in the future, and finally how to guide this conversion from the current state to the desired state” (Nakhoda & Alidousti, 2011, p. 540)

In the following chapter the author of this paper will go through the different aspects in the change management literature.

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5.4. Nature of change One of the most discussed topics in change literature is the nature of change. This is a discussion of different kinds of change that can affect the organisation in different ways and needs different approaches to be managed. In his review of the change literature, By (2005) conceptualises three different aspect of change in which this discussion can be divided; The rate of occurrence, how it comes about and its Scale. As with other areas in the Change Management literature, there is no consensus on terminology as each author seems to mark himself by inventing his own terminology for describing the same approaches (Todnem By, 2005, p. 371). Characteristic for all the theories of the nature of change is that they don’t seem to be grounded in any empirical research.

5.4.1. Rate of occurrence Almost self-explanatory, the rate of occurrence relates to how often organisations are faced with necessity to change. This is also sometimes called rhythm or pattern of change (Weick & Quinn, 1999, p. 361), an indication that change always comes at a constant rate (L. Nelson, 2003, p. 18). The most common distinction is whether the organisation is changing continuously, is characterised by episodic periods of larger change, or can be characterised by larger periods of stability (Todnem By, 2005, p.

371).However there exist many different versions, terms and combinations as illustrated by the table below:

(Todnem By, 2005, p. 317)

Some of the distinctions also include aspects on how change comes about and the scale of change and since these aspects will be discussed in the following sections, the models themselves will not be further discussed.

The Change Management literature in general has undergone a shift from discontinued models of change towards models of continuous change (Todnem By, 2005, p. 371). While earlier models argued that stability was preferred because people need routines (Luecke, 2003, p. 71; Rieley & 24

Clarkson, 2001, p. 162), it is now argued that organisations need to constantly evolve and it is vital that people are able to undergo continuous change, a necessity caused by a continuously changing environment (Rieley & Clarkson, 2001, p. 161; Todnem By, 2005, p. 372). Furthermore, episodic change causes larger and more turbulent change when it occurs because of an accumulated need for change (L. Nelson, 2003, p. 18). This in turn can create more resistance (Luecke, 2003, p. 72) and continuous change is therefore preferable because it ‘smoothens’ the experience of change as it comes more often but in smaller amounts at a time.

5.4.2. How change comes about The most dominant distinction in how change is initiated and ‘comes about’ is between planned and emergent change (Todnem By, 2005, p. 373). The planned change approach has been dominant in the literature the last 50 years (Bamford & Forrester, 2003, p. 546), but since the 1980s the emergent model has gained steady ground (Burnes, 1996, p. 546).

The planned change approach originates from Lewin’s work in the middle of the century, a model often referred to as the Unfreeze-Move-Freeze model (K. Lewin, 1947, p. 34), in which social habits or customs are broken/unfrozen, moved to a higher level and then freeze/establish new habits (Kurt Lewin, 1951, p. 35). This view sees a change process as predefined steps an organisation must pass through in order to move from an unsatisfactory state to an identified desired state (Burnes, 1996, p.

12). Bullock & Batten in their review, synthesise 30 different planned change models into a four phases model: (1) Exploration, (2) Planning, (3) Action, (4) integration (Bullock & Batten, 1985, p.

400), and criticising, together with Burke, Lewin’s model to be “simple to state but not simple to implement” (Bullock & Batten, 1985, p. 391; Burke, 1982, p. 48).

The emergent change approach and its supporters seem to agree on a critique of the planned change approach, but much less agree on a specific alternative (Bamford & Forrester, 2003, p. 547). The common critique is that change cannot occur and be planned from one stable state to another, as the business environment today is changing too fast and the needs for change will change before the change initiative is completed (Bamford & Forrester, 2003, p. 548; Burnes, 1996, p. 11). Furthermore, the prescribed approach relies too much on the manager’s role, and assumes that everyone will follow and agree on the path decided (Bamford & Forrester, 2003, p. 547; Todnem By, 2005, p. 374).

For these reasons the emergent approach sees change as more open-ended and in theory as a continuous learning process, and it tends to focus largely on overcoming the employee’s resistance, even if it means change of plans (Todnem By, 2005, p. 374). It relies instead on reaching an understanding of the often complex issues involved and identifying a range of possible options

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(Bamford & Forrester, 2003, p. 548). To some extent, emergent change relies more on a “bottom-up” action rather than “top-down” control (Bamford & Forrester, 2003, p. 548).

Famous subscribers to the emergent change perspective include Kotter’s eight steps for organisational transformation (Kotter, 1995, p. 61), Luecke’s seven steps to change (Luecke, 2003, p.

31), Kanter, Stein, & Jick’s ten commandments for executing change (Kanter, Stein, & Jick, 1992, p.

382), or Weick & Quinn’s unfreeze, transition, refreeze model (a clear opposition to Lewin model of planned change), (Weick & Quinn, 1999, p. 379).

Some authors step outside the frames of planned or emergent approaches, like Dunphy and Stace who propose a contingency model for change (Dunphy & Stace, 1988, p. 331, 1993, p. 908), arguing that change is situational and that change strategies can be varied to attain an optimum fit for a given changing environment. This has started a discussion whether organisations are only subject to external changes or have ‘a choice’ and can choose their situation and successfully affect their environment in this direction, as argued by Burnes (Burnes, 1996, p. 18).

5.4.3. The scale of change When it comes to the scale, intensity or extent of change, the consensus on terms and definitions is non-existent. While the most common distinction is between radical and incremental change (Bordum, 2007, p. 64; D. Buchanan et al., 2005, p. 202; Soosay & Sloan, 2005, p. 2), an equivalent to radical and incremental innovation, many other authors have very different distinctions.

Dunphy and Stace distinguish between fine tuning, incremental adjustments, modular transformations and corporate transformation (1993, p. 908), Miller - between evolutionary, revolutionary and quantum changes (Danny Miller, 1982, p. 133), Levy, among others, between firstand second-order change, Buchanan and Huczynski - between ‘depths of change’ as surface, shallow, penetrating, deep and transformational change (D. A. Buchanan & Huczynski, 2010, p. 565), and others between convergence and upheaval (M. Tushman, Newman, & Romanelli, 1986, p. 583), incremental and strategic (Nadler & Tushman, 1989, p. 196), reorientation and re-creation (M. L. Tushman & Romanelli, 1985, p. 179) and even further distinctions of change include Branch/Root, Executive/policy-making, Vertical/Lateral, Linear/Nonlinear, Rational/Radical, Developmental/Revolutionary, Superficial/Real, Homeostasis/Radical, Alpha/Gamma, Transition/Transformation, Single-Loop/Double-Loop, Momentum/Revolutionary, Normal/Paradigm and Growth/Development (Levy, 1986, pp. 8–9).

The scale of change is a mixture of how large a part and level of the organisation is affected (Kuipers et al., 2014, p. 3), the intensity of the change (Nadler & Tushman, 1989, p. 196), and the 26

character/type of change involved (D. Buchanan et al., 2005, p. 202). With often fuzzy and blurry boundaries and definitions, and so many in existence, it is difficult to use the terms consistently across the literature.

One of the few who has taken time to create solid lines between the different types of change is Bartunek & Moch, who distinguish between three orders of change (Bartunek & Moch, 1987, p. 486).

The concept of orders is far from new, but originates from cognitive psychology and the terms of firstorder and second-order change that were brought into the change literature during the 70s, often ascribed to Watzlawick, Weakland & Fish (1974, p. 10), as in Palmer, Dunford, & Akin (2006, p. 86) and Weick & Quinn (1999, p. 363).

The orders of change are centred around the term schemata, that Bartunek & Moch define as “…templates that, when pressed against experience, give it form and meaning” (Bartunek & Moch, 1987, p. 484). It is similar or identical to terms like paradigm, frame or cognitive map. Schemata enables individuals to identify entities as they encounter them, specify relationships among entities and integrate them into a coherent whole (Bartunek & Moch, 1987, p. 485).

The third order was first brought into the change literature by Bartunek & Moch (1987) and where Watzlawick, Weakland & Fish discuss orders of change on a group level, Bartunek & Moch take the discussion on the application of orders on an organisational/systemic level. According to Bartunek & Moch, organisational members continuously negotiate specific organisational schemata that allows the members to have a common orientation towards events and generate shared meanings and understanding of the entire organisation (Bartunek & Moch, 1987, p. 485). Organisational schemata is furthermore defined by Manz as: “… a knowledge structure that people use to organize and make sense of social and organizational information or situations. “ (Manz, 1985, p. 529)

Bartunek & Moch argue that organisational schemata has the potential either to constrain or guide change (Bartunek & Moch, 1987, s. 485). While first-order change is change that happens within the existing schemata, second-order breaks current schemata and transforms it into new one (Bartunek & Moch, 1987, p. 486). Third-order change is a change in the ability to conduct second-order change. In an organisational context, Bartunek & Moch define third-order change as ”… the training of organizational members to be aware of their present schemata and thereby more able to change these schemata as they see fit” (Bartunek & Moch, 1987, p. 486).

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5.5. Critique of Change Management literature The body of knowledge on change management might be large, but among the literature reviews examined there exists an almost unanimous critique towards the change management literature, criticising the field that, despite its 50 years of research traditions, still seems to lack focus, scholarly quality and practical relevance (Garg & Singh, 2006, p. 46; Schimmel & Muntslag, 2009, p. 400; Todnem By, 2005, p. 370; Weick & Quinn, 1999, p. 363; Wetzel & Dievernich, 2014, p. 129). Others criticise the literature for lack of focus on the public sector (Kuipers et al., 2014, p. 2).

5.5.1. The myth of a 70% failure rate Within the change literature it is considered an undisputed ‘fact’, that most change efforts fail (Hughes, 2011, p. 452; Oakland & Tanner, 2007, p. 2; Todnem By, 2005, p. 370). The exact number is ranging from ‘above 50%’ (Kotter, 1995, p. 60) to as high as 90% (Oakland & Tanner, 2007, p. 1), while most refer to a 70% failure rate. A quick literature search reveals that much literature is based on the assumption that most change efforts fail (Hladik & Jandos, 2014; Jorritsma & Wilderom, 2012; MejiaMorelos, Grima, & Trepo, 2013; David Miller, 2002; Oakland & Tanner, 2007; Omazic, Vlahov, & Klindzic, 2011; Pruijt, 1998; Sicotte & Aubry, 2011; Spiser, n.d.; Todnem By, 2005; Wetzel & Dievernich, 2014). Some researchers even go as far as claiming that this number has been stable over the last decade or two (Jorritsma & Wilderom, 2012, p. 365; Werkman, 2009, p. 664). This is done without questioning why companies would even keep on initiating change projects, when it clearly doesn’t match up with the effort. It would appear that some change is better than no change at all.

(Bamford & Forrester, 2003, p. 560) Hughes (2011) finds and examines the source of these statements to assess their validity and reliability, but in his effort he fails to find any reference to academic research. The earliest source is Hammer and Champy (1993) who state that between 50% and 70% of reengineering efforts failed to achieve the dramatic results that they intended, which resulted in headlines of a 70% failure-rate for reengineering projects. Kotter is also often cited as the source in either his early work (1995) or latest work (Kotter, 2008). Lastly, one of the newest and most cited sources is Beer and Nohria (2000).

When taking a closer look on the cited sources, most references end with the experiences of the authors or an unsupported statement. Hammer and Champy clearly state that this is their ‘unscientific estimate’ (Hammer & Champy, 1993, p. 200), Kotter in his first article states that ‘Well over 50% of the companies I have watched fail…” (Kotter, 1995, p. 60) and in the later ‘From years of study, I estimate that today more than 70 percent...’ (Kotter, 2008, p. 12), while Hughes tracks Beer & Nohria’s references originate from Hammer & Champy (1993).

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One of the most grievous examples stems from the McKinsey & Company report The Inconvenient Truth about Change Management (Keller & Aiken, 2009). In citing a survey among 1546 executives (also from Mckinsey & Company), Keller & Aiken state that only 30% answered that their change initiatives where “completely/mostly” successful. In fact, the McKinsey & Company report stated that only 22% of the respondents said their change efforts to some degree failed at improving the organisations performance and only 23% that it failed in equipping the organization for sustained, long-term performance, the rest was either neutral or slightly positive (McKinsey and Company, 2006, p. 4).

The myth of high failure rates might have been pushed forward by the consultancy industry, who wish to give the impression that “this stuff’ is though. Don’t try it on your own; let us help you” (M. E. Smith, 2002, p. 30). Implying that change is hard to implement has made the change literature focus on why change efforts fail and how to make them more successful. One of the most dominant causes of failure is said to be the employees‘ ‘resistance to change’ and a lot of change literature have focused on this aspect.

