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Big Data: Big Opportunity In Banking… Or Big B.S.?

Big Data. Everyone in retail banking is talking about it, but no one really seems sure what it is. At the very least, confusion and controversy surrounds how to utilize big data in practical applications, and the magnitude of opportunity is represents. Will big data transform the future of financial marketing? Or will banks and credit unions get crushed by a crippling tidal wave of information?

For years, financial institutions have leveraged internal insight they have on their customers to manage risk and fraud, as well as to improve product development, and (of course) marketing and customer communications. Today, however, new and enhanced technologies coupled with the availability of a vast pool of structured- and unstructured external “Big Data” allows for real time multichannel decisioning that can save money and increase revenues. At least that’s the theory.

The question is: Is now the time to embark upon a Big Data strategy?

What is Big Data?

“There were 5 exabytes of information created by the entire world between the dawn of civilization and 2003. Now that same amount is created every two days.”
— Eric Schmidt, CEO/Google

Much of the confusion around big data stems from a misunderstanding about the definition itself. Rather than any single characteristic clearly dominating the view of big data as part of a recent poll by IBM, respondents were divided in their views on whether big data is best described by today’s greater volume of data, the new types of data and analysis, or the emerging requirements for more real-time information analysis. One in 12 dismissed it as nothing more than the latest buzzword.

While there are several definitions of big data, the most common reference focuses on data that reflects added Volume (terabytes, records, transactions), additional Variety (internal, external, behavioral, social), and increased Velocity (near- or real-time assimilation).

Understanding the “Three Vs of Big Data” is important, since understanding the value of data being created today allows a bank to understand their businesses, customers, channels and the marketplace dynamics, including new sales and service opportunities.

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“Analytics, customer-centricity, and multichannel technologies are major trends across the market,” says Bart Narter, SVP of the analyst firm Celent, and co-author of a recent report, Core Banking Solutions for Midsize Banks: A Global Perspective.

“Banks increasingly want a core system that utilizes data analytics in order to provide a more complete view of the customer, which then allows for better customer-bank communication,” Narter writes.

In the financial services industry, while there is a great deal of discussion around big data, many banks are just beginning to consolidate and utilize many of the internal data elements at their disposal, such as debit and credit transactions, purchase histories, channel usage, communication preferences, loyalty behavior, etc.

In the context of big data, banks would expand their current structured insights to include the gathering and analysis of data from sources including web click streams, social interactions (Facebook and Twitter), geo-locational insight, and other similar new wells of information.

What Are the Potential Benefits of Big Data?

The areas within the banking industry that present the strongest near-term opportunities for tangible performance improvement include risk management/fraud detection and improved customer communication and loyalty.

Risk Management/Fraud Detection – With the financial crisis of 2009, risk profiling and fraud detection became top priorities. Expanding the use of alternative channel insight and increasing the velocity of data capture, the use of data beyond the institution’s firewalls provides an enhanced snapshot of household finances and spending behaviors.

For instance, with the addition of alternative device transactions and the ability to track changes in behavior beyond what is occurring with a client’s credit account, banks can isolate new fraud or risk triggers. This added insight and enhanced algorithms provides advantages in effectively reducing risk, managing credit exposure and allowing for timely intervention where necessary.

“We have been looking at how technologies around big data can help us make data analytics faster and more agile.”
— Greg Nichelsen,
ING Direct Australia

Targeting – The ability to better understand consumers, seamlessly matching “right-time” offers to a customer’s or prospect’s needs, allows a financial institution to optimize the management of profitable, long-term customer relationships. The addition of a vast amount of relatively unstructured online insight provides an enhanced view to this end, potentially improving both effectiveness and efficiency of marketing efforts.

Layering upon this added insight, the ability to leverage precisely timed geo-locational and event-based targeting at the point of sale (either at the bank or an offer delivered to a smartphone by a merchant partner) provides both marketing and payments advantages that few industries can match. The impact can break through the wall of promotional noise, leading to improved revenues along with an enhanced customer experience.

For example, if a customer has a habit of going to a certain area for shopping or lunch on a regular basis, analyzing the data could provide the foundation for offers that are highly personalized even to the type of food the customer prefers, with a knowledge as to the likelihood of offer acceptance. Adding device specific capabilities, the offer could be delivered by SMS at the most logical time for decisioning. This is the same type of model used by Amazon and other retailers.

