Post by Bob Meara
Data analytics is not a new pursuit. SAS, for example, has been offering solutions since its inception in 1976. But owing to the inherent complexity of advanced data analytics platforms, experience with data analytics has been the domain of only the largest organizations. However, the last several years have witnessed an explosion in applications for data analytics, especially in the area of customer analytics. With the growth in applications came conveniently pre-configured software solutions that were fine-tuned for a bevy of specific applications. The combination of product evolution, specialized analytics-savvy consultants, professional services firms, and cloud computing, has brought advanced analytics swiftly down market. Now, even small banks and credit unions can foray into customer analytics with a comparatively small investment and without a legion of data scientists on staff. But are they?
Well, that depends on what you mean by data analytics. Celent recently surveyed about 100 North American banks and credit unions to understand the state of analytics adoption and the drivers behind its growth. In our resulting report, “Customer Analytics Adoption in Banking: When Management Doesn’t Lead” (September 2014), we noted that about half of the financial institutions in the sample had some experience with data analytics. However most of these efforts might be considered rudimentary, such as customer profitability or web analytics applications. A third of the respondents to the survey had experience with social media sentiment monitoring, an example of advanced analytics, but inexpensively available in the cloud and easily used by non-data scientists. In contrast, usage of predictive analytics applications is far less common. Just one in five financial institutions demonstrated experience with next-best-action analytics, and one in ten showed an understanding of customer lifetime value.
What gives? If customer analytics holds such great promise, why aren’t more banks and credit unions deriving value from its use? I think there are at least two reasons. First, we are seeing an immature state of data analytics at most financial institutions. Second, and perhaps more important, there appears to be a lack of interest by leadership at the top of these financial institutions in driving data-driven strategies.
Is Your Organization Data-Driven?
Using data to make decisions is not the same as being data-driven. An organization doesn’t become “data-driven” simply by installing an advanced data analytics application. So, what does it really mean to be a data-driven organization? Celent asserts that data-driven organizations use analytics extensively and systematically to influence and execute strategy. Practically, this takes many forms, but it begins with attitude. Organizations start by deciding to value data, develop confidence in its validity, and make decisions based upon data even when doing so is uncomfortable. In other words, being data-driven amounts to having faith in the efficacy of data and acting accordingly. It means walking the walk, not just talking the talk.
How many banks are true data-driven organizations? Not many, we find. It’s probably fair to say that the concept of an organization being data-driven isn’t a binary thing. Instead of a “yes” or “no” answer, perhaps the question is best posed, “How data-driven is your institution?” and additionally; “How data-driven would your organization be if it were up to you?” The survey found that just 29% of responding financial institutions thought their organizations were highly data-driven. Nearly 90% of that same sample said their organizations would be highly data-driven if it were up to them. In other words, they wished for it. Clearly, we think that the industry wants to be data-driven, but doesn’t think it’s there yet.
Lack of Leadership
Intuitively, this suggests a leadership problem, but does the data support this conclusion? It does. We cross-tabbed the survey results by respondent roles and found significant differences in attitudes surrounding data analytics. Specifically, responses to the question “How data-driven would your organization be if it were up to you?” varied dramatically by role. It turns out that all respondents in IT/IS roles wished their organizations were highly data-driven – or would be if it were up to them. In contrast, respondents in strategy or innovation roles as well as those in marketing, showed somewhat less passion for being a data-driven organization. Perhaps a surprise, coming in last in support of data driven strategies were those in executive management; compared to those they lead, this group was the least desiring for their organizations to be data-driven.
Although surveys aren’t the final word on any topic, the results do suggest a leadership problem, which if addressed, would go a long way towards better serving customers through skilful use of data analytics. As banks better understand the merits of being data-driven, we think that financial institution leadership will ultimately lead the march to supporting data-driven business strategies, particularly those focused on customer analytics.