Dent & Goldberg examine the rise of resistance to change in the 1950s-60s and find it has changed meaning over time. Lewin meant it as a systemic resistance in the system as a whole, but it has since transformed into a psychological concept. Dent & Goldberg find indications in the literature that the view might be changing again towards a systemic view of resistance to change (Dent & Goldberg, 1999, p. 39) 5.5.2. Lack of internal validity The best documented critique comes from Barends, Janssen, ten Have, & ten Have who have done a systematic review on studies on change intervention over the last 30 years. They find that 88% of the change intervention studies have a weak internal validity (Barends et al., 2013, p. 15). Over the last 30 years the number of uncontrolled studies conducted has increased dramatically, but the number of controlled studies remains about the same. This means that only 5% of the change intervention studies between 2005 and 2009 (N=214) are controlled and have high internal validity (Barends et al., 2013, p. 15).

5.5.3. Limited methodological repertoire and replication Some of the review literature criticise the lack of diversity among research methodologies (Barends et al., 2013, p. 21). As earlier stated, a large ratio (88%) of intervention studies features an uncontrolled design, 47% of the studies were built on a case study design and 31% on cross-sectional design. Only 2% us a case control design, 1% time series and only 3% use a cohort/panel study design (Barends et 29

al., 2013, p. 20). The research is dominated by one-shot studies and seldom addresses the same intervention more than once and the change management literature is characterised by “we know increasingly less about more” (Barends et al., 2013, p. 21) The study of Barends et al. (2003) also concludes that there is little to no replication among the intervention studies (Barends et al., 2013, p. 16). The existence of few but good examples of randomized and controlled designs, triumphs arguments that this should be impossible due to the dynamic nature of organisations (Barends et al., 2013, p. 21) On this basis Barends et al. (2003) concludes that the change management literature is limited in “… capacity to answer fundamental questions on what works (and what does not)”, and advises that ”…practitioners should be sceptical about relying uncritically on research findings … as a basis for important decisions” (Barends et al., 2013).

5.5.4. Pro-change Bias Following the same line of critique that has happened to the innovation literature for a pro-innovation bias, the change literature is criticised for a tendency to see change as always desirable or inevitable (Sturdy & Grey, 2003, p. 654; Wetzel & Van Gorp, 2014, p. 130). Change is seen as necessary and therefore preferable to the alternative of ‘no change’. An alternative that is rarely addressed (Sturdy & Grey, 2003, p. 655). And derived consequence of this bias is that employee resistance to change is seen as irrational (Boudon, 1986, p. 46; Sturdy & Grey, 2003, p. 655). In more recent view the literature has moved towards what Sturdy and Grey call a totalitarianism of change (Sturdy & Grey, 2003, p. 655). This view claims that everything is change, change is constant and stability is unnoticed change (Kanter, 2003, p. 13). Stability, if existent, is therefore seen as temporary as a mean in a change process (Kanter, 2003, p. 375).

The early approaches and theories to organisational change management suggested that organisations could not be effective or improve performance if they were constantly changing (Rieley and Clarkson, 2001). It was argued that people need routines to be effective and able to improve performance (Luecke, 2003). However, it is now argued that it is of vital importance to organisations that people are able to undergo continuous change (Burnes, 2004; Rieley and Clarkson, 2001). While Luecke (2003) suggests that a state of continuous change can become a routine in its own right, Leifer (1989) perceives change as a normal and natural response to internal and environmental conditions.

5.6. Topics in change management There is a general consensus in the change literature on two aspects of change. First of all, that the pace of change is increasing and never has been greater in society and the business environment than 30

it is now (Price & Chahal, 2006, p. 239; Todnem By, 2005, p. 370). Secondly, there is a consensus that change drivers for change come from internal and external areas to the organisation, and that it comes in many shapes and sizes that affects all organisations in all industries (Todnem By, 2005, p.

370).

5.6.1. Resistance to change One of the most researched topics within the change management literature is the employees’ ‘resistance to change’. A literature search on resistance to change reveals 2.195 academic articles on the subject (1,526 with ‘resistance to change’ in the abstract, 851 in the subject terms and 292 as part of the article title)2. The term was made popular by Kotter in his 1995 article ‘Leading Change’; a 8step plan to deal with resistance to change, but originates all the way back to Lewin’s original text from 1947 (K. Lewin, 1947, p. 33). Lewin’s idea of resistance to change was built on social habits and customs, that could be difficult to break (K. Lewin, 1947, p. 32). Since then the concepts of resistance to change has developed into an underlying assumption that employees’ natural reaction to change is sceptic and fearful, and that this is the biggest obstacle to change (Garg & Singh, 2006, p. 48; Price & Chahal, 2006, p. 245).

According to Kuipers et al. ”... there does not appear to be a consensus on what causes resistance and how it can be overcome (Kuipers et al., 2014, p. 10). However, a common view is that resistance to change is dispositional and can be assessed with inquiries such as Oreg’s Resistance to Change Scale (Oreg, 2003, p. 691), and that generally people resist change because they expect it to have a negative impact for their work situation (Kuipers et al., 2014, p. 10). Others, such as Vann (2004) argue that resistance can come from clashes in grammar between the change facilitators and employees that may “… occur as an unintended consequence of the communication environments of these projects” (Vann, 2004, p. 48). There exist plenty of models of determinant and countermeasures towards resistances as the ones below, but few of them are grounded in any empirical research.

2

http://search.ebscohost.com.proxy1bib.sdu.dk:2048/login.aspx?direct=true&db=aph&db=bth&db=nlebk&db=poh&db=snh&bquery=(AB+%26quot% 3bresistance+to+change%26quot%3b)+OR+(SU+%26quot%3bresistance+to+change%26quot%3b)+OR+(TI+%26 quot%3bresistance+to+change%26quot%3b)&type=1&site=ehost-live

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Methods for managing change

Determinants of resistance to change

    

     

Education & communication Participation & involvement Facilitation & support Negotiation & agreement Explicit and implicit coercion

(Kotter & Schlesinger, 1979, p. 5)

Basic predisposition to change Personal sense of security Prevailing cultural beliefs Extent of trust and loyalty Objective historic events Specific apprehensions and expectations about the particular change (Bryant, 1989, pp. 193–194)

Countermeasures against resistance to change  Communicate  Make resistors part of the project  Be honest  Manage the change well  Managers create an example  Use of Peer Pressure  Create a safety net  Discipline (Price & Chahal, 2006, p. 249)

As most change studies have focused on the organisational dimension of the change process, such as culture, it has been at the expense of deeper studies into the variations of individual perceptions, and responses to change (Bamford & Forrester, 2003, p. 546).

As it is assumed that most change efforts fail and the biggest obstacle for change is the employee’s resistance to change, it is easy to conclude that employees, in general, resist change. In her review of studies of resistance to change, Piderit criticises the research for “…failure to take the good intentions of resistors seriously…” (Piderit, 2000, p. 792), Barends el al. in their comprehensive review of change interventions find that 25% of studied change interventions feature downsizing/reducing head count, 15% focused on performance optimization and 8% on job restructuring (Barends et al., 2013, p. 17), all factors that would understandably be viewed with scepticism by some employees, and Soosay and Sloan in their recent study find all small extents of resistance of change, all of which is effectively overcome in the change process (Soosay & Sloan, 2005, p. 17).

On the other hand, if most change efforts succeed, at least to some extent, people might not be as reluctant to change as it is assumed in the literature. Taken into account that resistance to change has been a management issue since the 1920 (Piderit, 2000, p. 784), combined with the undisputed statement that the pace of change is increasing both in organisations and in society as a whole, it is not unlikely to assume that people might not be as reluctant to change as they used to be, and maybe resistance to change is not the most important object for the change management literature now and in near future.

5.6.2. Drivers for change A large portion of change literature is concerned with the drivers for change. There’s typically a distinction between external (to the organisation) and internal drivers, however some researchers argue that internal drivers can be “… considered to be a manifestation of external drivers for change” (Oakland & Tanner, 2007, p. 5). As with the nature of change, there is little agreement on the terms 32

and typology for categorising drivers for change. Some examples of drivers of change are listed in the below table.

Drivers for change       

The User Competition Diversity Legislation Human Resource Management Technology Finance (Baker, 2007, p. 5)

 Customer requirements  Market competition  Demands from other stakeholders, such as the Government  Regulatory demands  Shareholders  Improving operational efficiency  Need to improve the quality of products and services  Process improvement (Oakland & Tanner, 2007, p. 5)

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 Customer satisfaction/ service value  Competition  Continuous Innovation  External Push  Operational performance  Keeping up with Technology  Financial Performance (Soosay & Sloan, 2005, p. 11)

The Change Management literature is fairly limited in providing further details regarding how exactly the organisational environment affects the organisational change process, and is focused on the choices made by the actors involved in the change, and most empirical studies focus on the organisational and intra-organisational level (Kuipers et al., 2014, p. 7). In addition, noted by Kuipers et al., many external drivers have consequences for a number of organisations at the same time, an aspect that is very absent in the literature (Kuipers et al., 2014, p. 8) .

What might be more interesting than the specific drivers that fuel change (of which we still know surprisingly little), is how the need for change is perceived by the organisation. As noted by Kanter, Stein and Jick (1992), no matter what shapes or what drives the necessity to change, change is “…ultimately driven by someone’s belief that the organization would, should, or must perform better (Kanter et al., 1992, p. 490). So all (intentional) change is dependent on an (influential) individual’s belief that the change is necessary. As further noted by Gagliardi, the need for large-scale change is rarely perceived by members of the organisation, as they are too deeply involved in the existing culture and is often first recognized by outsiders (Gagliardi, 1986, p. 129). An organisation with a proactive approach will also be able to recognise the need for change early and thus increase the probability for successfully managing the change (Price & Chahal, 2006, p. 241).

5.6.3. Change outcomes A criteria for assessing whether change is successful, requires to have a set of measure to which the outcome of the change process can be compared. Whereas most planned change efforts have welldefined outcomes in advance, the more evolving nature of the emergent approaches means that outcomes change during the process and emergent approaches are therefore considerably more difficult to assess both in advance and in its outcome (Kuipers et al., 2014, p. 12).

Most planned change efforts list increased organisational efficiency and greater profitability as the main goal of the change (Kuipers et al., 2014, p. 13). Customer satisfaction and effectiveness are also common criteria, but more unusual criteria such as increased safety, reliability, behaviour of actors, experiences of clients and stakeholders, attitudes of employees, morale, transparency, equity, legitimacy or other values are also existent (Kuipers et al., 2014, p. 13).

Part of the change literature recognises that change initiatives might have other, unintended outcomes that planned. One reason for unintended consequences is the before mentioned resistance from the employees regarding the change (Armenakis, 1999, p. 304). Some researchers see the organisation as a large system of subsystems and a change in element of the organisation is likely to affect other elements

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which in turn will affect yet more elements (Armenakis, 1999, p. 312; Lorenzi & Riley, 2000, p. 122; White, 2000, p. 169) 5.6.4. Sustainability / Stickiness A field that has drawn limited attention in the change literature is change sustainability or stickiness, a topic that Bucharan et al (2005) review existing literature on. The topic concerns whether, when and how the change persists for a period of time after implementation and becomes the new norm (D. Buchanan et al., 2005). One reason for a lack of attention to this area is a consequence of the earlier mentioned lack of focus on stability and focus on change as the preference. Especially in the episodic and continuous change models the focus is on the next change and ongoing adaption (D. Buchanan et al., 2005, p. 190).

Another reason is that while change implementations can be studied over a relative short period, sustainability requires longitudinal studies and more resources than available to the average researcher (D. Buchanan et al., 2005, p. 190). And, as also confirmed by the review of Barends et al. (2013), only 16% of the existing research on change features a longitudinal design (Barends et al., 2013, p. 14).

There exists however an increasing amount of change literature with focus on sustainability and even though sustainability has not been a hot topic in change management, it is far from new. Lewin, who is by many considered the father of change management, saw sustainability as paramount for change implementation: “.. It does not suffice to define the objective of a planned change in group performance as the reaching of a different level. Permanency of the new level, or permanency for a desired period, should be included in the objective” (K. Lewin, 1947, pp. 34–35)

The empirical research on sustainability is still very limited (D. Buchanan et al., 2005, p. 192). Buchanan et al indifies from the existing literature 11 factors affecting change sustainability and decay (Substantial, Individual, Managerial, Financial, Leadership, Organizational, Cultural, Political, Processual, Contextual, Temporal) , but not much is known of their relative importance or individual function (D. Buchanan et al., 2005, p. 201).

There seems to be a consensus among contemporary authors that the benefits from discontinuous change do not last (Bond, 1999; Grundy, 1993; Holloway, 2002; Love et al., 1998; Taylor and Hirst, 2001).

According to Luecke (2003) this approach allows defensive behaviour, complacency, inward focus, and routines, which again creates situations where major reform is frequently required.

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6. Business Intelligence & Big Data This chapter will investigate the literature of Business Intelligence in order to present an overview of the literature and main discussions within the field. The review aims at answering the following questions:  What is Business Intelligence and what do we know about Business Intelligence?

The short answer is that we know a lot about the technical tools used in the Business Intelligence practice, and a lot about implementing Business Intelligence projects in organisations. In other words, we know a lot about the work of Business Intelligence consultancy companies. We do however not know much about the process of Business Intelligence and how it is used in organisations. It has therefore been necessary to investigate the following aspects of Business Intelligence outside the existing Business Intelligence literature:  What is the relationship between data, information and knowledge?  How do organisations transform data into knowledge?