Another example is the combination of Zillow functionality with an augmented reality home lending app that could allow a customer to use their smartphone to find values of houses in a neighborhood and seamlessly apply for a loan using a handheld device. The insight captured during this shopping process combined with credit bureau information would allow a bank to better target messages around a potential pre-approved loan.

“I think the biggest benefit is that it takes you an enormous leap forward in analytic capabilities.”
— Greg Nichelsen,
ING Direct Australia

From a foundational level, big data could provide the insights to develop segmentation strategies based on transactional, behavioral and even social profiles. This would allow the organization to provide a highly personalized, consistent experience regardless of the channel selected by the customer, eliminating traditional silos that create a challenge today.

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More Data = Better Decisions?

As with the claims surrounding the value of CRM in the 90s and social media more recently, many of the stated benefits of big data for banks are — at the present time — little more than vendor fantasies. There is a glaring lack of concrete case studies, and those that are cited are based on broad, often incorrect interpretations of what “big data” is. In searching for evidence of big data’s potential, you’ll find that much has been written about its application in theory, but specific success stories are scarce indeed.

“The idea that you need to store, mine and analyze every scrap of the customer data they collect is not practical.”
— Penny Crossman, American Banker

It is indisputable that using data more effectively can lead to substantial benefits, including monetary improvements in risk management and marketing, but the jury is still out as to whether these benefits provide an adequate ROI compared to the investment required.

Key Question: Is more data actually better? Does it lead to better decisions? Or just muddle the waters?

Best Data Strategy May Be to Start Small

As opposed to jumping into big data with both feet, it may be best to test the waters first, prioritizing investments and using a test-and-learn mentality to determine how fast — and deep — to go.

According to new research by Capco, 62% of banks believe that managing and analyzing big data is important to their success. However, only 29% say they are currently extracting enough commercial value from data.

These challenges are all too familiar to bankers. Saddled with legacy, siloed technology platforms, they lack analytical expertise and structure, and can only support “traditional” approaches to data utilization. According to Capco’s study, this is why most financial institutions will find they’re woefully unprepared for a big data transformation.

Learn to Walk Before You Run Away With Big Data

According to a recent Novantas report, most banks are best served by using a building block approach to big data, only layering in additional complexity (and investment) after establishing a strong data foundation. This philosophy seems especially relevant for those banks that don’t currently have a unified view of their customers; many banks have created data silos with no linkage between checking, small business, retail, mortgage and credit card customers.

The illustration below shows how Novantas believes banks can build an enhanced customer profile.

Doing Nothing May Not Be an Option

While moving cautiously in the world of big data is a viable strategy, doing nothing is not an option. With many institutions building a big data strategy, the ability to pick off your best customers from a current competitor is an increasing threat. Beyond that, there are many alternative providers that are building pseudo-banking strategies, collecting vast amounts of insight that can be used against you in the future. Google, PayPal, Amazon and other organizations are building a wealth of data on purchasing patterns.

While the data within your firewalls provides a distinct competitive advantage, the unstructured insight available online, through mobile channels and through social media are equally valuable. In the new world, knowing that a purchase was made may not be enough. Knowing what was purchased may be the incremental difference needed to create loyalty.

As with most investments, a financial institution needs to determine if the added volume, variety and velocity of data brings measurably better results. As Richards J. Heuer, Jr. argued in the Psychology of Intelligence Analysis (1999), the primary failures of analysis are less due to insufficient data than to flawed thinking. To succeed analytically, we must invest a great deal more of our resources in training people to think effectively and we must equip them with tools that support that effort.

The question, though, is that most banks are already dealing with data overload and aren’t very good at consolidating and using the data they have now. What are the odds we’ll get better with more being piled on?

More data doesn’t fix bad analytics.

Maybe, many financial organizations should focus on perfecting the ‘small data” first.

Jim MarousJim Marous is co-publisher of The Financial Brand and publisher of the Digital Banking Report, a subscription-based publication that provides deep insights into the digitization of banking, with over 150 reports in the digital archive available to subscribers. You can follow Jim on Twitter and LinkedIn, or visit his professional website.

All content © 2017 by The Financial Brand and may not be reproduced by any means without permission.

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  1. Nice overview. I especially like this: Knowing what was purchased may be the incremental difference needed to create loyalty.

