Data warehouse for business intelligence 2

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Data Warehouse for Business Intelligence The video should be no more than 5 minutes in length.

You may use any screen capture software, e.g. Snagit, Camtasia, CamStudio, Quicktime, etc. The video should be seen as the presentation to management and show off 2 or more of case studies as well as their successful execution. The voice commentary should explain the motivations and implications of the case studies as well as highlight any interesting / surprising results or outcomes.

Shubham Mishra

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Data

Warehouse

for

Business

Intelligence Task: Architect, Populate and Explore a Data Warehouse for Business Intelligence (40%) This CA will assess your ability to apply the theories, methodologies and strategies tackled in the course to successfully implement a data warehouse for a business intelligence case study.

Setting: your company wishes to consolidate several streams of data ranging from social media, CRM and ERP systems, and industry reports, among others to improve its corporate planning, and reporting mechanisms as well as address issues of competitive intelligence. You have been tasked with building a prototype data warehouse to convince management to invest in a fullyfledged data warehouse. Therefore, not only should you architect and populate the warehouse, you must also prepare 3 or more appropriate case studies that can be used to convince management of the usefulness of a data warehousing solution. Management are currently not concerned with the actual data context of the data warehouse, they rather wish to understand what a data warehouse can do for their business.

CA Deliverables For this CA there are 2 deliverables: 1) a report, and 2) a pre-recorded video demonstration with commentary of up to 5 mins.

The paper should include: 1. A justified data warehouse architecture and implementation using scientific literature where needed/appropriate.

2. A methodical ETL strategy capturing at least 3 sources of data. Extra creditwill be awarded for incorporating and analysing particularly challenging sources of data.

3. Three case studies that highlight the business value of your data warehouse. These should be in the form of non-trivial business intelligence queries, and their output should be appropriately documented.

The final report should not be more than 20 pages including all figures and references.

The video should be no more than 5 minutes in length. You may use any screen capture software, e.g. Snagit, Camtasia, CamStudio, Quicktime, etc. The video should be seen as the presentation to management and show off 2 or more of case studies as well as their successful execution. The voice commentary should explain the motivations and implications of the case studies as well as highlight any interesting / surprising results or outcomes.

Implementation and Architecture (40%) Full freedom on the design and implementation methodology is given to the student. Students are free to use any data warehousing tool set discussed in the course, or otherwise.

The implementation should be stored as a virtual machine (or similar) until Feb 1 st 2016 as you may be called upon to demonstrate your solution if further details or evidence is required to

Freeassignmenthelp.com support your paper and video presentation. It is your responsibility to ensure appropriate storage for your project, failure to do so if demonstration is required could impact the grade for this CA. The final report should detail the data warehouse architecture, data models used, and must detail with supporting argumentation the architectural, methodological and design choices made.

ETL (30%) Students are expected to independently source relevant data for use in the case studies. Note: data sets will likely need cleaning and you need to ensure that appropriate relationships exist between your data sets, such that they can be transformed and loaded into the warehouse for meaningful case studies. The warehouse must incorporate 3 or more sources of data, of which at least 1 should be structured and 1 unstructured. Extra credit will be given for incorporating specifically challenging or innovative sources of data.

The report should detail the sources of data, how they were generated or extracted, and the steps taken to load and transform the data for storage in the warehouse.

Some example sources of data include: -

Social Media and Blogs Corporate documents, reports and white papers Patents (Dummy) CRM (Dummy) ERP (Dummy) Customer transaction records Accounting documents News articles (e.g. via feedzilla) Stock tickers (e.g. quandl.com)

There is a huge number of online repositories of relevant data exist, where some examples include, but are not limited to: -

IBM’s many eyes repository: http://www-958.ibm.com/software/data/cognos/manyeyes/datasets Amazon’s public dataset repository: https://aws.amazon.com/datasets?_encoding=UTF8&jiveRedirect=1 Google’s Public Data Directory: http://www.google.com/publicdata/directory UK’s open government data repository: http://data.gov.uk Central Statistics Office, Ireland: http://www.cso.ie FigShare:http://figshare.com Run My Code: http://www.runmycode.org/ The UCI machine learning repository: http://archive.ics.uci.edu/ml/ Mockaroo (for data set generation): https://www.mockaroo.com

Case Studies (3 x 10%) The case studies represent three exemplary non-trivial business intelligence queries that document potential business value of your prototypical data warehouse via knowledge discovery. Appropriate presentation of the results should be provided in the report. Their

Freeassignmenthelp.com implications should be appropriately discussed referencing relevant literature where applicable.

If more than 3 case studies are documented, the best three will be used for grading purposes.