Furthermore, there is a new phenomenon within Business Intelligence, called Big Data, which promises great upheavals in Business Intelligence, and, as some go as far as saying, in society in general. It is so new, that it is hardly a research area in itself and very little academic literature exists on the subject. From the literature available the following questions will try to be answered:  What characterises Big Data, and what separates it from ‘ordinary’ Business Intelligence?  What are the likely implications of Big Data for realms of science and business?

This chapter is therefore separated into three sections: Business Intelligence, Data, information and knowledge, and Big Data.

6.1. Literature selection on BI A broad literature search on Web of Science using the keywords “Business Intelligence” and “review”/”overview” in all text fields returned only 169 results3. Because of the relatively limited result, it was possible to go through the titles manually and abstract without further filters. After a review of titles

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WoS search criteria: TOPIC: ("business intelligence") AND TOPIC: (review) OR TOPIC: ("business intelligence") AND TOPIC: (overview) Timespan: All years. Indexes: SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH.

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and abstracts, 21 articles where identified that were either an overview of Business Intelligence or included a literature review on Business Intelligence. A list of these articles can be seen in appendix XX. Of these 21 articles, only 6 were journal articles, the remaining 15 were conference proceeding papers.

Of the 6 journals, one was in quartile 1, one in Q2 and the rest in Q3 of journals of their respective categories, according to data from Web of Science Journal Citation Reports. Conference proceeding papers lack the scientific value assured by the peer review process of established journals. This clearly shows in the returned literature.

Because of the lack of formulated theory on the Business Intelligence process, it has been necessary to include literature from the domain of information science in order to achieve a proper distinction between the elements in Business Intelligence, data, information and knowledge. This part is mostly inspired by the works of Zin Chaims (2007) and Boisot & Canals (2004).

6.2. Business Intelligence 6.2.1. The Business Intelligence Literature Jourdan, Rainer, & Marshall (2008) make a literature analysis of research articles on Business Intelligence available in leading journals between 1997 and 2006. Their result shows that 56% of the research is focused on theory creation and literature reviews. 96% of the research employed methods are with measurement of low precision and 68% with a low degree of realism of context. Instead, the literature is focused on a high degree of generalizability with 76% of the research focused on generalizability (Jourdan, Rainer, & Marshall, 2008, p. 125)

(Jourdan et al., 2008, p. 125)

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Jourdan, Rainer, & Marshall also look into the contents of the research and divide them by BI Category and topic. As seen in the above table, most of the literature is focused on technical use opportunities and challenges of Business Intelligence and not much on the organisational use and consequences of Business Intelligence.

Other reviews in the literatures search conclude that the existing literature on Business Intelligence is mainly characterised by “…a lack of agreement among the authors […] on what BI is and how it is defined” (Shollo, 2013, p. 21) and a general “...low level of contributions to international conferences and journals” (Yeoh & Koronios, 2010, p. 23). Therefore the body of knowledge on Business Intelligence “…is fragmented and future research is hindered by lack of clear definitions” (van Roosmalen, 2009, p. 263).

On the content side, Business Intelligence is characterised by “…normative ideas of how the BI output should be used in decision-making and how it can enable people to make better decisions” (Shollo, 2013, p. vii). There is “… little empirical research into BI [….] regarding how to identify the concepts of information life-cycle (Thamir & Theodoulidis, 2013, p. 199) and “… there is a gap in research that explores the role of acting (decision-making) within BI”(Shollo & Kautz, 2010, p. 9) 6.2.2. Defining Business Intelligence As previously noted, Business Intelligence is not very well defined within the literature (Singh & Samalia, 2014, p. 52; van Roosmalen, 2009, p. 264).

Shollo & Kautz (2010) make a large review of the BI literature between 1990 and 2009 with the purpose of synthesizing a unified definition of BI. According to their review, the first definitions saw BI solely as a process in which data is transformed into information and later into knowledge (Shollo & Kautz, 2010).

Later definitions also see BI both as a process and as a product in the form of actionable information or knowledge used in corporate decision-making. Most recent definitions, according to Shollo & Kautz, also incorporate the technologies used as they create and develop the very foundation for BI (Shollo & Kautz, 2010).

Shollo & Kautz concludes by defining business intelligence as: “…a process where data are gathered, stored and transformed into information through analysis, and where information is transformed into knowledge which is used when acting (making decisions). Ultimately, the product of this process is better decisions. Technologies used in the process support the transformation from one phase to another.”

(Shollo & Kautz, 2010)

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6.2.3. Business Intelligence technique and process Shollo & Kautz (2010) end their literature review by describing two different views of Business Intelligence within the literature, the process view and the technical view.

Two views of Business Intelligence

(Shollo & Kautz, 2010, p. 44)

In many cases information that has been produced is not used, is unsuited for decision purposes or is ambiguous and interpreted differently across different contexts (Davenport 2010).

6.2.4. Business Intelligence Maturity Models A common topic within Business Intelligence, both in the academic and popular literature, is the assessment of an organisation’s Business Intelligence ‘maturity’. Two of the papers in the literature search focus entirely on this aspect (Chuah & Wong, 2011; Thamir & Theodoulidis, 2013).

The concepts stem from the popular literature and most notably form various vendors and consultancy companies. Thamir & Theodoulidis find and analyse a total of 14 different of these maturity models (Thamir & Theodoulidis, 2013, p. 211). The models vary on which parameters they evaluate, but all models assess both organisational, human and technological aspects. The results of Thamir & Theodoulidis is a synthesis of the models with the Information Management Practices (IMP) made by Kettinger and Marchand (2011). It divides the assesment of an organisation’s Business Intelligence maturity into the five faces of the information life cycle: Sensing, Collecting, Organizing, Processing, Maintaining (Thamir & Theodoulidis, 2013, p. 200) Generally, all maturity models aim to assess two dimensions of the organisation: How much data an organisation is able to process and how much information/knowledge is it able to extract from these data and make use of.

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6.2.5. BI 1.0 & BI 2.0

6.3. Data, Information & Knowledge As previously examined, the Business Intelligence literature gives little answer to how Business Intelligence is used in organisations and focuses more onto its technical functionalities. It has therefore been necessary to include views from other areas of information science to the processes of data, information and knowledge.

Independently of Business Intelligence there exists an ongoing discussion within science and especially within Information Science, regarding the nature and interrelations of data, information and knowledge.

Even though there still is no consensus on the nature of either data, information or knowledge, as well as the relation and hierarchy between them, it is possible to discuss and define the different views on this dilemma. This chapter builds mainly on the comprehensive works of Zin Chaims (2007) and Boisot & Canals (2004).

6.3.1. Epistemology of data, information & knowledge In the analysis of different definitions of data, information and knowledge and their relations from 45 leading scholars, Zin Chaims (2007) identifies a minimum of 5 different models. He finds that the differences are based mainly on the views on respectively data, information and knowledge belonging to the universal or subjective domain (UD/SD). Despite the differences, there seems to be a consensus that data belongs (at least partly) to the universal domain and knowledge (at least partly) to the subjective domain, as seen in Chaims schema below:

(Zins, 2007, p. 489) According to Chaims, analysis model 1 seems to be the most dominant model where data and information reside in the external world (e.g., in books and databases), and knowledge only in the internal (i.e., as a thought). Unfortunately, Chaims’ analysis is short and not deep enough to arrive at any further conclusions. The subjective and objectives qualities of data will each be discussed in further details in their respective chapters (the results are similar to that of model 5).

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6.3.2. Hierarchy of data, information & knowledge Another important aspect is the interrelations, or hierarchy, between data, information and knowledge.

The differences here lay in the way that data, information and knowledge is defined. The traditional view is a data-information-knowledge (D-I-K) hierarchy that starts with data (as fact or representations of facts) and defines information as data combined into meaningful structures, and knowledge as information that is given meaning and put into action (Spiegler, 2003, p. 103). The more unconventional view (K-I-D) sees information as extracted from and the bearer of knowledge, and data as information arranged in a meaningful way that allows for processing and aggregation (Spiegler, 2003, p. 107). Where the traditional view seems to be concerned mostly with knowledge creation, the latter seems to focus on knowledge sharing and aggregation.

This earlier definition of Business Intelligence clearly indicates a hierarchy in which information is created from data and knowledge is created from information. However, as many researchers in Spiegler’s interview claim, interpretation of data and information most often requires and is affected by predisposed knowledge. The knowledge creation process is therefore continously qualified by the expanding knowledge base of the agent.

6.3.3. Defining Data Boisot & Canals (2004) make a thorough analysis of nature and relations of data, information and knowledge. They define data on the basis of stimuli. Stimuli is what can be perceived through a ‘sensory apparatus’ (Boisot & Canals, 2004, p. 47). Data is in its essence a representation of a ‘discernible difference’ in the stimuli received. The sensory apparatus however, is limited by the fact that it requires energy to transform the stimuli into data, and not everything that might be perceived is necessarily recorded as data (Boisot & Canals, 2004, p. 47).

6.3.4. Defining information It has already been stated that data can contain information. As Boisot and Canals (2004) note, this can be well exemplified by the concept of encryption. Data can be encrypted, with the purpose of hiding the information that it contains. Boisot & Canals define information as the ‘significant regularities’ that can reside in the data (Boisot & Canals, 2004, p. 47). As the encryption needs to be able to retrieve the information again, the information is never deleted, only hidden. This builds on the principle that it takes energy to extract information from data, and encryption techniques greatly increase the cost of extracting the data without having the previous knowledge on how to do so (by having the decryption key (Boisot & Canals, 2004, p. 47).

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6.3.5. Defining knowledge Knowledge might be the hardest phenomenon to define in science, even though it is what science is all about. As the analysis of Zins’ show, there is a general agreement that knowledge is, at least partly, subjective. This for the reason that the most common view sees knowledge as residing within an individual as a certain kind of belief, a view originating from Plato’s dialogues Theaetetus from around 369 BC. (Bordum, 2007, p. 64). Boisot & Canals are a bit vague when it comes to defining knowledge.

They try to get away with defining knowledge as “… a set of expectations held by agents and modified by the arrival of information” (Boisot & Canals, 2004, p. 47). This however, is nothing but a circular reference to information, and shrinks the definition of knowledge to a set of expectations held by an agent.

Some scholars in Zins’ study, see knowledge as a prerequisite in order to obtain data or information, for example Fidel who describe knowledge as “… a personal/cognitive framework that makes it possible for humans to use information” (Fidel in Zins, 2007, p. 483) or Moukdad who sees knowledge as “… needed to decipher data and turn them into information” (Moukdad in Zins, 2007, p. 485).

Following the Business Intelligence process view on data, information and knowledge, knowledge has to be defined on the basis of information. A proposed definition of knowledge for this view is “Knowledge is internalized or understood information that can be used to make decisions” (Tenopir in Zins, 2007, p.

486) “…that has developed inside of a cognitive system or is part of the cognitive heritage of an individual” (Biagetti in Zins, 2007, p. 490). In this view, knowledge is residing with the mind of an individual and utilized in decision-making. The validity of knowledge is based on the validity of the information as well as whether the information is understood.

A last note on knowledge is, that even though most aspects of knowledge are still being debated, there seems to be a general acceptance that knowledge is not easily, but to some degree transferred through information. The degree is still debated, and the most limited view is that knowledge “… cannot be communicated by speech or any form of writing, but can only be hinted at” (Gladney Zins, 2007, p.

483).

6.3.6. From data to knowledge From their definitions of data, information and knowledge, Boisot & Canals develop a model for knowledge acquisition and action. The model shows how the existing knowledge is used both in the data collection and information creation processes as respectively perceptual and conceptual filters, and how the world is in turn affected by the actions of the agent (Boisot & Canals, 2004, p. 48)

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(Boisot & Canals, 2004, p. 48)

As mentioned earlier, by limits of resources and sensory ‘technology’, not all data can be perceived and not all data can stored, and the agent’s knowledge plays an active role in how to filter the data. Boisot & Canals call this for perceptual filters. Similarly, conceptual filters are used in the information creation process in which ‘significant regularities’ constitute information (Boisot & Canals, 2004, p. 47).

6.4. Difference between Big Data and Business Intelligence Seen from a user’s perspective, the software technologies used is what separates Big Data from ‘ordinary’ Business Intelligence. Hadoop - an open source project - emerged in 2005 and have since become synonymous with Big Data (Tekiner & Keane, 2013, p. 1495). It consists of a distributed file system (HDFS) and a MapReduce program that enables the software to almost automatically distribute both data storage and calculations across multiple nodes (servers), and thereby greatly reduce the time and complexity of handling vast amounts of data (Sagiroglu & Sinanc, 2013, p. 44). An example is Visa who, after the adoption of Hadoop software, reduced the time to interrogate 73 billion transactions from a month to 13 minutes. (Walker, 2014, p. 181).

That Big Data has been such a widely discussed topic, is of course not just because of new technologies and methods, but because of the huge implications that it has and is deemed to have in the future.

