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ANALYTICS INNOVATION THE
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INTERNET OF THINGS
DEC 2015 | #1
JAN 2016 | #3
City of Chicago:
An Analytics-Driven City Cities of the future have been a prominent feature in science fiction films for years. We look at how Chicago is using data to enable reality to catch up with fantasy | 13
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Why Analytics Needs The Human Element Technology has brought us to the point where data can almost analyze itself, but is this for the best, or does data still need people? | 6
Top Analytics Trends for 2016 We take a look at what the year ahead holds for analytics practitioners | 15
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ISSUE 3
EDITOR’S LETTER Welcome to the 3rd Edition of the Analytics Innovation Magazine
The year 2015 saw the world wracked with fear of terrorism, as ISIL spread its tentacles throughout the Middle East. Massacres carried out under their banner in Paris and San Bernardino also escalated the crisis, drawing reaction from Western governments that will likely have far-reaching consequences.
issue that surveillance could identify the wrong people, often through racial profiling. This is an issue with people’s inability to accurately analyze surveillance materials, and the application of predictive analytics to surveillance technologies could increasingly see this cease to be a problem.
Defending against attacks at home is currently the most important issue facing governments, and 2016 will like see surveillance greatly increase, despite fears that this will bring about a police state. There are two major problems with increasing surveillance, neither of which are about the oft cited invasion of privacy, a nebulous concept that is to this generation what religion was to the Spanish inquisition. Firstly, people fear that governments will use it to identify people they believe simply to be ‘subversives’, but in actuality pose no existential threat to the population, only to the government’s hold on power. This is not so much a problem with invasion of privacy, as it is a lack of trust in government. Secondly, there is the
There are a number of ways in which predictive analytics can help drive better surveillance. The development of video content analysis (VCA) software is in its early stages, but new algorithms for image analysis, especially facial recognition systems, are getting much better at identifying human faces, particularly in a crowd. Predictive analytics can also look at video surveillance to identify potentially threatening human behavior in public spaces by analyzing patterns found in video footage of similar past events. However, there is a danger in turning surveillance over to technology. Later in this magazine, Alex Lane looks at how the use of deep learning will increase over the next
year, while Sam Button argues in favor of retaining the human element in data analysis. Terrorism isn’t the only major concern facing the world in 2016. The global economy remains fragile, and many fear another financial crisis. Retail banks are still suffering the effects of the last one, and in this issue Elliot Pannaman examines how analytics could help them retain their dominance in the face of an increasing number of challenger banks. Whatever the problems facing the world this year, it is likely that analytics will play a major role in tackling them. How successfully they are applied remains to be seen.
James Ovenden managing editor
As always, if you have any comment on the magazine or if you want to submit an article, please contact me at jovenden@theiegroup.com
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contents 6 | WHY ANALYTICS NEEDS THE HUMAN ELEMENT
Technology has brought us to the point where data can almost analyze itself, but is this for the best, or does data still need people? 9 | IS DATA RIGHT FOR THE CREATIVE INDUSTRIES
The romantic ideal of the lonely genius working in solitude has endured for centuries. Can data analytics put paid to this? 13 | CITY OF CHICAGO: AN ANALYTICS-DRIVEN CITY
Cities of the future have been a prominent feature in science fiction films for years. We look at how Chicago is using data to enable reality to catch up with fantasy 15 | TOP TRENDS FOR ANALYTICS PRACTITIONERS IN 2016
19 | KNOWING THE NUMBERS BEHIND YOUR MARKETING STRATEGY
Marketing is moving away from the Don Draper-type creative, and data analysts are now looking at the numbers for the most effective campaigns 23 | CAN ANALYTICS SAVE RETAIL BANKING FROM ITSELF
The reputational damage to banks that followed the financial crisis has changed the industry. Could retail banks turn to analytics to maintain their dominance? 26 | IS THE WORLD READY TO EMBRACE DEEP LEARNING?
Deep learning and machine learning both hold tremendous potential for enabling a brighter future for everyone, but do we need to exercise a degree of caution?
