Home / Banking Strategies / The definitive data dilemma: Community banks and the struggle to bag big benefits

The definitive data dilemma: Community banks and the struggle to bag big benefits


Everyone has heard comments such as these:

“Data is your greatest asset.”

“If you don’t learn how to leverage your data, you’ll get left behind.”

“Your number one technology challenge is to get control over your data.”

But is it really true for community banks? If so, why are so few doing anything about it?

Data update: The four kinds community banks have

This may sound like an obvious question, but do community banks actually have useful data that they’re not already leveraging? Emphatically, yes! Even if it doesn’t reside in their own systems, they have access to many kinds of data. Banks’ data could drive marketing, sales, decision-making, risk management and product design.

  • Customer data is the most obvious kind that others don’t have the same access to. And the deeper a bank drills into a customer’s business, the richer the data. Some is structured (e.g. transaction data); some unstructured (e.g. financials, asset records). But it is also potentially valuable.
  • Bank financial data when brought together gives a stronger basis for risk management and financial decision making.
  • Market data gives insights into local markets and the prospective customers’ industries
  • Economic data: global, national and regional/local trend data. This should help with both marketing insights and portfolio risk management.

Data strata: What matters most

Community banks that plan to continue their current business model don’t need to do anything about their data. But they will also fall further and further behind. They miss out on opportunities such as these:

  • More focused marketing approaches that use data about markets and companies. These will allow better use of different media and drive messaging to speak to real problems faced by banks’ customers.
  • Insight-driven sales: using the right media to approach the right people at companies most likely to buy bank services.
  • Management reporting and analysis: allowing executives and senior managers to gain a complete picture of bank operations and financials.
  • Deeper understanding of risk (credit, market and operational): insights that will help with risk-based decision-making.
  • Design and delivery of new products: meeting emerging customer needs, based on insights into customer businesses and industries.

So yes, data is very important for community banks. But it also seems hard for them to get their arms around it. Why?

Data difficulties, defined

I see several challenges to overcome before banks can truly take advantage of their data.

  • Without a strategic plan for the bank’s development and even transformation over the coming years, it is hard to see how data will help the bank. It represents a means to an end, not an end in itself.
  • Much of the bank’s data is locked up in its core banking system. As banks review core vendor contracts, they should look for increasing openness of systems. They should also demand flexibility of access to their data.
  • Most community banks do not have in-house expertise to organize and utilize their data. Most of their outsource providers are generally not data specialists. They need specialist firms that know data as well as the opportunities and challenges of community banks.
  • Bank boards are reluctant to invest in technology to the degree necessary for data management, analysis and reporting. This is in part because bank executives struggle to make the business case. However, now is the time to invest—while loan books are full and revenue streams steady.

From data marts to data smarts: What it takes

Several steps must be taken to harness the full value of a community bank’s data:

  1. Create a well-articulated business strategy. This will determine what a bank needs from its data, and indeed all its technology.
  2. Build a technology strategy that responds to each point of the business strategy. Such a strategy will almost certainly be data-centric. It will include an architecture that will provide access to all the bank’s data, as well as relevant data from outside the bank. Elements may include:
    1. A data lake, which is a collection of all the different data sources, without focusing on format and structure. This data may mix structured and unstructured data.
    2. A data integration platform that will validate, cleanse and transform data. This will make the data usable by transactional systems and the data warehouse environment.
    3. One or more data warehouses to provide a general structure to all related pieces of data.
    4. Data marts and analytical views of the company’s data. These provide a window from the perspective of particular business functions (e.g. marketing, sales, finance, risk).
    5. Visualization tools that work with the functional views of data. They will allow building of reports, dashboards, interactive web pages and insights to realize the data’s full value.
  3. Bring in data experts to build out and execute on the detailed steps. It doesn’t make sense for a community bank to build a data core competency. This is a good area to outsource. But due diligence, strong contracts and rigorous vendor management are essential. Data is indeed one of a bank’s most critical assets and must be protected accordingly.
  4. Get help from your data experts to design new data-driven customer products. These may also use artificial intelligence to analyze the data. These products will differentiate the bank from its competition (both bank and non-bank). For example, utilize your customer’s data to build out cash management products that will allow a bank to meet the needs of larger commercial customers.
  5. Continue to maintain, manage and leverage data as more becomes available. Create new views and visualizations as market conditions and customer needs change. It’s ideal to outsource this to the same firm that builds the data stack in the first place.


Simply put: Bank CEOs and CIOs should get more from their data. They can transform their business and in so doing, transform data of a different kind that reflects a bolstered bottom line.

Look for future posts to drill deeper into some of these topics.

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Graham Seel, a 30 year banking veteran, runs BankTech Consulting. An expert in commercial banking who provides strategic insight and innovation consulting, Seel works as a fractional Customer Success Executive to FinTech firms, facilitating their partnership with banks. Listen to Graham discuss Millennials and community banking on the BAI Banking Strategies podcast.