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7. Big Data 7.1. Literature selection on Big Data The method on Big Data includes an explorative literature review. As Big Data is an extremely new research area with less than 3 years history, traditional literature search methods, such as citation indexes to find to most cited articles, are of almost no use. On the other hand, it is possible to include nearly all the literature on the subject. However, after an initial literature search it became clear that most articles were focused on the technical issues of Big Data or examples of practical application of Big Data Methods, especially within the natural sciences Medicine, Healthcare and Biology.

The initial search for “Big Data” on Web of Science using keywords “Big Data” and “review”/”overview” resulted in 132 articles. A further filtering by reading titles, abstract and articles resulted in 15 articles and 4 conference proceedings.

Because of this very limited search result and that all of the articles where elaborating on Big Data only on a very superficial level, the search was further extended to include academic book reviews on popular big data titles and reports published by private research organisation. This expansion resulted in 19 book reviews 8 reports. The result of the literature search is listed in appendix III. This of course is far from an optimal literature selection. But as concluded in chapter Error! Reference ource not found., Big Data is an extremely new research area, and so it is unlikely that much review literature exists. Furthermore, it is already an incredible large research field, which makes literature selection even more difficult.

The scientific quality of the theoretical discussions on Big Data in the literature lacks the clear definitions and research traditions of an established field. As a consequence, this review focuses mostly on what is agreed in literature and I have tried to organise the many fragments in the literature into a clear definition of Big Data and its implication. This review though is not to be taken for much more than an overview in interesting research topics that are hopefully further investigated in the coming years.

7.2. What is Big Data?

Big Data is driven by recent advances in hardware and software technology. While data storage cost has historically been the main barrier for larger databases (Jacobs, 2009, p. 36), it is now decreasing at an exponential rate (Blok, 2013). Meanwhile, the advance in CPU clock speed halted during the mid-2000’s (Muehlhauser, 2014), that has shifted focus from clock speed towards parallelism; improving performance by taking advantage of several CPU at a time. (Jacobs, 2009, p. 43).

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There seems to be no rigorous definition of what Big Data is. Many have come up with definitions of big data, but they are all very similar and none of them do a very good job at setting strict boundaries between Big Data and what came before it. Thomas Davenport, one of the Big Data ‘gurus’, says about his own definition of Big Data: “I am not a fan of the term. But we seem to be stuck with it for the moment.”

(Smith, 2014, p. 92). Davenport describes Big Data as: “I describe big data as data that is too big to be processed on one server, too fast-moving to be sequestered in a data warehouse or too unstructured to fit into a conventional database.”

(N. Smith, 2014, p. 92)

It seems that for now Big Data is loosely defined by the volume of data, the rate by which data is being gathered, and that some of the data is less structured. These three elements are often referred to as the ‘3 Vs’ of Big Data: Volume, Velocity and Variety. These 3 dimensions of data warehousing first originated in 2001 (Laney, 2001), but were made popular for describing Big Data in a Gartner report from 2012 (Beyer & Laney, 2012). The three characteristics are not new at all to data warehousing and so Big Data is only defined in the extent to which these dimensions have evolved. However, as the following review and analysis will show, it is possible to distinguish some features of Big Data that makes it very distinct from previous thought on data.

7.3. The Many V’s of Big Data There seems however to have been an inflation of V’s in Big Data as practitioners are competing to find new words (beginning with V) to describe Big Data. It is almost impossible to pick out the source of these, as several authors, spread across blogs on the internet, claim credit and none give. There are mentions of at least 8 other V’s: Veracity (Tee, 2013), Value (Rijmenam, 2013), Visibility (Livingstone, 2013), Variability (Reilly, 2012), Visualization (Rijmenam, 2013), Validity (Hurwitz, Nugent, Halper, & Kaufman, 2013), Volatility (Hurwitz et al., 2013) and Viability (Biehn, 2013).

While the tree original V’s try to define Big Data as it is stored in a data warehouse, the additional V’s are mostly concerned with the implication and usefulness, or other aspects of Big Data. They are often vague and loosely defined, and most likely a product of trying too hard to be the inventor of a term.

Nonetheless, two of these extra V’s are repeated more than the others (Veracity and Value) and have been found useful in the structure of this review.

Lastly, I will discuss the impact of Big Data as it is proposed by some authors to be a new scientific paradigm.

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7.4. Volume Big Data implies that we collect and store much more data than before. Storage cost has, and is still, decreasing at exponential rates and we have developed all kind of sensors that collect data all around us.

This carries along new and surprising opportunities and challenges of which we might only have seen the top of so far.

“Data can frequently be collected passively, without much effort or even awareness on the part of those being recorded. And because the cost of storage has fallen so much, it is easier to justify keeping data than discarding it,” (Hayashi, 2014, p. 35)

7.4.1. A cornucopia of data Following the increasing amount of data, there arise problems that are associated with that “our perceptions and institutions were constructed for a world of information scarcity, not surfeit” (Walker, 2014, p. 182). We have and produce more data than it is possible for us to store and process. As the following graph shows, we have traditionally been able to store all the data we produced, but reached that limit already in 2007:

(The Economist, 2010, p. 3)

The surfeit of data creates challenges, not just for storing the data, but also for securing, curating, sharing, analysing and extracting information from it. How should they be curated and preserved for future generations (Nielsen, 2009, p. 722), and how to put a value on it and distinguish between valuable and non-valuable (Hayashi, 2014, p. 38).

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It does however look like the increase in data is slowing down. In 2006, the global creation of data was estimated to 0.173 Zettabytes (1021 bytes) which increased by ten times in five years to an estimated 1.773 Zettabytes in 2011 (IDC White Paper, 2008). However, EMC predicts that it will take 7 years to come from the present 4.4 Zettabytes (2013) to 44 Zettabytes in 2020 (EMC, 2014) 7.4.2. Scarcity of professionals As already mentioned, there exists a global and increasing shortage of data analysts and data-savvy employees and managers. A way of viewing this shortage is through the increased amount of data. EMC looks at the amount of data IT professionals have to handle. EMC estimates that now each of the 28 million IT professionals worldwide has to handle, on average, 230 GB data. In 6 years this increases to 1231 GB for each of the estimated 36 million IT professionals. This means that not just IT professionals, but also data analysts, managers and employees will have to be able to handle an increased amount of data.

7.4.3. Collecting all the data It not new in itself that we produce and collect much more data than ever before. It has been a general assumption that many technologies increase at exponential rates, following for example Moore’s law predicting that the number of transistors in an integrated circuit will double every second year (Moore, 1975) The new and probably most important and distinctive features of Big Data are that in some areas and for some applications it is actually possible to collect and store not just more data, but all data available (Demchenko, Grosso, de Laat, & Membrey, 2013, p. 49). This has tremendous consequences for the analyses of Big Data, as it makes it possible to skip some of the scientific methodologies and costly processes associated with collecting and sampling a representative subset of the given population. The scientific impact of Big Data is much more complex and will therefore be discussed further in section XX.

7.5. Variety As mentioned in the definition of Big Data, some elements of big data don’t fit into the conventional way of storing and managing data. Big Data includes tools on how to deal with unconventional data format. In practice, this is done through the implantation of Blops (Binary Large Objects), which makes it able to store any digital object as an entity in a database. Furthermore, Big Data includes tools for working with these unstructured data.

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7.5.1. Unstructured and structured data In Big Data, boundaries are increasing for what data is and what can be ‘datafied’ or quantified. Many things, which can be put into a database, are not numeric. Documents, pictures, video or sound are examples of so called unstructured or semi-structured data (Swetkis, 2014, p. 264). It is not because there suddenly exist more unstructured data, but because we are now able to extract more information from it.

Because of advances in software technologies like face recognition, voice recognition, text analysis, image analysis etc., we are now able to find structures (information) within previously unstructured data.

For example, a group of Facebook scientists developed a face recognition algorithm that can recognise faces with a 97.35% accuracy, compared to the average human detection rate of 97,50% (Taigman, Yang, Ranzato, & Wolf, 2014, p. 6). Meaning that in the near future machines might be better at recognising face than us. There exist however still lots of unstructured data that is yet too complex to structure, quantify and extract information from.

7.5.2. Repetitive and non-repetitive data Another important distinction between data is that of repetitive and non-repetitive data. Non-repetitive data is data where each occurrence is unique and representing a unique entity. Any similarities between non-repetitive data is therefore just a matter of change (Inmon, 2014, p. 2). Non-repetitive data is often in the shape of unstructured or semi-structured data such as emails or other documents (Inmon, 2014, p.

2). However, as it is possible to find and extract structures from unstructured data, these extracted data can be repetitive.

The pattern of value differs between repetitive and non-repetitive data. In non-repetitive data, all the data is valuable to some degree, whereas in the repetitive data the value lies in the few data that deviates from the norm. For example, Harvard epidemiologist Caroline Buckee calculated the spread of malaria from cell phone tower data in Kenya, as she found out that people were making 16 times more trips away from the area of Kericho than the regional average. It turned out to be a malaria hot-spot (Talbot, 2013)

7.6. Velocity Velocity is both related to how fast the amount of data being gathered as well as how fast it is processed.

Even though the technologies in Big Data are able to process much more data than ever before, they are still not able to process all the sensory data available. Part of Big Data is therefore also meant to filter out unnecessary or low value data both in the data collection and the data processing.

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7.6.1. Expanding limits in data representation As earlier noted by Boisot and Canals, our representation of the world through data is limited by at least two factors. First of all, by the sensitivity of our sensory equipment which limits size of the differences we are able to discern, and secondly, by the energy it takes to transform and store the stimuli received as data. It is partly the expansion of limits that have created big data. Lots of new and improved sensors have been developed that enable much more and detailed stimuli to be perceived, as well as the computational power and storage capacity to transform and store almost all of it as data.

7.6.2. Perceptual filters In order to be able to process and store the vast amounts of data, it is often necessary to filter out data deemed as unnecessary already in the data generation process. This issue has long been known for digital photographers, where modern DSLR camera by default stores images directly in a compressed JPEG format. The compressed image takes significantly less storage and can be directly used, whereas the RAW format contains more data, which can be useful in professional post-processing (The Library of Congress, 2006) A Big Data example of this process is the LHC ATLAS detector, with sensors that create 1 Petabyte (1 million GB) of unfiltered data every second, but in processing this is reduced to 100 Mb making it able to detect 40 million collision events per second (Demchenko et al., 2013, p. 50). This is equal to the perceptual filters of Business Intelligence as described in chapter XX. 7.6.3. Conceptual filters After the filtered data has been collected and stored, there’s still too much data to be processed all at once. Therefore several mechanisms are evoked in Big Data to handle the huge amount of data.

Technically, a large degree of parallelism is used, distributing the data across a multitude of servers to increase the ‘bandwidth’ for how much data can be processed and the processing time is reduced (Tekiner & Keane, 2013, p. 1453). But this is, of course, not a filter as such.

The conceptual filters are not much different from Business Intelligence. In general, key data is identified and aggregated by concepts in order to gain a meaningful understanding of it. In Big Data this is usually done through a procedure called MapReduce which consists of two steps: First a function is used for mapping key/value pairs, which is then subsequently aggregated by a Reduce function (Tekiner & Keane, 2013, p. 1496)

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A distinct aspect of Big Data though, is the development of new and far more complex algorithms.

Artificial neural networks for example, imitate the structure and function of biological neural networks, and are especially well-suited for finding non-linear patterns (Manyika et al., 2011, p. 29), for example object-recognition in images. Another is machine learning, which aims to make algorithms capable of automatically learn to recognize complex patterns and make intelligent decisions based on empirical data.(Manyika et al., 2011, p. 29). Predictive analytics are algorithms focused learning from data, to predict people’s future behaviour and drive better decisions (Lott, 2014, p. 1302) Translated to the model of Boisot & Canals, these developments reduce the need for human knowledge in the conceptual filters.

7.6.4. Preservation filters Another important aspect of Big Data is the question of what data to keep and what to delete. As later in the thesis is revealed, the value of data decreases over time. So the challenge is to put a value on data for its present and future use, to be able to decide which data shall be stored and which data shall be discarded (Chen, Mao, & Liu, 2014, p. 175; Hayashi, 2014, p. 38).

This issue is related to data conservation. As argued by Nelson (2009), data is in fact very expensive to conserve as it needs to be refreshed, migrated to new platforms, updated to new formats, verified for provenance, supported for continued access, tracking changes (M. L. Nelson, 2009, p. 7). These issues are even more pressing when it comes to public or scientific data, in which data has not just a financial but also a social value.

7.7. Veracity Veracity refers to the quality and accuracy of the data. The basic issue here is that improper data representation will reduce the value of the original data and even obstruct effective data analysis (Chen et al., 2014, p. 175), regardless whether this is result of errors an inaccuracies in the data collections process or use of non-standardised formats and procedures in data exchanges and inputting.

7.7.1. Data Cleansing After data is stored, it might contain inaccuracies, incompleteness or errors as result of inaccurate or faulty sensory equipment or procedures. Data cleansing is a process of identifying and correcting or deleting these inaccuracies to improve data quality (Chen et al., 2014, p. 183). The general process of data cleansing includes five complementary procedures: 1. Defining and determining error types 50

2. Searching and identifying errors 3. Correcting errors 4. Documenting error examples and error types 5. Modifying data entry procedures to reduce future errors (Chen et al., 2014, p. 183).

