A Snarketing post by Ron Shevlin, Director of Research at Cornerstone Advisors
Aite Senior Analyst Ron Shevlin knows a thing or two about data analytics. He’s crunched numbers in the banking industry for 20 years, and he says financial institutions should purge the term “big data” from management discussions. Here Shevlin outlines three reasons financial marketers should dodge the “big data” trap.
By Ron Shevlin, Senior Analyst with Aite Group
Claims of the potential impact of “big data” are everywhere. One technology vendor writes:
“The value in big data is in being able to aggregate new information sources into a unified platform to gain new insights and knowledge. With [our big data technology], financial services firms can: reduce risk by acquiring real-time visibility into transactions and complying with current and future regulations; cut costs by simplifying IT infrastructure and quickly building big data applications; and increase revenue by spotting trends before competitors and developing new products that meet customer demands.”
Reduce risks, cut costs, simplify IT and increase revenue? These claims are typical among many of big data’s proponents. What’s missing from most (if not all) of these claims is any logical or empirical proof of results. The promise of big data is nothing more than that — just a promise.
There are three really good reasons why smart financial services executives should avoid getting caught up in this “big data” hype:
- The real drivers of marketing effectiveness
- The real challenges of customer data integration
- The real cause of the data scientist shortage
1. The Real Drivers of Marketing Effectiveness
Yes, it’s true that analyzing data can produce insights that can help produce improved marketing results. And I’ll concede that incorporating new types of data, and in more timely ways, could improve the quality of insights.
But… the analysis process — call it “Sensing,” the ability to identify consumer needs and intentions based on their behaviors and actions — at best only partly determines marketing effectiveness. Another determinant of marketing success is “Responding” — delivering appropriate advice, guidance, and offers at the right time and in the right channels.
Financial services firms can analyze data — big data, new data, whatever data — until they’re blue in the face to generate insights about customer needs and market trends. But without the ability to reach customers and prospects with appropriate messages, marketing ROI will not improve.
In other words, identifying needs in real-time makes absolutely no difference without the ability to get corresponding messages to the appropriate customers in real-time. If financial institutions are going to “increase revenue by spotting trends before competitors” (as promised by so many big data proponents), then they need real-time methods of contact through mobile devices and social media. And yet how many financial institutions use snail mail as their only official mode of communication?
But you don’t hear the big data proponents talking about this, do you?
2. The Real Challenges of Customer Data Integration
Similar to the “Sense” and “Respond” concept in the previous section, there are two mechanisms involved in data analysis: putting data in, and getting data out. The former is (relatively) easy, the latter is really hard.
One of the top 10 US banks went through a “customer data integration” effort a number of years ago, integrating data from a number of business lines in an attempt to create a 360-degree view of customers. The bank achieved that objective by building a technology infrastructure pulling data from across the organization, but in doing, but they lacked the ability for users to easily access the data.
As a result, marketers in the various lines of business were forced to submit data requests to IT for campaign sizing and segmentation. These requests often take weeks (if not months) to complete, hampering the bank’s ability to rapidly respond to changing conditions and customer demand in the market.
The real challenge to customer data is not incorporating new types of data, but in making what is captured useful. Who cares if you have petabtyes of data if no one can access it and make any sense from it?
Maybe that’s where you think “data scientists” will come in. Don’t hold your breath.
3. The Real Cause of the Data Scientist Shortage
There is a common misconception among many big data proponents that a new organizational role — so called “Data Scientists” — will emerge alongside the boom in big data analytics. What is a data scientist? According to eConsultancy, a data scientist…
1) May be involved in the design and development of systems that collect and process large amounts of data using tools like Hadoop and programming languages like R
2) Need to have a deep understanding of statistics and probability
3) Are capable of designing and testing predictive models
4) Provide the greatest value by answering the questions “Where are we likely going?” and “What would we need to do to go somewhere else?”
5) Will realistically need to acquire a high level of domain expertise
That sounds a lot like the database marketing analytics professionals that exist in many financial institutions today. eConsultancy tries to distinguish between data scientists and analytics experts by saying that analytics experts:
1) Analyze smaller or more specific data sets typically collected by third-parties
2) Primarily use existing services and apps for data visualizations
3) Do not require a formal scientific background
4) Answer questions like “Where have we been?” and “Where are we today?”
5) Should have some domain expertise
The analytics folks I’ve worked with easily meet all the criteria in the definition of a “data scientist” (with the possible exception of having expertise in Hadoop). They’re hardly limited to small(er) or specific data sets, or data collected from third-party tools, or using “existing” services. Most of the analytics experts I’ve worked with have a PhD in statistics, which should meet most people’s definition of a “formal scientific background.” And the analytics experts I’ve worked are generally focused on the question “What should we do?” not “Where have we been?”
The point here is that the people who are most likely to help your organization analyze big data already exist within many large financial institutions, and within their marketing services vendors. They are folks with advanced degrees in statistics, and have significant experience in market research and statistical modeling.
And the reason why there aren’t already hordes of data analytics people out there today? Because statistics is really hard, and most people don’t have any interest or desire in learning advanced statistical techniques. Crunching numbers just isn’t sexy.
The data scientist shortage isn’t going to be addressed if people don’t want to acquire the basic skill sets required for the job.
The Big Data Management Fad
Management fads come and go. In the 30 years I’ve been working, I’ve witnessed at least five: Client/Server, Reengineering, Knowledge Management, Digital Business, and Social Media.
A common trait among all management fads is that they’re poorly (or loosely) defined. Management fads become fads, in part, specifically because they’re so loosely defined; it allows for multiple interpretations, giving many folks the impression that what they’re doing falls under the banner of the new fad. As a consequence, at the peak of any particular management fad, I see at least half of a firm’s new initiatives for a given year described in terms of that fad.
2013 is the year of the big data fad. Is big data totally useless? No. Like all the other fads before it, there are elements of value. In financial services, there are plenty of opportunities to make smarter business decisions by using new and different types of data.
But it will take years — yearrrssss! — for companies to develop and integrate “big data” competencies. The claims of big data ROI bandied around are unattainable in all but the most exceptional of cases.
The antidote for any fad’s hype is to focus on business problems and business value. Instead of asking “What can big data do for us?,” financial services executives should focus on asking “What are the most important business issues we need to address?” and “What can we do to alleviate those issues and capitalize on opportunities?”
Smart execs should ban abstract fads like “big data” from all management conversations, and force executives to talk in terms of concrete problems and specific technology solutions.