  2. Not BS – Big Data Yields ROI in thousands – just don’t start big data projects in IT – start with Big Questions

    Great article Jim. Big Data provides shortest path to answering Big Questions. Executives and business managers are liberated to ask any question and should spend efforts asking, “What three questions, if answered, would most change the way I do business?” – Big Data solutions the cost to ask and act on these questions affordable. Design tip – design the actionable questions first, then map technical solution – results generate ROI in thousands.
    (Should mention that government regulations are going to require more big data nimbleness.)
    You can always drop Big Data in CxC Customer Experience Matrix template to track customer life cycle, plot customer product, service and communications strategy and measure all results.

  3. Jim – outstanding post. I wholeheartedly agree with starting small and getting really good at that before moving to bigger data, you might even find out that you don’t need to go any bigger. In my opinion, the key to data is identifying the information that is strategically important to you. This also enables you to identify information that is not of strategic importance today. The ultimate key with big data is getting the right filter on it so that you get information of value.

    Key #2, do something with it. I still see far too many institutions that are actually doing a pretty decent job of gathering/filtering/identifying data and turning it into information but not taking action with it. Positive results are dependent upon action.

  4. Jim – Great overview for traditional banks. Highlights the fact that banks really can’t think creatively or transformatively. Banks have sat on some of the richest sources of data for decades yet cannot consider anything more that risk management or upsell/cross sell use cases. While they stay in that mindset, they will never understand the true power of big data. Leaves the door wide open for more nimble, agile thinkers to enter and dominate the space 🙂

  5. Nice post and very interesting thoughts. I agree that starting small and testing different strategies seems the best way for banks to go. Clearly, building loyalty is key, and has been a long time coming. Personally, I hope that banks can be successful in using big data to personalize offerings to individuals and offer value added to the relationship. Seems like a dream. Hope it comes true!

  6. Jim,

    I wrote on this subject one month ago, almost to the day. Great minds think alike.

    I would add profitability to the Novantas chart, though. Not just as a performance measurement tool, but an assessment tool on who deserves the best service, and what demographic group/industry group deserves our customer acquisition attention.

    ~ Jeff

  7. Very thorough and well written article with helpful comments, too. I agree with MR Hoffman that Big Data is not BS – as proven by MarkLogic customers that achieved a substantial ROI (many zeros) with their MarkLogic implementation.

    It’s the marriage of the structured and unstructured data that is both within and external to the bank’s firewalls where true insights are achieved. Some of our clients tried using a traditional, relational database which is not built to handle the 80% of data that is unstructured so it’s not surprising the projects all failed. Others were unsuccessful because they tried open source options which did not have the enterprise, ACID compliance required.

    The competitive edge in business today is not money; it’s having the right tools and using them effectively.

  8. Darren Oddie says:

    Great concise and honest overview. Much more enjoyable to read than most articles I’ve read on the subject.

    I agree with Scott that most incumbent banks are saddled with unimaginative thinking when it comes to big data and marketing. Having said that though, customer inertia is the winning card for big banks.

    Effective big data analytics can disseminate customer insight throughout the organization and inform marketers on the full customer lifecycle, however it requires organizational readiness for holistic consumer engagement. Yep, you have to have a dialogue with your customers, i.e. don’t use Twitter just for servicing complaints and don’t use Facebook for press releases only.

    Big data analytics can deliver insight that highlights the behaviour change and predictive trends of consumers, as they are ahead of the corporate technology adoption curve. Mobile and tablets are driving new consumer decision-making paths to purchase. The omni-channel engagement of consumers applies equally to banks and retailers.

    Until banking customers lose their switching inertia and become engaged in banking then big data will remain a ‘nice to have’ for big bank marketing. Big data will probably be used more for risk management and operational efficiencies where traditional metrics still apply. Banking start-ups and smaller banks will be the ones better engaging their customer base through the use of big data analytics and insight.

    I wrote on how marketers should approach the subject of big data so that they aren’t overwhelmed by all the BS that is out there.


  9. The focus should be on data and and how to use it period. Big data is just hype. Move on and focus on the task at hand…using data.

    The uses of data in bank risk management is getting better and better all the time and it is an innovative (yes Scott I said innovative) use of data in banking.

    Also, there is nothing wrong in using it for cross sell or upsell either. If you do not use data for marketing you should. You need to master the basics before you can progress to the really fun parts of advanced uses of data.

    Marketers have been using data for decades. As the volume and type of data has increased so has the sophistication of the tools and some of these very sophisticated tools have also been around for decades.