We take a look at what the year ahead holds for analytics practitioners
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managing editor james ovenden
| assistant editor george hill | creative director nathan wood
contributors sam button, euan hunter, david barton, olivia timson, elliot pannaman, alexander lane analytics innovation
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Sam Button Analytics Evangelist
Big Data is now, more or less, an accepted fact of business, and all companies are collecting it to some degree. Collection is, however, the easy part. Analyzing it to leverage insights that could actually improve company performance is the entire point of collecting data, yet many firms lack staff with the appropriate skill set to do this. It’s like a football team spending millions on training facilities and then signing exclusively farm animals to play for them.
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In an EY and Forbes Insights survey of 564 executives in large global enterprises, most respondents said they still lack an effective and aligned business strategy for competing in a digital, analytics enabled world. They also continue to struggle with change management issues in getting business users to adopt analytics insights. Meanwhile, according to a recent study by Forrester Research, most companies estimate they’re analyzing a mere 12% of the data they have.
Big data needs context and interpretation, and when it’s applied to scenarios such as who to give life-saving treatment to, there are often variables at play that only humans can understand
7 This inability to really embrace data analytics is often a case of not having the right people in place who understand data and where it can add value. Contrarily, many companies are also putting data on a pedestal and forgetting how important the human element is. Firms often now find themselves reliant on analytics software powered by algorithms that they have no understanding of, and look to data visualization programs to provide them with insights from this data and make decisions for them. Humans have limited capacity when it comes to making sense of large data sets, and computer driven algorithms are far better placed to deal with them, so they are largely left to their own devices and the conclusions they reach taken as gospel.
Firms often now find themselves reliant on analytics software powered by algorithms that they have no understanding of, and look to data visualization programs to provide them with insights from this data and make decisions for them This has the potential to cause a number of problems further down the line. Buying some analytics software and treating data as the be-all and end-all of decision making is not ‘having a data strategy’, in the same way that buying a fast car doesn’t make you ‘a Formula 1 driver’. Data analytics needs people in place who have a real understanding of how the models work to establish which information is useful. They are also needed in
cases that big data throws up with an ethical and moral dimension that data can’t address. Big Data needs context and interpretation, and when it’s applied to scenarios such as who to give life-saving treatment to, there are often variables at play that only humans can understand. The logical conclusion of this was illustrated in the film ‘I, Robot’, in which a robot is forced to make a decision between saving the life of a young girl and the hero, played by Will Smith. The robot opts to save Smith because the data suggests he will be easiest to save, despite his protests. A lack of human input around data is not necessarily all a company’s fault. There is a substantial difference between the number of data scientists available and the number needed by companies. McKinsey & Co estimated that there would be a shortfall of between 140,000 and 190,000 people with analytical expertise by 2018 in the United States alone, and between 1.5m managers and analysts with the skills to understand and make decisions based on the analysis of big data would be needed. While it may be difficult to find people, it is still important that companies recognize the importance of human judgment in having the last word when it comes to decision making. The human element is needed throughout the chain, both in understanding when data analytics is needed, but also when it isn’t.
The human element is needed throughout the chain, both in understanding when data analytics is needed, but also when it isn’t
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James Ovenden Managing Editor
In George Orwell’s essay Tolstoy and the Fool, in which he discusses the Russian author’s deep-seated hatred for Shakespeare, he writes that, ‘One’s first feeling is that in describing Shakespeare as a bad writer he is saying something demonstrably untrue. But this is not the case. In reality there is no kind of evidence or argument by which one can show that Shakespeare, or any other writer, is ‘good’. Nor is there any way of definitely proving that — for instance — Warwick Beeping is ‘bad’.’
Making artistic decisions based on likely popularity, however, has seen Hollywood become a graveyard for real creativity, with huge sums being spent on terrible sequels analytics innovation
10 When discussing the arts, opinion, as Orwell says, is almost entirely subjective. There is a degree of criticism that can be said to be objective, resting on whether or not a piece observes a certain set of rules around form and character, but even these are disputable. ‘Show don’t tell’, for example, has long been held up as the central cornerstone of screenwriting, but many films that are acclaimed by critics and audiences alike as ‘great works’ rely on narrators that do exactly that. Most of these rules are just mantras used by critics and people running classes to validate their career choice.