7.7.2. Messy data As a result of the enormous amount of data collected, an almost infinite amount of it can be used on cleansing and assuring data quality. Because of this, some authors argue that we will have to live with ‘messy’ data (Walker, 2014, p. 182). This concept is also referred to as noise and the more data you have, the more noise there is (Walker, 2014, p. 182). There is a disagreement on to whether and how much the presence of noise affects the resulting analyses. Some authors argue that it ‘inevitably entails an upsurge in bad analysis’ (Fung in Bhasin, 2014, p. 57), where others simply argue that ‘more trumps better’ (Hayashi, 2014, p. 36).

7.7.3. Friction in data exchanges When data is exchanged between two systems or databases, it is paramount that the two systems share the same ‘vocabulary’ and ‘speak the same language’. In practice, that is achieved by conforming to the same formats and standards. An exchange between two systems with lacking standards or formats results in increased cost of data cleansing, validation and conversion. This is called ‘friction’ in data exchanges (European Commision, 2013, p. 26). This is not new issue as such, as companies have always struggled with different standards between different internal systems. The new is an increase in data exchange between companies. And as it can be difficult to agree on standards within or even between industries (or countries), this is an area that has also drawn attention of government institutions. One of the examples is the European Commission that has initiated a project to look at how to reduce this friction between European actors as well as a Research Data Alliance initiative to reduce the friction in global data infrastructures for research (European Commision, 2013, p. 26).

7.8. Value The value of big data can be illustrated by the diagram below from one of the big vendors. The predicament of Big Data is that the value of data decreases rapidly over time, but it takes time to analyse and aggregate this transactional data and turn it into valuable and actionable information. So the faster the data can be processed and analysed, the more value can be ‘squeezed’ out of it.

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(Jarr, 2012)

7.8.1. 2 + 2 = 5 The real ‘magic’ of Big Data is in the possibility that sometimes two plus two equals five. This comes in play for example when value of utilizing multiple datasets can be far higher than the sum value of the individual datasets (Chen et al., 2014, p. 203). This is what the red line in the diagram above illustrates.

A lot of the attention on Big Data comes from privacy implication (Chen et al., 2014, p. 203) and the ‘magic’ can also be illustrated by the blurry lines of personal data. When large enough amounts of data gets collected, it becomes possible to identify a person from data that we traditionally think of impersonal. An example of this is “Browser Fingerprinting”, a technique that replaces traditional cookies stored on your computer. After many years of debate there are now extensive regulations and laws regarding the use of cookies by the EU (European Commision, 2012), to great annoyance to internet advertisers who need to identify internet users in order to target ads. By collecting enough data about the user’s browser and system environment (data exchanged when visiting a webpage) and comparing this to a database of other ‘fingerprints’, it is possible to uniquely identity an internet user without his consent or knowledge (and effectively bypass all established regulations), to great annoyance for the privacy organisations (Helmond, 2014, p. 1172). Another example is a study where the researchers were able to predict social security numbers from publicly available data (Acquisti & Gross, 2009) 7.8.2. The half-life of data value Nucleus Research Inc., a company dedicated to ROI analysis, have analysed decision making in 47 companies and found that the scientific concept of half-life can be an applied measure in the diminishing value of data (Nucleus Research Inc., 2012, p. 3). They correlate the rate of decline in data value to the tempo of its use in a company’s decision making processes. They did this by evaluating the companies’

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decision making process by the time spent making a decision that was either tactical (driving process changes in near real time), operational (driving changes that take days or weeks to implement), or strategic (driving changes that become part of a quarterly or longer planning and implementation process) (Nucleus Research Inc., 2012, p. 3).

(Nucleus Research Inc., 2012, p. 6)

Nucleus Research Inc. found that for tactical decision makers the half-life of data was on average 30 minutes, meaning that data older than 30 minutes has lost half its value to tactical decisions. This group, however, had a high standard deviation with some companies reporting half-life as low as 6 seconds. For operational decisions, the average half-time was 8 hours (ranging from 1 to 48 hours) and for strategic decision makers the average half-life was 56 hours (Nucleus Research Inc., 2012, p. 6).

As data valuation is becoming an important part of Big Data business, it becomes increasingly important to find techniques and methods to compute the rate of data depreciation (Chen et al., 2014, p. 203).

One part of the value of data relies on how old it is (Chen et al., 2014, p. 175). However, as the report from Nucleus Research Inc. further notes, there is also a significant amount of time used for searching and analysing data and sometimes on hesitation because of lack of confidence in information available.

They do however not have enough data for fully defining data efficiency (Nucleus Research Inc., 2012, p.

7). Therefore it important to look at the full data-information chain in order to estimate its value.

7.8.3. Repurposing data One characteristic of data that becomes more apparent in Big Data is that it can be repurposed. This means that it can later be used for other purposes than for which is was collected (Chen et al., 2014, p.

203; González-Bailón, 2014, p. 159). This is true when data is combined from different sources to suddenly identifying a person from otherwise impersonal data, and when multiple ‘small data’ are 53

combined in a Big Data analysis and application of data can be far from the purpose for which it was collected (Hayashi, 2014, p. 36).

For this reason, there has been a trend in later years to open data up to the public. This is of course especially apparent in the public sector, where more and more data-collecting agencies are working towards making these large data sets available to the public, either voluntarily or as a result of government regulations (Swetkis, 2014, p. 264). This further complicates the valuation of data as “Every single dataset is likely to have some intrinsic, hidden, not-yet-unearthed value, and the race is on to discover and capture all of it” (Hayashi, 2014, p. 36).

7.8.4. The value personal data It goes without question that not all types of data are of equal value, and a lot of debate arises from the worries of personal data in Big Data applications. Personal data have been of great interest to companies because of its high value. Financial Times have reviewed industry pricing of data in order to figure out what personal data is worth and what affects the value. They found that general information (age, gender and location) is worth 0.5 $ per 1,000 people. Identifying people with certain needs increases the value dramatically, and as such car buyers are worth 2.11 $, pregnant women in their second trimester are worth 110 $, and information on people with an identified certain medical condition is valued at around 260 $ per 1.000 people (Steel, 2013).

Recently MasterCard and American Express have put their data for sale, though in an anonymous and aggregated format (Kaye, 2013). Similarly, the telephone companies Telefónica and Verizon are both working on ways to analyse and package their data about people’s whereabouts, with intent to sell them to retailers in need of a location for a new store or as help for traffic and city planners (Leber, 2013) In a recent study Staiano et al. examined people’s perceived value of their personal information by equiping 60 participants with special mobile phones recording their activity (GPS location, communcations, app usage and media usage) over a 6-week period. Data was sold on regular auction among the participants using a reverse second-price auction (where participants where paid the second lowest bid). Surprisingly, GPS data was valued higher that communication (calls, messages) and in general unusual days more valued higher than typical days (Staiano et al., 2014, p. 12).

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7.9. Implications of Big Data 7.9.1. A changing paradigm Some authors discuss whether the developments in Big Data can constitute a new paradigm within science. It is a debate to whether these changes reflect a new way of “seeing” and understanding, or if it is a true paradigm shift in the Kuhnian way (Abeles, 2012, p. 4). Either way, there seems to be no doubt that Big Data can have a big impact on scientific methods.

The authors that see Big Data as a new paradigm often refer to it as ‘the fourth paradigm’. The first paradigm is the generation of theory, following the Enlightenment in the 18th century, which requires little data to construct models, The second paradigm is experimentation emerging during the 19th century, in which apparatus, artefacts, and observation are used to test theories and models (Eitel, Kanz, Hortig D., & M.A., 2000, p. 321; M. L. Nelson, 2009, p. 6).

There is some discussion and confusion regarding the third paradigm. First of all some refer to the third paradigm as computation, which is a specialization of experimentation with the unique numerical opportunities that computers have provided (M. L. Nelson, 2009, p. 6), and others to the emergence of the evidence-based approach (e.g. evidence-based medicine, evidence-based management) which can be interdisciplinary and use systematic reviews, meta-analyses and other techniques to assess the evidence quality of existing research (Eitel et al., 2000, p. 322) It is agreed that for each new paradigm more data are required, but it can be discussed if and when an increased amount of data justifies its own entity (M. L. Nelson, 2009, p. 6). One way to see this changing paradigm is that science no longer fits the books and journals in which it is published. Large sets of data do not fit the publication and instead the author vaguely states ‘contact the authors for the complete data set’ or links to old webpages with a “404” response (M. L. Nelson, 2009, p. 7). However, the Big Data literature shows more changes than just an increase in data.

In the literature of Big Data, the fourth paradigm is often referred to as data-driven science. Data-driven discovery is fuelled by two aspects of Big Data. First, the idea that it is possible to collect all the data concerning a phenomenon. Supporters of data-driven discovery argue that there is need for sampling or extrapolation, and obviate scientific methods in the data collection process (González-Bailón, 2014, p.

157; Walker, 2014, p. 183). Secondly, advance algorithms are more than ever capable of doing the dataanalysis on their own. Statistical methods are being replaced by prediction models, machine learning and neural networks (Helbing & Balietti, 2011).

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Big Data is a very new phenomenon and though the literature is booming, it is so new that not many cross-references exist within the literature, and discussions of Big Data are therefore very new and fragmented. And the concept of data-driven science is even newer and the debate even more fragmented. Many scientists are of course very sceptic about the developments and their proposed impacts, but the phenomenon is so new and undefined, that many of the comments are very broad and general. For this reason their analytic value is very limited, and it makes it hard to pinpoint arguments for and against the assumptions that data-driven science is based on.

7.9.2. Cost of data-driven science As earlier noted by Nelson (2009), while data discovery in Big Data might initially be a cheap project, the cost of conserving the data, along the included cost of continuous support for access, migration to new standards and updating the data might turn out to be much more expensive than expected (M. L. Nelson, 2009, p. 7). In the following graph Nelson has tried to picture a comparison for the cost of science in the four paradigms, which shows that new paradigms (both computational and data-driven) entail low initial cost, but might be a lot more expensive in the long rung:

(M. L. Nelson, 2009, p. 7)

This proposes new challenges for the scientific community in order to handle, store and preserve the data for transparency and further use, as a big part of Big Data is that the data might also have a larger future value, as earlier noted.

7.9.3. Data-driven discovery I will choose to call data-driven science for data-driven discovery. This is for two reasons. First of all, the new paradigm is that knowledge creation is no longer so ‘scientific’, and not necessarily made by scientist. Secondly, Big Data and data-driven discovery requires two things, or tree resources. It requires

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an immense amount of data, it requires highly specialised technical competencies, and as just noted, it requires money.

These first two resources are currently unavailable in science, and the last one has always been scarce.

Exactly these competencies needed in Big Data are in shortage and are predicted to be even more so in the future. The skilled people reside currently in the larger enterprises who have invested in Big Data in recent years. The large amount of data needed in Big Data also currently resides in the larger companies, such as electricity companies, telephone companies, e-mail hosting companies or social media companies.

7.9.4. Critique of Big Data Furthermore, companies do not care as much about the negative aspects of making decisions based upon Big Data and data-driven discovery. The biggest critiques about Big Data and data-driven discovery are that it primarily seeks correlation and ignores the need for investigating causality, and emphasized the efficiency of correlation over accuracy in the result (Chen et al., 2014, p. 205; Helbing & Balietti, 2011, p.

19; McAfee & Brynjolfsson, 2012, p. 68; Yin, Jiang, Lin, Luo, & Liu, 2014, p. 15).

But companies have never cared so much about causality, as long as it is efficient and returns on investment. And the possibility for a great return exists. As Hayashi explains about ‘The Prediction Effect’, even a modest increase in the accuracy of predictions can give substantial savings. Event To wit: Even a modest increase in the accuracy of predictions can often result in substantial savings, and tells of an insurance business that had saved almost $50 million a year by using predictive analytics to shave just half a percentage point off its loss ratio (Hayashi, 2014, p. 38).

The second critique is that it is hard to include context when data are aggregated at larger scale, and that it is harder when data are used to fit into a model. (Bail, 2014, p. 477; Boyd & Crawford, 2012, p. 670).

And as noted by Burell, the informational value of data can be highly questionable (Burrell in Zins, 2007, p. 481). This is called meta-data within Business Intelligence, but no means yet exist to include them on a larger scale in Big Data.

7.9.5. Data-driven science In order for data-driven discovery to be data-driven science it needs scientists. In order for this to be a reality, science first of all needs investments, first to train scientist in Big Data techniques, secondly to cater to the increased costs of maintaining data-driven science. Thirdly, science needs data. Science has plenty of ‘small-data’ scattered in different places and systems, but needs common standards and an

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infrastructure capable of integrating these data for Big Data purposes. Such ideas are already partly in the making in form of the Research Data Alliance initiative (European Commision, 2013, p. 26).

Right now the discussion of Big Data in science is fragmented and vague, but when science has accommodated these three resource needs, the debate will mature and begin to give a more clear answer to what this really means for science. Is it some form for new positivism or can we incorporate context, meta-data and causality in the Big Data paradigm?