    For some additional fun reading:






  10. Thanks Jim, this is definitely the most sensible discussion of big data I’ve seen so far.

    I especially like the More Data=Better Decisions? section. Red flags go up whenever I hear “data-driven strategy” — strategy is almost by definition decision making with incomplete data. I think the most common misconception about big data is that it will allow us to make decisions with COMPLETE information– ie, to make the perfect decision. That will never happen. There will always be uncertainty.

    (I never replied to your question on twitter a while ago about Sandy and a cashless future- the answer is that most of our money only exists on core banking systems already anyway, and that you should put your servers in the mountains.)

  11. Great job here, Jim. The back and forth in the comments is great, too.

    Here’s what struck me; In the graphic showing what execs think Big Data is, the top vote-getter got 18% of the votes.

    That tells me that no one really knows (or understands) what Big Data is and isn’t. If there’s no consensus on (or common understanding of) what it is, how can anybody conclude what impact it will have?

  12. Thanks for all of the great comments on this post. Having just spoke in London, I find that the topic of ‘big data’ is a great buzzword but, as many of the readers have pointed out, is both misunderstood and somewhat misguided given the massive number of priorities marketers and bankers face on both sides of the Atlantic and elsewhere. Everyone at the Future of Retail Banking conference in the UK believed that collection of more data is paramount to success, but that we need to ‘mind the store’ and work with data we already have before expanding scope.

    Another very interesting perspective that became interrelated at the conference was that our desire to know more about our customers is the foundation for an improved customer experience. The customer is used to the experience at Amazon, Netflix, Pandora and even LinkedIn. Until we can illustrate our ‘smarts’, the customer will be underserved. In addition, the ability to gather greater insights and to serve the customer better will be undermined if we lose the battle for the mobile wallet/payments. If the customer decides to download a payment app from Google or another third party player, we lose transactional data that is powerful.

    Bottom line – Start small but move fast. There are financials and non-financials looking over our collective shoulders.

    My Future of Retail Banking presentation on Big Data and customer engagement is located at: http://slidesha.re/WwnSqo

  13. You are right, not doing anything cannot be a strategy. Big Data can reduce waste, increase client engagement and some tools are being deployed in fraud detection. A bank that fears Big Data will lose out on these opportunities and more.


  14. Interested to know about how big data could play a role for wholesale banking industry in identifying risks with existing and new customer credits? What sources are available externally and what information could be leverages for this analysis?
    Any suggestions?

  15. Ilya Geller says:

    So far, for the past 70 years, the only one technology was used – SQL. It’s external to data technology, which helps to catch patterns and distill statistics from the data based on queries, from outside, externally.
    This technology emanates from External Relations theory of Analytic Philosophy: students of Moore, Russell and Wittgenstein established IBM and everybody followed their path.
    However, there is Internal Relations Theory, which follow Bradley and Poincare. In this Theory patterns and statistics are found from inside structured data.
    What is the structured data? I discovered and patented how to structure any data: Language has its own Internal parsing, indexing and statistics. For instance, there are two sentences:

    a) ‘Sam!’
    b) ‘A loud ringing of one of the bells was followed by the appearance of a
    smart chambermaid in the upper sleeping gallery, who, after tapping at
    one of the doors, and receiving a request from within, called over the
    balustrades -‘Sam!’.’

    Evidently, that the ‘Sam’ has different importance into both sentences, in regard to extra information in both. This distinction is reflected as the phrases, which contain ‘Sam’, weights: the first has 1, the second – 0.08; the greater weight signifies stronger emotional ‘acuteness’.
    First you need to parse obtaining phrases from clauses, restoring omitted words, for sentences and paragraphs.
    Next, you calculate Internal statistics, weights; where the weight refers to the frequency that a phrase occurs in relation to other phrases.
    After that data is indexed by common dictionary, like Webster, and annotated by subtexts.
    This is a small sample of the structured data:
    this – signify – : 333333
    both – are – once : 333333
    confusion – signify – : 333321
    speaking – done – once : 333112
    speaking – was – both : 333109
    place – is – in : 250000
    To see the validity of technology – pick up any sentence.

    Do you have a pencil?

    Being structured information will search for users based on their profiles of structured data or can be easily found by its only meaning. Each and every user can get only specifically tailored for him information: there is no any privacy issue, nobody ever will know what the user got and read. (No spam!)

    Therefore, all what is based on SQL is the bubble: all possibilities for SQL were investigated, nothing new can even theoretically come.

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