When making business decisions, it is obviously important to have a balanced, unbiased perspective that draws from a range of experience, as opposed to a human being’s relatively narrow frame of reference. The quality of a work of art, on the other hand, is dictated almost entirely by the emotional response it elicits from the viewer One of the main advantages of Big Data Analytics often cited by its disciples is that it is a means for overriding gut instinct in the boardroom. When making business decisions, it is obviously important to have a balanced, unbiased perspective that draws from a range of experience, as opposed to a human being’s relatively narrow frame of reference. The quality of a work of art, on the other hand, is dictated almost entirely by the
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emotional response it elicits from the viewer. So for those who run the business side of creative industries - movie producers, book publishers, art dealers - who need to appreciate how good a work of art is before they sell it, is Big Data really useful? The movie industry, the music industry, and the publishing industry have all already embraced analytics to varying degrees and for a range of purposes, and it is now driving substantial amounts of their decision making. Social media analytics, for one, is extremely useful to all of these industries as it enables them to gauge customer sentiment around a products release, and they can target the marketing accordingly. By analyzing past releases that may be similar, along with audience sentiment for how they are thought of now, Big Data can certainly predict whether something has the potential to be a success, and this is obviously hugely useful for allocating funds and resources.
Making artistic decisions based on
11 This is obviously a concern for artists, but it should also be a concern for the consumer. Excessive use of data in the production of artwork could see movies and music reduced even more to sanitized, formulaic, by the numbers banalities, and real creativity could fade into the oblivion.
Jonathan Franzen once said that it’s doubtful anyone with an internet connection at his workplace is writing good fiction. It could be that eventually the internet connection does not need the human to write fiction. Whether it’s good or not is another matter
likely popularity, however, has seen Hollywood become a graveyard for real creativity, with huge sums being spent on terrible sequels. Social media sentiment before a film has been released is based on things like the trailer, the cast, the story idea - Things that say nothing about the quality of the film. The popularity of previous similar films also tells you nothing about how good a film is likely to be. But It is becoming possible to produce art based purely on audience reaction. In technology writer Evgeny Morozov’s ‘To Save Everything, Click Here’, Morozov claims that Amazon has vast amounts of data collected from its Kindle devices about what part of a book people are most likely to give up reading. He speculates that using this, Amazon could build a system to write novels automatically that are tailored to readers’ tastes.
The idea that Big Data is a ‘buzzword’ no longer holds water. It has become a ubiquitous part of business strategy and is now more or-less accepted as a fact of life. Those reluctant to take it up are often tarred as Luddite - in many cases, rightly so. Jonathan Franzen once said that it’s doubtful anyone with an internet connection at his workplace is writing good fiction. It could be that eventually the internet connection does not need the human to write fiction. Whether it’s good or not is another matter.
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automation have proved prescient. Fortunately, Wells has proved similarly correct in decrying the silliness of how the film depicted the consequences of this drive. That being said, with the rise of IoT, and the decision of many of the world’s largest cities to embrace ‘open data’ schemes, it’s safe to say that we are nowhere near our capacity for automation.
Euan Hunter Industry Expert
In H.G. Wells’ heavily critical review of Fritz Lang’s dystopian classic, Metropolis, he set against the film for its central premise that automation created drudgery instead of relieving it. Wells noted that ’Masterman’s (the protagonist) watchword is ‘Efficiency,’ and you are given to understand it is a very
dreadful word, and the contrivers of this idiotic spectacle are so hopelessly ignorant of all the work that has been done upon industrial efficiency that they represent him as working his machine-minders to the point of exhaustion, so that they faint and machines explode and people are scalded to death.’ It is now almost 90 years since the release of Metropolis, and its vision of municipal planning as dominated by the drive for efficiency and
It is now almost 90 years since the release of Metropolis, and its vision of municipal planning as dominated by the drive for efficiency and automation have proved prescient According to Data.gov, 46 U.S. cities now have their own data portal. Of these, Chicago is arguably leading the way as the most aggressive in its adoption of data and analytics. On December 10, 2012, Mayor Rahm Emanuel issued Executive Order 2012-2 to codify his commitment to open data, in which he outlined
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14 the City’s commitments to gather data and make it available to the public en masse. The City’s open data portal now offers user-friendly access to more than 600 data sets, and it is always adding more according to public desire and usefulness.