Until then, science will have to rely on teaming up with big enterprises to take advantage of Big Data opportunities. And big companies will likely use data-driven discovery to try to make better decisions, and all until now indicates that they will be successful, but the area is still too new to know for sure.

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8. A Data -> Change model In this analysis I will try to answer the first part of the research goal – How can we integrate Business Intelligence and Change Management…?

This chapter is inspired by the model of Boisot & Canals of how agents turn stimuli into data and data into information, under the influence of perceptual and conceptual filters. Alongside Bhimani & Willcocks, I argue that this model is applicable on an organisational level, and use the work of Nonaka & Takeuchi to discuss different ways of increasing the organisational knowledgebase through the creation and sharing of knowledge. Last, a definition of planned change is implemented into the model, as well as the possibility of hidden processes to cause unintentional outcomes of in the organisation.

8.1. Tacit and Explicit knowledge Earlier in this thesis, the model of Boisot & Canals was used to depict how data is transformed into information and from information into knowledge. In a Big Data context, Bhimani & Willcocks also find inspiration in the model of Boisot & Canals. They do however also find inspiration in the work of Nonaka & Takeuchi (1995) on tacit and explicit knowledge. Together this gives a view on how both explicit and tacit knowledge is created and shared within an organisational perspective.

(Bhimani & Willcocks, 2014, p. 471)

According to Nonaka (1994), knowledge is fundamentally created by individuals (Nonaka, 1994, p. 17).

Organisations however, can support amplification of the knowledge created and crystallize “… it as a part of the knowledge network of organization” (Nonaka, 1994, p. 17). Tacit knowledge is knowledge that is

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hard or impossible to articulate or codify, and often related to action, commitment, and involvement in a specific context (Nonaka, 1994, p. 16). Explicit knowledge on the other hand is codified knowledge and is as such transmittable in a formal, systematic language (e.g. information) (Nonaka, 1994, p. 16).

8.2. Knowledge creation According to the Nonaka’s thought on knowledge, the knowledge ‘level’ of an organisation can be amplified either through knowledge creation or knowledge sharing. Nonaka’s model is based on an assumption that knowledge is created through the “…conversion between tacit and explicit knowledge” (Nonaka, 1994, p. 18), but Nonaka does not reflect further on how knowledge is created in first place.

This is however covered by the work of Boisot &Canal/Bhimani & Willcocksas earlier discussed.

A new aspect of knowledge creation from Nonaka, is what he calls combination. Organisational members can, through exchange and combination of knowledge, create new knowledge (Nonaka, 1994, p. 19), which is basically the same concept in which the ‘magic’ of Big Data finds new structures and create information throug compilation of existing datasets. As Nonaka is aware of, it can be nessecary to break down the knowledge into its essential parts (data), and so knowledge creation can come through the “… reconfiguring of existing information through the sorting, adding, recategorizing, and recontextualizing…” of existing knowledge (Nonaka, 1994, p. 19).

8.3. Knowledge sharing Knowledge can be shared either in the tacit form through socialising, through the externalisation of tacit knowledge into explicit knowledge, or through internalisation explicit knowledge into tacit knowledge, a process similar to the traditional idea of learning (Nonaka, 1994, p. 19) As tacit knowledge is, by definition, hard to articulate, it is also hard to share (Nonaka, 1994, p. 16). But it can be shared, even without language, through continuous interaction - a process Nonaka suitably calls socialisation (Nonaka, 1994, p. 19). Explicit knowledge might be easier to share through instant communication, but no information media can contain all the complexity of knowledge and its context.

Communicated information therefore always contains some tacit elements and noise. Nonaka compares this to explicit knowledge being digital and information and tacit knowledge being analogue (Nonaka, 1994, pp. 16–17).

These four modes of knowledge conversion, as Nonaka calls them, can be compared to theories of organisation. The mode of socialization is rooted within theories of organizational culture, the mode of combination within information processing and internalization and is in close connection to

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organizational learning. Externalisation on the other hand is an area not well developed within organisational theory (Nonaka, 1994, p. 19)4

8.4. Structured and unstructured knowledge The distinction between explicit and tacit knowledge is almost identical to the earlier distinction between structured and unstructured data that entails that both knowledge and data can be structured/codified.

So there is a great similarity between the different types of data and the different types of knowledge, however not a complete overlap, as not necessarily all unstructured data is difficult to structure, whereas tacit knowledge by definition is difficult to extract information from. As mentioned by Nonaka, information is often of an analogue nature and leaves much for interpretation (Nonaka, 1994, p. 19).

Information, as well as data and knowledge, can be more or less structured. A spreadsheet of analysed data along a user-friendly graph can easier transfer explicit knowledge, whereas a large text or image contains lots of tacit information that might be received by the recipient as tacit knowledge.

8.5. A Data -> Change Model By combining the ideas of Boisot & Canal, Bhimani & Willcocks and Nonaka with the previous chapters of Business Intelligence, Big Data and Change Management, it is possible to create a model for how organisations sense and interpret their environment (which can be both internal and external) and how organisations use this to enable change.

4

A method develop by Lego called Lego Serioues Play, builds on hand-on experience and Lego as metaphors in order to help articulate (and externalise) tacit knowledge. See (Roos, Victor, & Statler, 2004)

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Data Processing

Information

Externalise

Data Storage

Communication

Data Collection

Tacit Knowledge

Organisational view

Conceptual filters

Explicit Knowledge

Internalise

World

Perceptiual filters

Modeling

Combination

Desired State

Current State

Change tension

Organisational Knowledge Base

Expectations

Decision to change

Change process

Change Outcome

Hidden Processes Time

Socialisation

In the above model I have illustrated an organisational data -> change model. As in the model of Boisot & Canals, data is sensed and collected through a data collection process in which perceptual filters to reduce the energy and amount of data is stored. Subsequently, the data is processed and patterns and irregularities are recognised, in order to turn the data into information under the influence of conceptual filters.

It has earlier been discussed how processes in Big Data aim at reducing and compressing the amount of data to maximise processing capability. As noted by Boisot & Canals, the organisational knowledge base affects the perceptual and conceptual filters in the evaluation of which sensory input and data to keep and which regularities to value. Following Nonaka and Bhimani & Willcocks, the information is then modelled and communicated, internally and externally, which affects both the tacit and explicit part of the organisational knowledge base. The knowledge is further amplified and shared throughout the organisation, increasing the organisational knowledge base.

The organisation uses the explicit knowledge to view and evaluate its current and a possibly desired state, from the definition of Change Management in chapter Error! Reference source not found.. When enough ension arises between the current and desired state, the organisation will decide to change and initiate a change process. It is further illustrated how the tacit part of the knowledge base might disturb hidden processes in the organisation and affect and create unintentional change outcome. Lastly, the model illustrates how the organisation affects its environment through external communication and actions (the change process).

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9. Four Modes of Change In this chapter I will try to answer the second part of the research goal: … to get an understanding of how organisations use knowledge creation in the change process?

In the following I will discuss the data -> change model, and elaborating further on the elements in the value chain from data to change. I argue that at least two dimensions can be defined throughout the chain, in both the data collection methods, the characteristics of change, data, information and knowledge, the organisational interpretation, the decision making process, and the type of change. This gives a three dimensional model, resulting in four modes in which organisations can understand their environment and facilitate a data -> change process accordingly. A thirds dimension goes through all the elements and indicates the amount of change over a given period, also earlier noted as the rate of change.

9.1. Data Structure As noted several times, there is often a distinction between structured and unstructured data. The term unstructured data dates back as far as the 1960’s (Reid, 1975, p. 231). Basically, unstructured data is all that data that doesn’t fit into an Excel spreadsheet, but can potentially be loaded into a database (as it is data). This can for example be images, sound, video or text (documents, e-mails, messages, and articles).

In one sentence, Lee, Wang, Wang, & Cheung defines unstructructered data as: “… unstructured data (or unstructured information) refers to (usually) computerized information that either does not have a data model or has one that is not easily usable by a computer program.”

(Lee, Wang, Wang, & Cheung, 2012, p. 12744)

Unstructured data can in certain cases, and to some extent, be structured either by a manual or automatic process. In the early age of the computer, scientist relied on a team of coders to interpret, categorise and put the unstructured data (usually from surveys or interviews) into a computer system (as well as programmers to create a custom statistic program) (Reid, 1975, p. 231). In recent times, several programs exist to aid and automate parts of the process, most data are already digitalised and some of it is already semi-structured by containing metadata (e.g. image description or GPS-location). As mentioned earlier, more advanced techniques are emerging to automatically extract structures from unstructured data (e.g. face/voice/object recognition, semantic analysis).

In our three dimensional model the different structures of data is illustrated by two axes. The first is defining the relative amount of data, and the other two the ratio for how much of this data is structured 63

and/or unstructured. In order of completeness it is necessary to include non-data or noise, which as well can have an entire structured or unstructured appearance. Nondata is not something that cannot be put into a database. According to the earlier stated definition of data, data needs to have a connection and resemblance to the ‘real’ world, which means that it is possible for something to exist in a database without any connection to anything and therefore does not qualify for data. Within Business Intelligence and Big Data, this aspect is often referred to as noise (Walker, 2014), but as it does reside as, and within, data in a database, I choose to call in non-data, in contrast to noise which exists in the sensory equipment before the stimuli is recorded as data, and as thus can lead to non-data. The two axes are illustrated in our model as follows. The centre of the model depicts an equal amount of data and non-data of equally unstructured and structured nature.

Data Structure

9.2. Data Collection Methods In the end of the 19th century modern sociology and psychology took form, and a discussion originated between supporters of quantitative and qualitative research methods respectively. August Comte initially argued in 1844 that the positivistic methods from the natural sciences could be transferred into the social sciences, and Émilie Durkheim founded academic sociology on the ground of these thoughts. The quantitative method was loosely criticised from around the turn of the century, initially by the founders of phenomenology, but it was not until the 1960’s and especially the 1980’s that the qualitative methods gained ground as a response to an increasing complexity in the social system (Newman, 1998, p. 6).

As a response to the critique and shortcomings of positivism, a group of scholars met in the 1910’s, an event later known as the Vienna circle, further developed positivism and gave rise to logical positivism,

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which added an element of rationalism to positivism where knowledge could be derived from logic or reason (Newman, 1998, p. 4).

Guba (1978) examines 14 differences between what he defines as Naturalistic Inquiry and Conventional Inquiry. In a later article, William Firestone (1987) draws parallels between respectively Naturalistic Inquiry and Conventional Inquiry, and qualitative and qualitative methods. Firestone sums up the 14 differences into the 4 most important ones: 1. Philosophy: Quantitative research is built upon the positivistic tradition, whereas the qualitative research stems from the phenomenological tradition.

2. Purpose: Quantitative research seeks to explain the causes of changes, whereas qualitative research is concerned with understanding the phenomenon.

3. Approach: Quantitative research typically employs empirical or correlational designs, whereas qualitative research is prototypically examined through ethnography.

4. Role: In quantitative research the researcher is ideally detached to avoid bias, where in the qualitative research the researcher is immersed in the phenomenon of interest.

(Firestone, 1987, p. 17)

In recent years, some researchers have argued against this dichotomy and for a synthesised paradigm.

From this discussion, mixed-method research has emerged, which allows for more than one approach to be utilised in a single research program, even across the quantitative-qualitative paradigms. Cook & Reichardt (1979) argue that the researcher’s method can be separated from the ‘worldview’ of the research, and thus it is possible for a researcher to shift between the paradigms and even use both quantitative and qualitative approaches (Cook & Reichardt, 1979, p. 12).

Newman (1998) goes further and argues that the dichotomy of quantitative/qualitative research can instead be seen as an interactive continuum, and creates a synthesised model and method for all research (Newman, 1998, p. 23). Quantitative data is by definition structured, whereas qualitative data can be more or less structured and many qualitative approaches include methods of structuring the data.

As a consequence, it is possible to depict Newman’s continuum of scientific methods in the model as going from quantitative methods, which employs structured data, over mixed methods, which can entail both structured and unstructured data, to qualitative methods that initially rely on unstructured data.

None or poorly executed methods can produce noise and result in non-data.

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Data Collection Methods

9.3. Information Characteristics The earlier definition of information as ‘significant regularities within data’ still leaves us to define what is meant by, and who is to be the judge of, what is significant. Boisot & Canals introduce convention as the means by which significance is measured (Boisot & Canals, 2004, p. 47). Generally, it is the individual who is the measurer of what constitutes information and what does not. As such, all information is subjective.

Although Boisot & Canals argue, that by convention of what constitutes ‘a significant regularity’ information can appear to be objective. However, as Boisot & Canals note, this happens only within the community regulated by that convention (Boisot & Canals, 2004, p. 47).

This means that in order for data to contain objective information, the data needs to have been processed within the domain of a given convention. A lot of data processing entails aggregation and thereby structuring of the data. This means that structured data within an organisation, given that the structuring processes are done by convention, is more objective than unprocessed, unstructured data.

This conforms with the data collection methods, where qualitative methods generate generalised, objective and context-free conclusion whereas qualitative methods is more critical towards the contextfree nature and generalizability of their results (Newman, 1998, p. 50).

