Don’t fear data: Seven things to know about data analytics

For some community bank executives, advanced data analytics are more like a minefield than a gold mine. Compared to many other industries, the banking sector is behind the curve when it comes to understanding, applying and leveraging the value derived from Big Data. A study by Celent, the technology research and consulting firm, found that only 20 percent of bank executives interviewed wished to be data driven. Yet, for those institutions eager to explore the benefits, 69 percent believed data analytics can improve sales results and customer relationships.

So why are some banks slow to transform data into an asset? Likely because they have not seen their data truly simplified and visualized.

There is a correlation between asset size and a focus on advanced analytics. Community banks have long demonstrated the competitive advantage of market presence and deeper relationships. Imagine if the community banks coupled their natural competitive advantages with advanced analytics.

Data analytics for many community banks can be the key to unlocking new revenue streams and greater customer loyalty. Banks that are building and cultivating sophisticated, data-driven cultures are able to engage with customers more effectively, drive new business and mitigate risk. Succeeding with data analytics starts with a solid plan and knowing the right questions to ask.

If your community bank is ready to go down the data analytics road, consider these seven questions as you begin the process:

1. What is your goal? What are your community bank’s top priorities for analytics? Is it to improve sales, customer service, cost reduction, regulatory compliance and risk management or to gain a competitive edge?

2. Do you have buy-in? For any new initiative to work well, all departments within your community bank need to communicate and work together. Does your bank’s management team see the value of data and the benefits it delivers in cost savings, efficiency and cross-selling capabilities? Buy-in will require fortitude and integration into the strategic plan, culture and incentive structure.

3. Is your analytics program ready? Does your community bank have the technological capabilities to access, refine and warehouse its data? What data analytic tools will it need to prepare and present data in an actionable way that guides decision-making?

4. Do you have in-house talent: Does your community bank have any semblance of a data analytics team that can properly collect, prepare, manage and analyze the data on a daily basis? Does it have a data scientist on staff? Can it hire, train and retain this staff?

5. Can your data drive action? Does your community bank have a system that can turn data into useful and actionable insights for its sales and marketing officers?

6. Can you measure and track ROI? Does your community bank have a system in place to determine which marketing campaigns and segmented audiences generate the greatest response and most promising opportunities for ROI measurement? What gets measured gets done.

7. How will you safeguard data? Can your community bank take the right steps to keep the data safe or partner with a provider that has SSAE 16 certification and meets data security compliance regulations?  You want to make sure sensitive, private customer information is not compromised and exposed.

Community banks that understand the value of analytics will continue to invest in their customers’ data to gain new insights and discoveries. Analytics can empower your bank to effectively segment its customers to identify opportunities to determine best pricing and target new products and services to the right customer at the right time. The key is to transform data into information; identify segments of opportunity; and take, measure and improve actions to develop recurring best practices.

Reggie Beason is Sales Director of Financial Institution Solutions at Saggezza. He provides a wealth of experience in the retail banking industry and a deep understanding of the software and data analytics used by financial institutions.