Last September, Tom Schenk was was appointed Chicago’s new Chief Data Officer. Schenk previously served as the the City’s Director of Analytics and its open data portal, and he is overseeing a radical adoption of analytics across every facet of city management. In 2014, Chicago’s Department of Innovation and Technology (DoIT) also began constructing the SmartData Platform, an open-source predictive analytics platform funded with a $1,000,000 award from Bloomberg Philanthropies’ Mayors Challenge.
This analytics-driven world has already done wonders for the productivity of civil servants, to say nothing of how much they have done for city budgets
analytics innovation
Schenk has been able to use predictive analytics to leverage the data at his disposal in a number of innovative ways. For example, determining where to place bait for rats by listing which dumpsters are most likely to be overflowing. According to Schenk, this has seen the city become 20% more efficient at controlling rats. One of the most notable ways that predictive analytics has been used in Chicago is in food inspection. Under Schenk’s leadership, DoIT has collaborated with a number of City departments to put predictive analytics to good use, including the city’s Department of Public Health (CDPH). Chicago, with a population nearing 3 million, has less than three dozen inspectors to oversee the annual checking of the city’s 15,000 food establishments. In order to make the best use of these inspectors, DoIT and CPDH have collaborated to build an app for inspectors that uses predictive analytics to score food establishments on how likely they are to face a critical violation. This score was based on factors believed to correlate to violations - such as a prior history of critical violations, possession of a tobacco and/ or incidental alcohol consumption license, the length of time an establishment has been operating, as well as nearby burglaries, among others. Inspectors can then use this to make sure that they visit those food establishments that most urgently need visiting first. By using this analytics-based procedure, Chicago has been able to discover critical violations an average of seven days earlier than with the traditional inspection method. Security is another component of Chicago’s analytics drive. The city has worked closely with the Chicago Police Department to set up a mobile command team armed with cameras to manage large groups of people at outdoor events, so they can intervene if an
area becomes too crowded and a riot seems imminent. The CPD also recently joined forces with the professor of electrical engineering at Illinois Institute of Technology, Miles Wernick, to create a controversial predictive algorithm that generated a ‘heat list’ of 400 individuals that have the highest chance of committing a violent crime. By focussing on likely suspects, the police say that they can concentrate their scarce resources where they are most needed. Far from the dystopian future shown in Metropolis, this analytics-driven world has already done wonders for the productivity of civil servants, to say nothing of how much they have done for city budgets. Not only this, but it is improving the lives of its citizens as Wells believed it would, rather than seeing them crushed under the yoke of automation and efficiency.
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In 2016, there will be new challenges for analytics practitioners to deal with - and new technologies, new methods of practice, and new ideas to help
It has been a year of great upheaval in the business community, and data analytics has seen its fair share of change. Big Data may have fallen off Gartner’s hype cycle, but data science is in, and companies continue to invest heavily in both the technology and the skills needed to exploit all the information they are gathering. There have, however, been a number of setbacks. Significant data breaches saw the personal details of millions of people stolen from the likes of TalkTalk and Ashley Madison - major companies which prided themselves on the security of their systems.
David Barton Head of Analytics
In 2016, there will be new challenges for analytics practitioners to deal with - and new technologies, new methods of practice, and new ideas to help overcome them. We’ve outlined our predictions for what 2016 will bring analytics. analytics innovation
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Greater Data Democratization
The Cloud Will Become The Home Of Data
Smarter Data Governance
Data democratization has many benefits. It enables a company to take a holistic approach to data analytics that helps it become more agile, and drives faster, smarter decision-making processes across the organization. Gartner has predicted that by 2017, most business users and analysts will be able to access self-service tools that prepare data for analysis.