Qualitative data is, as consequence of its unstructured nature, subject to interpretation, as it can contain several possible ‘significant’ regularities depending on the conventions used. The data residing in structured data is therefore of a more explicit character than information residing in unstructured data where the information tends to be more implicit or tacit. In this view, encryption and decryption can be also by seen as the processes of converting information between an implicit or explicit state.

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In Business Intelligence there are several processes that are concerned with explicating the information contained within data. Structuring data by quantification or organisation, data analysis and data mining, as well as basic reporting and data visualisation techniques, has the purpose of analysing and presenting the data in a way that explicate its information to the recipient.

In the dimensional model of change, information can therefore take the form of either more objective and explicit character, or subjective and implicit character. On the other axis it is possible to distinguish between the relative amount of information and misinformation, where the information is built on respectively data and non-data.

Information Characteristics

9.4. Knowledge Criteria Now it is time to take a closer look at knowledge. Ever since Plato and his dialogues have scientists and philosophers tried to define knowledge and the topic is still eagerly debated as the analysis of Zins (2007) shows. There exists a long and ongoing debate on Plato’s definition of knowledge as Justified True Belief, a debate which goes a lot further than the limits of this thesis. In reviewing the 45 definitions of knowledge from leading scholars that Zins (2007) collects, there seems to be a general acceptance of the underlying basics of Plato’s definition, even though many try to go further yet. As Zins finds, no one has reached an objected definition of knowledge. No matter how many objective criteria we set for knowledge, knowledge will remain an ideal, and “… a claim of knowledge should never be regarded as finally verified” (Hjorland in Zins, 2007, p. 484).

Plato’s definition states that in order for something to be knowledge is has to reside in an individual as a belief, it has to be (objectively) true, and the holder has to be justified in believing so. (Chisholm, 1982, p.

43). It is often discussed what is sufficient justification, for example in the Getter’s counterexamples that 67

is aimed at proving that Justified True Belief is not sufficient criteria for knowledge (Gettier, 1963).

Gettier’s argumentation builds upon the view that the true criteria of knowledge is binary, e.g. either true or false, and that logical deduction is there as a means of justification. If however, the truth is an ideal as mentioned, then the only knowledge we can obtain is a matter of probability, and justification falls in the category of profitability calculation. Following this, a logical deduction from two statements with of 70% probability will result in only 49% probability (0.7*0.7).

In this model no information or knowledge can come from just one piece of data, as it contains no regularities, and data processing always entails aggregation of data. As such, even with a binary truth value for each of data used in a data processing, the truth value for the resulting information and knowledge would be an assigned probability calculated from the truth distribution in the data. And seen from this perspective, true knowledge remains an ideal in which all the data in the data process are true without any noise or nondata involved.

These considerations can be depicted in our model as two axes indicating respectively the truth value and the justification value for an aggregated and processed amount of data. The truth value is pointing south, meaning that the more quantitative data the knowledge is aggregated from, the more true knowledge is obtained. In the east direction, justification is pointing towards that the more qualitative data is obtained, the more we ‘know’ about why our knowledge is true.

Justifying

Validating

Knowing Why

Knowledge Criteria

Applied from Plato (Chisholm, 1982, p. 43)

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9.5. Decisions Making Change literature rarely includes aspects of decision making, as most change literature come in play after the decision to change has been made. I have found this interesting to be included because it was possible to find two models of decision making that, at least to some extent, connect very well with the rest of the model. The two models are Differentiation and Consolidation theory and Max Weber’s four types of social action. Because it is somewhat out of the scope of this thesis, many aspects could have been elaborated further. It does however, give good opportunities to further research into the connections between decision making and respectively Business Intelligence and Change Management.

According to Svenson (1996), decision making theory has, during the last half of the 20th century, evolved from a structural perspective to a more process-oriented view. Svenson himself is the originator of Differentiation and Consolidation theory, a theoretical framework for decision making that does not build on one decision making theory but argues that different decision making models can be used for different situations. Furthermore, Differentiation and Consolidation theory includes the possibility of bad decisions in which the decision maker, in a post-decision process, evaluates the decision. This can result in degrees of regret, the evaluation can be biased towards supporting the chosen decision, and search of new information supporting the prior decision (O Svenson, Salo, & Lindholm, 2009, p. 405; Ola Svenson, 2003, p. 317; Orla Svenson, 1996, p. 254), this is an interesting aspect in the light of failed change initiatives.

9.5.1. Differentiation and Consolidation Theory Differentiation and Consolidation Theory distinguishes between four deferent types of decisions problems. The types can be seen as a framework than incorporates many theories of decision making.

According to Svenson, many theories of decision-making focus on one of these types only and most of literature is focused on level 3 decision problems. The different types of decisions problems are enlisted in a level hierarchy in which higher levels require more ‘energetic resources’ to make a decision (Ola Svenson, 2003, p. 302).

Level 1 Problem is well-known and past experience determinates the decision to be made, without reference to attractiveness.

Level 2 No conflicts between attributes and solution is obvious with a few references to attractiveness, including meta rules and Quick emotional reactions.

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Level 3 A choice between alternatives with goal conflicts, with some attributes in favour of one alternative and other attributes in favour of another alternative.

Level 4 Decision alternatives are not fixed nor is the set of attributes Problem solving constitutes an important sub process in decision making at this level.

Created on the basis of Svenson (2003, p. 302; 1996, p. 254)

The four levels can be distinguished in the terms of interests and alternatives. In Level 1 there are no alternatives as the problem is well known (so the solution is the ‘same as always’) and there is no conflict in interests (each alternative has equally attractive attributes, as there is only one). In level 2 there are alternatives, but the choice between attributes is simple and easy. In level 3, there is both a conflict in alternatives and a conflict in interests, as some of the alternatives have different attributes. In Level 4 there are new and unfamiliar alternatives and the interests and attributes need to be investigated.

The consequence of applying these levels to the model is that it is possible to make decisions on the grounds of both beliefs and true beliefs as long as the decision is simple or the problem is familiar.

Decisions based on justified beliefs have the answer to ‘why’, but without the knowledge of certainty, the decision becomes a balance between conflicting interests with arguments for and against each alternative. When new and unfamiliar problems arise, it is necessary to examine the problem and acquire both new qualitative and quantitative knowledge in order to understand the issue.

9.5.2. Types of Social Action Another theory of ‘decision-making’ is Max Weber’s four types of social action. Weber distinguishes between the traditional rationale, the affectual, the value-rational and the instrumentally rational (Weber, 1978, p. 24). As Weber notes, both the traditional and affectual rationale is close to the borderline of being a decision, as it often does entail a choice as such. In the traditional rationale no alternatives are investigated and the affectual rationale is often an automatic emotional reaction to exceptional stimuli (Weber, 1978, p. 25). Value-rational action is similar to affectional action in that the decision rationale is not built on the action outcome, but on the nature of action at its attractiveness. And lastly, the Instrumental rational action takes all means, results and secondary outcome into consideration in the decision-making.

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9.5.3. Decision-making and Business Intelligence This enables a discussion of decision-making in relation to Business Intelligence and our earlier distinctions. In the level 1/ traditional quadrant, the analysis of quantitative and true data gives a clear answer to the decision-making process. This is in its furthest extent similar to the metaphor of a ‘demise of the expert’ earlier discussed, in which Big Data gives a clear prediction of the outcome, no questions asked. Change is therefore direct response to the received stimuli.

Conflicting interests New interests

No conflict between interests

Two typologies of decision-making

(Weber, 1978, p. 24)

(Ola Svenson, 2003, p. 302)

In the affection rationale, noise and nondata are disturbing the analysis to such an extent that there is no better answer and decision-making is left to the randomness of affection and intuition. The value-rational action is built on qualitative, justified data giving the actor an understanding of the decision and its alternatives, but without any certainty of their results. Decision making is therefore built on values and the nature of the action, or in an attempt to affect the environment in a given direction.

The instrumentally rational action is built on both quantitative, true data and qualitative, justified data, and is as such the ideal form of knowledge and full information. Instrumental decisions can therefore take all considerations into account and make decisions that both create the best result and affect its environment in an intended direction.

This is of course an ideal look at the four quadrants and a decision would only to some extent fit into one or more of these categories. Since none of the two models have defined dimensions it is difficult to define them in anything but as ideal types.

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9.6. Organizational interpretation modes The next step in the model is how organisations view and interpret its environment. Here Daft & Weick have created a 2-dimentional model of organisational interpretation. This model is built upon an view of the organisation and it’s environment, where organisations scan and collect data about the environment, interpret and extract meaning from the data, learn and take action from this interpretation as depicted below:

(Daft & Weick, 1984, p. 286)

This view of the organization is very consistent with the initial framework for Business Intelligence and Change Management proposed in chapter 8. The underlying idea is that organisational resources allocated for surveying its environment are scarce and will therefore be deployed in extent and in ways that are conform with the way the organisation views itself and its environment (Daft & Weick, 1984, p.

284).

The two dimensions that Daft & Weick define in their model are (1) the beliefs of the analysabilty of its environment and (2) the extent to which the organisation intrudes into its environment to understand it (Daft & Weick, 1984, p. 287).

(Daft & Weick, 1984, p. 289)

As mentioned, organisations can assume their environment to be either analysable and predictable or unanalysable and to some extend unpredictable. This can for example be seen in the data used in data collection process, where organisations, seeing their environment as predictable, will put a lot of energy into quantitative data and statstical meassures, whereas other organsiation will rely more on qualitative studies and judgement (Daft & Weick, 1984, p. 287).

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The second dimension is focused on how actively the organisation scans its environment. Passive organisations use the data already available, whereas the more active ones question the data and seek new data (Daft & Weick, 1984, p. 288).

Active engagement

Passive observation

Organisational interpretaion modes

Applied from (Daft & Weick, 1984, p. 289)

In relation to the multidimensional model in this thesis, this gives a correlation between how the organisation views its environment and its engament, and through which methods and data it seeks knowledge from. When an organisation views its environment as analysable, it will attempt to find the objective ‘truth’ - in this model through the quantitative methods - and either passively and conditionally view its surroundings through this truth, or actively try to discover and understand its surrondings and in which ways it can affect it. If the organisation views its environment as unanalysable, it will either passively and undirectedly view its surroundings and seek no data, or actively seek an understanding of its environment through qualitative methods to gain an understanding of it surroundings how it might be able to affect it.

9.7. Modes of change Last but not least, the multidimensional model comprises a view on how all these different kinds of data collection, information and knowledge creation along with an organisational view and decision-making process determine which kind of change the organisation will be affected by.

This part will build on the work of van de Ven & Poole (1995). Through an interdisciplinary literature review containing 200,000 titles, 2,000 abstracts and 200 articles, van de Ven & Poole (1995) identify 20 different process theories that vary in substance and terminology. From these 20 theories they identify 73

four ideal-type development theories (which they call motors of change) which provide “… fundamentally different accounts of the sequence of events that unfold to explain the process of change in an organizational entity” (van de Ven & Poole, 1995, p. 513).

A change theory is not confined to only one of these four ‘schools of thought’, but can contain elements from one or more to explain the observed processes of change, giving a total of 16 theoretically possible theories to address change, where the simplest contain only one motor of change and the most complex builds on all four (van de Ven & Poole, 1995, p. 528) Four motors of change “A life-cycle model depicts the process of change in an entity as progressing through a necessary sequence of stages. An institutional, natural, or logical program prescribes the specific contents of these stages.

A teleological model views development as a cycle of goal formulation, implementation, evaluation, and modification of goals based on what was learned by the entity. This sequence emerges through the purposeful social construction among individuals within the entity.

In dialectical models of development, conflicts emerge between entities espousing opposing thesis and antithesis that collide to produce a synthesis, which in time becomes the thesis for the next cycle of a dialectical progression. Confrontation and conflict between opposing entities generates this dialectical cycle.

An evolutionary model of development consists of a repetitive sequence of variation, selection, and retention events among entities in a designated population. Competition for scarce environmental resources between entities inhabiting a population generates this evolutionary cycle.” (van de Ven & Poole, 1995, p. 520)

Van de Ven & Poole’s model is made from two dimensions of change. The first axis defines whether the change is prescribed or constructed. In the prescribed domain, change steps are determined by their antecedent events. Even though the steps might at times be large and seem radical, they are always prescribed by an underlying continuity. Constructive change on the other hand can be disruptive and ‘game changing’ as new goals are enacted underway. Van de Ven & Poole compare prescribed and constructive change with the common distinction between first and second order change, saying that prescribed change will usually lead to first order change and constructive change will lead to second order change (van de Ven & Poole, 1995, p. 522).

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(van de Ven & Poole, 1995, p. 520)

The second axis discerns whether the change is powered by and within one entity or in the competition or struggle between multiple entities. In the life cycle model the change is prescribed, uncontested and unchallenged and the teleologycal model goals of change are set by the ‘free will’ of the entity. When taken to the macro level, entities can either compete against each other in the evolutionary model or disagree on the goal of change and, by force or negotiation, set a common direction (van de Ven & Poole, 1995, p. 521).