Suggested milestones for the project: Advisory time plan:       

by Week 5 completed the Multidimensional SQL Server Tutorial

by Reading week identify topic and potential sources of data and ensure you have completed the SSIS tutorial in Week 10 finalise data model (star schema), and have data in the DW in Week 11 have at least 1 deployable cube in Week 12 begin documentation, and plan the video / case studies in Week 13 finalise the report in Week 14 – don’t plan anything for this week, you will probably miss the deadline!

Stuck on the project? Watch these two videos, they tend to help address most problems:  

https://www.youtube.com/watch?v=ctUiHZHr-5M https://www.youtube.com/watch?v=kTPJBAtv29k&list=PL7A29088C98E92D5F

This tutorial is also typically a good guide for the project http://www.codeproject.com/Articles/652108/Create-First-Data-WareHouse

as

well:

Freeassignmenthelp.com Grading Rubric

Implementatio n and Architecture (40%)

H1

H2.1

H2.2

Pass

Fail

The architecture and design is coherently documented.

Appropriate academic literature substantiates well-argued design choices and methodological considerations.

The architecture and design is coherently documented, with well-argued design choices and methodological considerations.

The architecture and design is coherently documented. Design choices are justified, but may lack depth, methodological precision and/or critical evaluation.

The architecture and design is documented in an intelligible manner. Some justification for design choices is given, but lacks in depth and/or argumentation.

The architecture is poorly conceived and/or little to no justification is given for the design of the data warehouse solution.

A well-conceived demonstration video documents key functionality.

The results of selected case studies are illustrated with an advanced grasp of their limitations, implications and efficacy.

A well-conceived demonstration video documents key functionality.

The results of selected case studies are illustrated with an acceptable grasp of their limitations and implications.

A well-conceived demonstration video documents a basic level of functionality. The results of selected case studies are illustrated; an attempt may have been made to discuss their implications and/or limitations.

A demonstration video is provided that shows a functioning data warehouse. However, the video is poorly conceived and/or lacks depth. Some results of case studies are shown, with little to no insightful discussion on their implications and/or limitations.

A demonstration video may be provided, but is poorly conceived or does not clearly illustrate a functioning data warehouse solution.

Meaningful results from case studies might not be illustrated.

Freeassignmenthelp.com ETL (30%)

Case Studies (30%: 3x 10%)

A solution set that addresses complex issues, and performs complex ETL processes using advanced methods.

A solution set that attempts with some degree of success to address some complex issues using some advanced methods.

A solution set that addresses a wide scope of issues and challenges, but lacks depth in one or more components in ETL processes.

A solution set that addresses the basic requirements of ETL such that a functioning data warehouse is facilitated.

A solution may be presented, but the basic ETL requirements are not met.

The data warehouse may not fully function.

May include or exploit new or emerging technologies to facilitate complex ETL methods.

May incorporate some innovative concepts or novel extensions to existing ETL tools.

Merges concepts from existing approaches and/or sources.

Meets basic requirements using standard tools or technologies.

Basic ETL requirements are likely not met.

2 or more data sources required advanced ETL methods.

1 or more data sources required advanced ETL methods.

3 or more data sets are included requiring applied knowledge and/or techniques.

3 or more appropriate data sources are included.

Less than 3 appropriate sources of data are included.

The ETL methodology is well documented and discussed.

The ETL methodology is appropriately documented and discussed, but may contain some minor inconsistencies or missing information.

The ETL methodology is documented, but weakly discussed. Some errors in argumentation and information may be present.

An ETL methodology is documented that is sufficient to understand the basic process undertaken.

The ETL methodology is poorly presented and/or not discussed with sufficient detail.

The ETL process is completely automated.

Three or more non-trivial case studies illustrating potential business value are presented in the report and at least 2 in the video. A solid evaluation methodology has been applied.

The ETL process is largely automated.

Three or more non-trivial case studies illustrating business value are presented in the report and at least 2 in the video. An attempt has been made to methodologically evaluate the case studies.

The ETL process is somewhat automated.

Three or more non-trivial case studies illustrating some business value are presented in the report and at least 2 in the video. An evaluation methodology may be present, but is weak.

Three or more non-trivial case studies are presented in the report and at least 2 in the video. They may lack clear business value. Little methodological consideration is present in the evaluation.

Less than three non-trivial case studies are presented in the report. The video may contain some evidence of the case studies. The evaluate lacks methodological grounding.

Case studies depending on multiple sources are presented.

At least 2 case studies depending on multiple sources are presented.

At least 1 case study depending on multiple sources is presented.

Some case studies depend on multiple, but potentially arbitrary, sources.

None of the case studies rely on multiple non-arbitrary sources of data.

The results are critically evaluated at multiple levels using appropriate academic literature to substantiate lines of argumentation.

The results are critically evaluated, but may lack some depth.

An attempt is made to critically evaluate the results, but lacks depth.

Little to no attempt is made to critically evaluate the results.

Little to no attempt is made to critically evaluate the results.

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