Cloud data and cloud analytics have been threatening to take off for several years, but progress has been stilted thus far. In 2016, we should see the cloud become the home for data and analytics, with myths that previously held companies back from adopting it, such as security, being dispelled. The cloud is no longer just a deployment model, it’s an engagement model, bringing you closer to both internal and external customers. The speed of analysis that it enables, and its scalability, mean that companies which do not adopt are likely to fall behind with their analytics. IDC’s recent NA Global Technology and Industry Research Org IT Survey found that 71% of respondents are using, planning, or researching cloud solutions, while 64% cited the importance of having subscription access to software.
For data democratization and cloud technology to evade concerns around privacy, companies will have to adopt smart and transparent data governance in 2016. In the past, many have considered governance and self-service analytics to be natural enemies, but it appears that the disparity that existed between business and technology is narrowing. Good data governance can help nurture a culture of analytics that meets the needs of the business, by giving people the peace of mind to access and use the data. There is also likely to be a new raft of regulations brought in on a national level, as governments look to take control of their citizens’ data, in the same way the EU has done by overthrowing Safe Harbour.
Central to the push for data democratization is data virtualization. Data virtualization is any approach to data management that allows an application to retrieve and manipulate data without the need for technical details about the data. According to Computerworld’s Forecast 2016 survey, firms are set to increase the amount of money they budget for virtualization projects next year, with 35% of those polled saying that they were increasing spending, and 64% saying they were beta-testing or piloting some kind of virtualization across desktop, server, storage, mobile or network.
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Spark Will Take Over
AI And Deep Learning For Business
Information of Anything
Real time tests have shown Spark to sort 100TB of data in just 23 minutes. Hadoop took 72 minutes to achieve the same results. Spark also did this using less than one tenth of the machines - 206 compared to 2100 for Hadoop. It guarantees up to 100 times faster performance for several applications, making it ideal for machine learning. Uptake to date has been exceptional, and it is still growing. According to a recent survey of 2,100 developers by Typesafe, 71% of respondents say they some experience with the framework. It has now reached more than 500 organizations of all sizes, who are committing thousands of developers and extensive resources to the open source project, and this is likely to increase further in 2016.
Given the volume of data available, and the need for ongoing, real time analysis, the use of AI techniques and processes to deal with it is expected to mushroom in coming years. There have been substantial advancements in AI and deep learning by the major tech firms over the past few years, but its use in business has been more limited. Advances in sorting speed, enabled by software like Spark, should soon see this change. IDC predicts that the global market for content analytics, discovery and cognitive systems software will reach $9.2 billion at a Compound Annual Growth Rate (CAGR) of 15% by 2019 - double what it was in 2014.
According to Gartner, by 2020 there will be 25 billion devices generating data about nearly everything imaginable. Companies that manage to make sense of this avalanche of data first will have a huge competitive edge, but firms are also increasingly having to look beyond the information produced by devices, sensors and machines. They are now looking to incorporate ALL data, such as that produced by server logs, geo location and data
Deep learning will replace much of the data analysis that was previously done by humans, which will mean people having to operate at a higher level and work on things like accelerating their data strategies. Deep learning will also play a central role in cyber security. According to an FBI official quoted in The USA Today, more than 500 million records have been stolen from US financial institutions over the past year as a result of cyber attacks, the average consolidated total cost of a data breach was $3.8 million according to IBM - up 23% on 2013. Deep learning can flag network anomalies, track user behavior, and detect zeroday malware. Anti-virus company Invincea, for one, will add deep learning-based capabilities to its end-point security product in 2016, and others are developing similar solutions.
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Olivia Timson Analytics Thought Leader
All marketers want to know how successful their campaigns are going to be before they launch them, for obvious reasons. Everyone wants to know the outcome of their actions before they embark on them as it stops them making bad decisions. For many, predictive analytics is enabling this. A new survey of 308 CMOs and business unit directors by Forbes Insights, ‘The Predictive Journey: 2015 Survey on Predictive Marketing Strategies’ found that 86% of executives with experience in predictive analytics believe the technology has delivered a positive return on investment for their business. Almost half of the organizations that were deemed ‘highly advanced’ in their use of predictive analytics credit it with increasing ROI by more than 25%.