Prescribed mode of change

Constructive mode of change

Motors of change

Applied from (van de Ven & Poole, 1995, p. 520)

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Including the van de Ven & Poole, the model is now a two-dimensional model going through all the chronological steps in the data-chain value chain. It shows four modes each of which built on different organisational interpretations of the environment and different types of data. I will use the names made

by Weber for the four quadrants in the model, as they describe the modes very well. The affectional mode of is characterised be competition in an unanalysable environment. This is the mode of change that organisations will reside in, when acknowledging that the environment is changing too fast for them to perceive and they therefore have no data or knowledge about their new situation. They might also still reside in the traditional regime, where they only look at the same data as always. Here change is of first order nature, happening only within the existing frameworks. Oppositely, in the value-driven mode, organisations cannot analyse their environment, but instead of active passively or randomly, they believe that they instead can affect the environment in a given direction and therefore actively seek qualitative data and understandings of the world around them. In the last quadrant, organisations innovatively seek new understandings of the world, while still relying on qualitative data to predict future changes.

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10. Conclusion The interest of this thesis is sparked by the paradigm-changing impacts of Big Data. Big Data is used in organisation to make correlations and predictions from data collected, with the purpose of better decision-making, and ultimately to change the organisation to meet challenges in the organisations current and future environment.

Through a meta-review, this thesis reviews the newest literature on Change Management, Business Intelligence and Big Data, in order to answer the research question: How can we integrate Business Intelligence and Change Management, in order to get an understanding of how organisations use knowledge creation in their change process?

In this thesis I have developed a data -> change model to understand how data is collected by organisations through perceived stimuli from its internal and external environment, how organisations create data though conceptual filters and information modelling into explicit and tacit knowledge , how the organisational knowledge base is increased through knowledge sharing, how the knowledge base is used to initiate change initiatives, and how tacit knowledge in the organisation might unintendedly affect the change process outcome.

The result of this study is an extension of van de Ven & Pooles four motors of change. The model explicit four different ways in which organisations can facilitate change through four different ways of knowledge creation, all depending on the interpretation the organisational has of their environment.

10.1. The Monkeys, the Banana and the Shower “Four monkeys were put into a room. In the centre for the room was a tall pole with a bunch of bananas suspended from the top. One particular hungry monkey eagerly scampered up the pole, intent on retrieving a banana. Just as he reached out to grasp the banana, he was hit with a torrent of cold water from an overhead shower. With a squeal, the monkey abandoned its quest and retreated down the pole.

Each monkey attempted, in turn, to secure the banana. Each received an equally chilly shower, and each scampered down without a prize. After repeated drenching, the monkeys finally gave up the bananas.

With the primates thus conditioned, one of the original four was removed from the experiment and a new monkey was added. No sooner had this new, innocent monkey started up the pole than his (or her) companions reached up and yanked the surprised creature yank down from the pole. After a few such aborted attempts, but without ever having received a cold shower, the new monkey stopped trying to get the bananas. One by one, each of the original monkeys was replaces.

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Each new monkey learned the same lesson: don’t’ climb the pole. None of the new monkeys ever made it to the top of the pole; none even for so far as a cold shower. Not one understood precisely why pole climbing was discouraged, but they all respected the well-established precedent. Event after the shower was removed, no monkey ventured up the pole.”

(Hamel & Prahalad, 1995, pp. 51–52)

The above story is a popular reference and has circulated the internet for many years in different shapes, often accompanied by a cartoon. It originates from a fictional experiment written by Hamel & Prahalad (1995) in their management book Competing for the Future. A similar experiment was in fact conducted

by Stephenson (1967) and also described in Galef (1976, p. 88). The story is used to describe several difficult aspects regarding knowledge acquisition and the difficulties of knowing what we now.

This story exemplifies many aspects of the multidimensional model and framework. In the story, knowledge is socialised between the monkeys. Since monkeys lack a defined language the can only share and work with tacit knowledge. The story exemplifies how the knowledge transfer of justified knowledge (knowing why) is more difficult, and maybe more complex, than true knowledge (knowing what and when), as the monkeys are able to transfer the knowledge of not climbing the ladder, but not of why.

Because of the lack of sharing, the knowledge of why not to climb the ladder is forgotten over time. This is twisted by removing the showerhead, thus making the previously learned ‘true’ knowledge ‘untrue’.

Furthermore, what if the showerhead had been removed among the first group of monkeys, then the justification of why not to climb the ladder would be false.

As earlier concluded the value of data is decreasing over time. The lesson to learn from the story is that similar to data, in a changing environment, the value of knowledge too, decreases over time. In a changing environment none not know if the knowledge he possess is in fact still knowledge.

10.2. Big Data: A Change Catalyst Big Data means more data, faster data and new kinds of data, but it also means better predictions and automatic data collection and analysis. High debated, Big Data might obviate the need for scientific methods such as of sampling and extrapolating, as all the data of a population might be collected, and promote a paradigm, where quick and dirty correlation is preferred over time-consuming causation. But for companies, who do not care about causation or understanding its environment, big data is a new way for organisations to analyse its environment and facilitate change initiatives.

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The model developed in this thesis makes it possible to discuss the implication of Big Data as a new paradigm. Based on Plato, the model of explicit four types of knowledge based on whether the knowledge is achieved by means of correlation or causation, or none or both.

From the model it is possible to conclude that Big Data can enforce a traditional mode of change. In this quadrant of the model, change is seen as prescribed, forecasted by the earlier events, in which there are no alternative decisions than the one calculated by the computer. This mode uses a quantitative paradigm, built entirely on structured data, and information that is highly objective and separated from subject and context. The traditional mode of change gives more precise answer to what and when something will happen, but lacks the answer to why and the general understanding of the phenomenon.

The story with the monkeys shows two important aspects that have been treated in this thesis, regarding the speed of change. First of all, change does not happen unless new data is acquired from the environment. In the regular, ‘old’ data discovery, new data is acquired either by a continuous collection of new data in the same data streams, or as a result of a manual and conscious investigations of new topics deemed interesting and possibly fruitful. In a full data-driven discovery scenario however, both these processes are automatic, and processed information is automatically presented, both on data in current streams, but also new data automatically acquired and process in new ways to discover new patterns.

Secondly, in ability to possess knowledge, knowledge creation is a continuous process of reconfirming what we know. In an increasingly changing environment, the value of data and knowledge decreases exponentially. Big Data can help companies change fast and continuously and act as catalyst for change, as it speeds up the process of correlation on both known and unknown phenomena. But it comes with the cost of understanding and inability to affect to world in ways we want it to be, as change will be a continuous forecast of proceeding events.

10.3. Suggestions for further research As many studies do, this thesis might give more questions than it gives answers. Change might be changing with Big Data, but not much is yet known about this new paradigm. Are organisations able to handle continuous change, when change is no longer pulled by manual analysis and intension, but pushed by decisions made by computer algorithms? And how will employees react, when presented with new changes, but when themselves asking about the drives for this change, the only answers they can get are ‘because the computer says so’?

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12. Appendix I: Review literature on Change Management Armenakis, A. A. (1999). Organizational Change: A Review

Journal of Change Management, 5(2), 121–151.

of Theory and Research in the 1990s. Journal of

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CHANGE IN PUBLIC ORGANIZATIONS: A LITERATURE

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O’Neill, P., & Sohal, A. S. (1999). Business Process

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Cao, G., Clarke, S., & Lehaney, B. (2003). Diversity

Ambivalence: A Multidimensional View of Attitudes toward

management in organizational change: towards a systemic

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Price, A. D. F., & Chahal, K. (2006). A strategic framework

Carr, A., & Gabriel, Y. (2001). The psychodynamics of

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Garg, R. K., & Singh, T. P. (2006). Management of Change --

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Higgs, M., & Rowland, D. (2005). All changes great and

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Todnem By, R. (2005). Organisational change management:

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Azevedo, A., & Santos, M. (2009). Business IntelligenceState of the Art, Trends, and Open Issues. In KMIS (pp. 296– 300). Retrieved from http://scholar.google.com/scholar?hl=en&btnG=Search&q

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Dekkers, J. (2007). Organising for Business Intelligence: A

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Chaudhuri, S., Dayal, U., & Narasayya, V. (2011). An overview of business intelligence technology.

information. BLED 2007 Proceedings, 625–636. Retrieved from http://aisel.aisnet.org/bled2007/

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Jourdan, Z., Rainer, R. K., & Marshall, T. E. (2008a). Business

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Intelligence: An Analysis of the Literature 1. Information

Chee, T., Chan, L., & Chuah, M. (2009). Business intelligence systems: state-of-the-art review and contemporary

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applications. In Symposium on Progress in Information &

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Communication Technology 2009 (pp. 96–101). Retrieved

Intelligence: An Analysis of the Literature 1. Information

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Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). BUSINESS

Kettinger, W. J., & Marchand, D. A. (2011). Information

INTELLIGENCE AND ANALYTICS: FROM BIG DATA TO BIG

management practices (IMP) from the senior manager’s

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perspective: an investigation of the IMP construct and its

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measurement. Information Systems Journal, 21(5), 385–

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Lönnqvist, A., & Pirttimäki, V. (2006). The Measurement of Business Intelligence. Information Systems Management,

Chuah, M.-H., & Wong, K.-L. (2011). A review of business

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intelligence and its maturity models. AFRICAN JOURNAL OF

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Mohamadina, A. A., Ghazali, M. R. B., Ibrahim, M. R. B., & Harbawi, M. A. (2012). Business Intelligence: Concepts, Issues and Current Systems. In 2012 International Conference on Advanced Computer Science Applications

Curko, K., & Varga, M. (2008). The review of the role of

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Maturity Models: Information Management Perspective.

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INFORMATION AND SOFTWARE TECHNOLOGIES (ICIST

ethics of business intelligence. Journal of Business Ethics,

2013), 403, 198–221. Retrieved from

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Shi, H., Peng, C., & Xu, M. Z. (2012). Business Intelligence in

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Construction: A Review. In Global Conference on Civil,

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and Soil (Vol. 594–597, pp. 3049–3057). TRANS TECH

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Shollo, A. (2013). The Role of Business Intelligence in

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Van Roosmalen, M. (2009). Operational Business

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EVALUATION (pp. 556–562). ACAD CONFERENCES LTD, Strohmeier, S., & Burgard, M. (2007). Business intelligence:

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culture with big data. Theory and Society, 43(3-4), 465–482.

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Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A Survey.

Performance, Process and Decisions Through Big Data.

Mobile Networks and Applications, 19(2), 171–209.

Quality Progress, 46(6), 68. Retrieved from

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Demchenko, Y., Grosso, P., de Laat, C., & Membrey, P. (2013). Addressing big data issues in Scientific Data

Beyer, M. A., & Laney, D. (2012). The Importance of “Big

Infrastructure. Collaboration Technologies and Systems

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Your Advantage. Financial Analysts Journal, 70(3), 57–58.

we face a fourth paradigm shift in medicine – algorithms in

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EMC. (2014). The Digital Universe of Opportunities.

Bhimani, A., & Willcocks, L. (2014). Digitisation, “Big Data”

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European Commision. (2013). A European strategy on the data value chain.

Gnadinger, T. (2014). BIG DATA AT WORK: DISPELLING THE MYTHS, UNCOVERING THE OPPORTUNITIES. Health Affairs, 33(7), 1302. Retrieved from 10.1377/hlthaff.2014.0593 González-Bailón, S. (2014). Big Data: A Revolution That Will Transform How We Live, Work and Think. Information

Castelluccio, M. (2013). Big Data: Managing the

Polity: The International Journal of Government &

Unmanageable. Strategic Finance, 95(5), 59–60. Retrieved

Democracy in the Information Age, 19(1), 157–160.

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Hayashi, A. M. (2014). Thriving in a Big Data World. MIT Sloan Management Review, 55(2), 35–39. Retrieved from

Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). BUSINESS

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Mervis, J. (2012). US SCIENCE POLICY Agencies Rally to

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Helbing, D., & Balietti, S. (2011). From social data mining to

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forecasting socio-economic crises. The European Physical

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Helmond, A. (2014). “Raw data” is an oxymoron.

(2014). Using big data to predict collective behavior in the

Information, Communication & Society, 17(9), 1171–1173.

real world. The Behavioral and Brain Sciences, 37(1), 92–3.

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Universe.

trends in information technology. Retrieved from Jacobs, A. (2009). The pathologies of big data.

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view. In NSF/JISC Repositories Workshop, April 2007.

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Ontological Approach to Big Data Analytics. Review of Policy

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Research, 31(4), 311–318. doi:10.1111/ropr.12077

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Lott, R. (2014). UNCHARTED: BIG DATA AS A LENS ON

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Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next

Nelson, M. L. (2009). Data-Driven Science: A New

frontier for innovation , competition , and productivity (p.

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Nielsen, M. (2009). A guide to the day of big data. Nature,

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Pool, R. (2013). SMART CITIES: BIG DATA, CIVIC HACKERS

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Sagiroglu, S., & Sinanc, D. (2013). Big data: A review. In

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201–212). Cham: Springer International Publishing. doi:10.1007/978-3-319-03437-9 Swetkis, D. (2014). Putting Big Data in Context. Journal of Public Administration Research & Theory, 24(1), 264–266.

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