How to build your organization’s data analytic proficiency; Part One.

The challenge facing most community financial institutions is not a lack of data. Banks and credit unions have access to an abundance of information; every day an institution will send millions of data points through expensive networks and applications in order to process, transmit, and maintain daily operations. But having this massive volume of data does not automatically correlate to having valuable insights. The value is found in being able to easily turn this cache of data into actionable insights that drive the institution’s ability to serve its community, streamline operations and ultimately, compete with larger institutions and non-bank competitors.

Multi-national institutions are investing in data science teams while simultaneously creating chatbots for their websites, using artificial intelligence to customize user interactions, and applying machine learning to efficiently complete manual, time-consuming tasks. Smaller financial institutions, by comparison, have found it difficult to move past the basic descriptive data analytics of canned ad hoc reports. These institutions face the ever-present obstacles of already compressed margins coupled with a shortage of resources and available talent to fill the gaps.

But contrary to common belief, the absolute first thing a community bank should do is dive into the deep end of the data pool head-first. Banks must deploy advanced data analytics to maximize the value of information. More insight means better decisions, which translates directly to better service for customers and a better bottom line for the institution.

Planning at this stage is crucial. Banks and credit unions need to have a holistic view of existing processes and outputs, and how current staff manages these functions. Once you know where you are, you’ll have a better idea of where you’re going. Part of the planning process will be to look at the usefulness of building out staff to meet project goals, or outsourcing through a consultant group or third-party software. If a community bank is able to attract, manage and retain a data specialist, then that position should be a part of the executive team so there is complete understanding of the institution’s strategic position and operating environment.

Consumer demand has moved beyond the branch – circumventing one of community financial institution’s core values: human touch and the customer experience. In this modern era, technological gaps become glaring roadblocks. For a community bank or credit union to deliver on the customer service promise, digital transformation using valuable, actionable insight, will be a priority.

So, the next question is how do we make-up lost ground? Follow along in our series as we illustrate the steps to find value in the rise of the data economy.