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According to Nipul Chokshi, Senior Director of Product Marketing at Lattice - the company that sponsored the Forbes research predictive analytics needs to be integrated across the company if it’s to be a success. Chokshi noted that: ‘One of the things we’ve found in working with customers over a number of years is that there’s a distinction between being a marketing organization that uses predictive analytics technology and being a predictive analytics organization. You can make gains in one functional area, but if you go outside of your silo and bring in data from other areas of the organization, those gains multiply exponentially.’ Predictive analytics is primarily useful to marketers in that it enables them to predict what it would take to encourage a desired customer behavior. It enables them to effectively target their promotional material at particular groups that are the most likely to engage with it, and provides the knowledge of where to put the material so that it best finds its way into that group’s hands. This cuts down on the costs incurred by wasted efforts on marketing campaigns that may have previously gone unseen, or gone to groups that either hated the campaign, or the product or service that it was trying to promote. It also enables both personalization and automation, with algorithms and software making marketing decisions on what to do with material based on patterns it discovers in the data. Many firms’ websites promote products to its users individually using recommendations which run on predictive analytics. Amazon, for example, will push products in this way, and its recommendation engine accounts for 30% of its total revenue.
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Predictive analytics is primarily useful to marketers in that it enables them to predict what it would take to encourage a desired customer behavior
It is up to leadership to ensure that such a holistic approach is embraced, and much of this is having staff in place with an understanding of how to interpret the data that is collected. Executives reported to the Forbes survey that there is an intense need for the skills that can deliver predictive marketing capabilities, with analytics/predictive analytics skills sought by 68% of organizations. A further 61% said the most valuable skill in recruits was basic operations skills. This either requires training for current staff, or a recruitment process that prioritizes such knowledge. The question is whether to hire people with the operational skills and train them in marketing, or whether to train marketers in the operational skills.
According to the Forbes survey, currently just 28% of executives say that the majority of their enterprise information is readily available to them in a single integrated format. Analysis by Technavio forecast that the global predictive analytics market will grow at a Compound Annual Growth Rate (CAGR) of 25.31% during the period 20142019, and marketing is expected to see a big part of this. Over 80% of respondents to the Forbes survey said that they intend to increase spending on marketing technologies and initiatives over the next year, and predictive analytics is likely to be central to this, with 68% saying they are seeking talent with expertise in predictive or analytics.For the finance industry, cyber crime on this scale is nothing new. The main motivations for hackers are money, protest, and simply proving that they can do it. Banks fill the brief on all these fronts. The volume of attacks and the devastating consequences they can have mean that their systems
21 must always be at the cutting edge, however despite heavy investment, they have still often been found lacking. John F. Kennedy once said that ‘if anyone is crazy enough to want to kill a president of the United States, he can do it. ’Which, other than being oddly sexist, also applies to today’s cyber threat landscape. There is no way, in the current climate, that companies are able to prevent determined adversaries from getting into their systems. According to an FBI official quoted in The USA Today, more than 500 million records have been stolen from US financial institutions over the past year as a result of cyber attacks, with the average consolidated total cost of a data breach now $3.8 million according to IBM - up 23% on 2013.
Big Data Analytics could, however, provide a solution for finance companies. Brendan Hannigan, General Manager at IBM Security, has claimed that, ‘with the rate, pace and sophistication of cyber-attacks continuing to grow exponentially, security has become a Big Data problem. Real-time analytics are required as the foundation of today’s security strategy’. The International Institute of Analytics (IIA), meanwhile, has predicted that Big Data Analytics
tools are set to become the first line of defence - bringing together machine learning, text mining and ontology modelling that can provide holistic and integrated security threat prediction, detection, and deterrence and prevention programs.
