Myth-Busting: Data & Analytics is a Big Bank Game

June 2nd, 2017

data.png

Consumers today are increasingly reliant on having data at their fingertips to make decisions and, most importantly, to simplify their lives. Companies like Amazon and Starbucks have set the gold-star standard when it comes to using analytics to understand customer preferences, as well as simplifying the purchase experience. Now those expectations are carrying into every aspect of our life, and several industries, including banking, find themselves playing catch up.

Banks of all sizes are struggling to meet these expectations, but mid-tier Banks (defined as $10-$50 billion in total assets) in particular find themselves at an inflection point with regard to data and analytics. Data and analytics are central not only to building a more loyal customer base, but also to creating greater efficiency to compete more effectively. However, mid-tier banks have the advantage of being more nimble relative to their larger competitors, allowing them to create better customer experiences and greater efficiency—even with smaller technology budgets.

For any financial institution, there are four levels of data and analytics maturity:

Limited. Many of these banks still rely heavily on intuition to make decisions. This is due primarily to lack of leadership involvement or support; lack of technology spend on architecture, talent or tools; and overreliance on ineffective legacy systems. These institutions need to get beyond those daily challenges to realize strategic benefits that will grow revenue, cut costs, mitigate risk and improve customer experiences.

Recognized importance. This level is likely the most difficult achievement, and will take more time than other transitions. These institutions have successfully garnered executive support, established a shared vision across the organization, and analytics use cases are taking shape with some small victories. But don’t bite off more than you can chew. The most successful banks start with small, focused use cases and build on what they have learned.

More advanced. For most institutions, achieving this level would be sufficient for the long-term. It takes approximately six to eight years to get here and at this level, the volume, variety and velocity of data begin to “explode” within the organization. New roles are required to manage this process, there is typically a centralized data warehouse in place, and analytics are a core part of strategic planning and budget processes. And notably, there is a strong understanding and receptiveness for data and analytics from the front lines all the way to the board of directors.

Culturally ingrained. While everyone will strive to achieve this, few will get to this level—and that’s okay. The benchmarks are set by a cross section of both banks, and non-bank powerhouses like Amazon and Nordstrom. At this level, the institution is well known for their analytics prowess. Predictive analytics are firmly in place, they are looking at using unstructured data, exploring more advanced analytics techniques (i.e. AI, IoT, blockchain), and are heavily focused on insight generation. Impressively, much of their analytics are real-time and alerting is in place to help decision makers better interact and please customers and prospects.

So how do you successfully progress forward on your data and analytics maturity journey? By focusing on the five data and analytics essentials:

  • Strategic Support and Adoption. For analytics to progress, it needs to be part of the fabric of the institution’s vision, strategic planning and day-to-day activity for decision makers.
  • Information Architecture and Governance. The most significant decisions are made here, but it is also where most institutions make mistakes. It requires a long-term view, significant investment over time, and both management and technical talent to execute properly.
  • Data and Analytics Capabilities. This area encompasses both the volume and types of data sources, the scope of the data requirements, the integration necessary to properly turn the data into analysis, and the toolsets used to organize, report and deliver the analysis.
  • Data needs to be both timely and available to succeed. Naturally, both the timeliness and accessibility should increase as the organization progresses on its analytics journey.
  • Organization and Cooperation. While often a roadblock, collaboration and cooperation for data and analytics across the organization is critical to success.

To be sure, the data and analytics journey is a long-term process. Levels can’t be skipped and progress must be “learned and earned.” So is this worth it? Is the ROI for these capabilities going to be meaningful? The answer is a resounding “yes.” Mid-tier banks that have achieved even the “recognized importance” level are seeing as much as a 20 percent improvement to efficiency ratios and 15 percent improvement in return on assets, respectively, as compared to less mature banks over the last three-year period.

Rob Rubin, director at Novantas, is the co-author of this piece.

ggoetzmann

Gordon Goetzmann is a managing director for Novantus.