One of the things we’ve found in working with customers over a number of years is that there’s a distinction between being a marketing organization that uses predictive analytics technology and being a predictive analytics organization
software and service, are getting heavy investment from VCs. FBR Capital Markets has predicted a 20% increase in ‘next-generation cybersecurity spending’ in 2015, and financial organizations looking to defend their data should be looking to adopt it if they want to stay ahead of those trying to infiltrate their systems.
Securely authenticating who is coming into the network is the primary issue facing companies’ IT security, as is the identification of anomalies that occur in the network in real time. Once in, though, hackers can spend months at a time in companies’ systems undetected, with perimeter-based defences often responsible. Government cybersecurity professionals estimate that cyber threats exist on government networks for an average of 16 days. According to the ‘Go Big Security’ report, underwritten by Splunk, 61% of government cybersecurity professionals say they could better detect a breach already in progress using Big Data and analytics, 51% say they could improve their monitoring of data streams in real time, and 49% say they could conduct a conclusive root-causes analysis following a breach. The increasing reliance on Big Data for dealing with threats is being recognized, and firms like Rapid7, a provider of security analytics analytics innovation
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Elliot Pannaman Head of Big Data
A succession of revelations about ‘dodgy’ practises in the banking industry - Libor, PPI, FOREX, HSBC’s dubious tax schemes have proven to many people what they thought they already knew about banks: They are not to be trusted.
For banks to retain their customers’ business, they have to improve relationships with them across face-to-face and digital touch points. To do so, they need to embrace data
This loss of faith comes at a particularly bad time for banks, with the rise in FinTech giving customers a range of alternatives. According to global consultancy McKinsey & Co., retail banks could see as much as 60% of their profits disappear to FinTech firms over the next decade. Major players such as Google and Apple are making a land grab into the payments sector, with banks set to lose as much as 35% of market share to the tech giants. Crowdsourcing is also providing firms with a root to funding outside bank loans, while a number of online-only banks are beginning to make waves. According to a recent Accenture report, 17% of millennials who switched banks did so to online-only models, while 31% of consumers aged 35-39 did the same.
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24 According to António Horta Osório, CEO of Lloyds Banking Group, in order for banks to regain trust, they need to focus their efforts, and they have to really start putting customers first: ‘Companies need to have a purpose and not do everything for everyone. At Lloyds we concentrated scarce resources in the UK as we thought it was where Lloyds would be most successful and because I felt we had a debt to the UK public.’
In today’s complaints-driven economy, fueled by social media and online review sites, the balance of power has shifted to consumers Engaging customers in any industry has never been harder. In today’s complaints-driven economy, fueled by social media and online review sites, the balance of power has shifted to consumers. People are also now handing over more data than ever, and they expect a more personalized experience as a tradeoff for doing so.
For banks to retain their customers’ business, they have to improve relationships with them across faceto-face and digital touch points. To do so, they need to embrace data. Retail banks can now draw on information from all stages of the customer journey. From this, they can leverage the necessary insights to provide the personalized consumer experience consumers now demand. Solid analytic frameworks around customer satisfaction and call center activity can pinpoint areas where upstream and downstream processes linked to specific customers need tightening. Banks are able to make unique, timely, and relevant offers based on available customer insight, instead of just offering what they themselves would like to sell. This both cuts costs and lowers the risk of customer dissatisfaction. If done correctly, customer analytics can also be done in real-time, so tailored product and services can be offered over the phone, or even at the cashier.
Retail banks could see as much as 60% of their profits disappear to FinTech firms over the next decade There are a number of different areas banks can apply analytics in order to drive customer satisfaction, particularly social media analysis and sentiment analysis. These are useful in that they can help get a sense of how effective sales and marketing are being with their campaigns. Banks also face the challenge of moving people to digital channels. Banks need to analyze this migration for engagement and shifts in channel to monitor customer satisfaction, and keep an eye out for re-pricing opport unities.
analytics innovation
It is not just customer retention where analytics can drive a retail bank’s success. Predictive analytics models like the FICO scoring system can analyze consumers’ credit history, loan or credit applications, and other data to assess whether the consumer will make their payments on time in the future. Not only does this save money on hiring staff to analyze the data, it invariably provides a more accurate and thorough picture. In the face of digitalization, banks must undergo a top-to-bottom reinvention to survive, or risk being left behind. To do this, they must adopt a more holistic approach to working, and this must be data led to keep up. There is good news for banks though. According to the Accenture report, customers in North America overwhelmingly trust their banks to securely manage their personal data over other industries. This is an advantage they need to exploit - and exploit ethically, without breaking regulations.
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Gaming Analytics Innovation Summit
LONDON MARCH 3 & 4 2016
Speakers Include +1 415 992 5352 sbutton@theiegroup.com www.theinnovationenterprise.com analytics innovation
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Alexander Lane International Events Director
Some of the world’s biggest tech companies have got involved. Google, Microsoft and Facebook have invested millions in research into advanced neural networks and Deep Learning
Films have long trained us to fear machine intelligence. This isn’t the movie industry’s fault, its primary function is to entertain, and the Terminator series would have been pretty banal if humans and robots had just stayed in and played Mariokart for eight hours. When Terminator came out, little was still known about Machine Learning and its implications for AI. This has really changed in the last few years, with the development of Machine Learning’s offshoot, Deep Learning, growing exponentially. Machine Learning algorithms enable a system to acquire analytics innovation
knowledge through a supervised learning experience, with a human being inputting data from which it discovers patterns that it will recognize in the future. So, in the case of images, Machine Learning will be taught what a cat looks like by having a human tell it the image it’s being shown is a cat until it can recognize it without prompting. Deep Learning takes this a step further by eliminating the need for a human to teach it what’s what. It is able to come to its own conclusions about layers of intermediate functions that need to be identified. So, to continue with the cat example, Deep Learning when let loose on a site
like Youtube, can analyze millions of videos and determine what a cat is by itself. Deep Learning began in the 1950’s with the invention of digital neural nets. These roughly simulate the way the human brain learns: When beginning a new task, a certain set of neurons will fire. You then observe the results of the task, and in subsequent trials your brain uses feedback to adjust which neurons get activated. These were largely forgotten about, with the exception of a few who persevered with their research. In 2006, Geoff Hinton - one of those
27 few - began to organize several layers of artificial neurons so that the entire system could be trained, or even train itself, to divine coherence from random inputs, in much the same way as the human brain learns.
Deep Learning is, as with all technology, neither inherently good nor bad. However, it is not just lunatics in foil hats who are worried that self aware computers could spell danger
just lunatics in foil hats who are worried that self aware computers could spell danger. The CEO and co-founder of DeepMind himself, Demis Hassabis, has acknowledged that the advanced techniques his own group is pioneering may cause AI to spiral out of human control, and could need to be constrained, while his co-founder, Shane Legg, considers a human extinction due to artificial intelligence the top threat in this century. As a result, contingencies have been put in place. DeepMind investor Elon Musk
has just spent $10 million on a study of AI dangers, and Hassabis and his co-founders put in the conditions of Google’s takeover that there be an outside board of advisors to monitor the progress of the company’s AI efforts. These are sensible. Deep Learning isn’t about self-aware machines taking over the world at the moment, but the speed at which technology advances means that it’s difficult to see five years in the future. Wherever it is that we are, however, it is likely Deep Learning will be a driving force.
Since then, some of the world’s biggest tech companies have got involved. Google, Microsoft and Facebook have invested millions in research into advanced neural networks and Deep Learning. Google has a particular advantage because it has access to so much data, and has made a land grab into AI that far outstrips the others, with last year’s acquisition of Londonbased AI outfit DeepMind for a reported $400 million standing out as a game changing move. The attraction for companies like Google is clear. Deep Learning is a self-perpetuating revenue generator. Not only does it improve the search engine’s functionality when it’s initially implemented, but every time you type a query, click on a searchgenerated link, or create a link on the web, you are training Google’s AI. The benefits are already being seen through a massive reduction in the search engine’s neural network, which has gone from requiring 1,000 computers to run to just four. Deep Learning is, as with all technology, neither inherently good nor bad. However, it is not analytics innovation
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