Bluntly speaking, the banking industry is falling behind when it comes to leveraging and monetizing data strategically. With all the customer, transaction, credit, delivery channel and market data that banks hold, it’s ironic to hear executives lament, “We don’t have our arms around data yet, and we’re not using it to drive the business.”
With massive improvements in cloud technology and the analytic tools that will be embedded in this environment, bankers are beginning to wake up to the fact that their first-generation approach to data analytics is fast becoming outdated.
Traditionally, bank CIOs have sought the security of “controlling data” by storing and managing it via an on-premise data warehouse, reflecting a bias that “if I can see the equipment storing the data, we’re safe.” While these first-generation efforts to organize data and deploy dashboards into business areas have been helpful, progress from the business leaders’ perspective has been slow, inconsistent and resource-intensive. And, a ton of disparate downloads, spreadsheets and ad hoc reports still lurk in the halls of every bank.
More and more, bank tech executives are giving up on the cycle of buying hardware and software, cobbling data together in-house and migrating to cloud-based data analytics platforms. Bank CIOs are seeing early value in “data-as-a-service” analytics platforms that can integrate, manage and analyze large amounts of data in a more scalable and flexible way than on-premises infrastructure.
As financial institutions work to transform into “smarter banks,” the data analytics journey between cloud and on-premises environments is akin to flying versus driving on a trip from Los Angeles to Las Vegas. While both approaches can get travelers to the destination, one will get them there much faster but cost more. Cloud is built for the massive real-time processing and rapidly adapting scale that would be very costly to replicate in-house.
“Aha!” a CIO might exclaim. “What if the plane crashes, meaning my cloud data is hacked?” While no environment is safe from information security risks, statistics show the chances of a data “car accident” are much higher than a plane crash. Protecting disparate data at scale in an on-premises environment can be extremely challenging, and threats like ransomware are more prevalent than ever.
Importantly, the “speed limit” of an in-house data journey can be slow while the cloud environment has characteristics that will likely allow the “flight” to pick up velocity over time. In the cloud environment, banks can access all the integrated tools and capabilities created by a widespread community of developers in that ecosystem.
CIOs are beginning to access tools that allow for the rapid integration of proprietary and third-party data, provide sophisticated data visualization, and layer on powerful machine learning and AI processing engines. In an on-premises data environment, all the chores of integrating data and leveraging new tools would fall on the resource-starved bank. That road trip may take a while.
In recent years, banks have begun more earnest efforts to leverage cloud platforms such as Snowflake, Databricks and Azure Analysis Services to start a new, more sophisticated data analytics journey. Each of these tools has different strengths and capabilities, but all are opening the eyes of bank executives to what a data-driven smarter bank could look like in the future.
It’s great that banks have started to organize their data with first-generation data warehouses, but it’s time for them to start the journey to a more powerful and fast-evolving analytics-in-the cloud environment. Here are key steps on this journey that banks can take:
In the future world of banking, only the smartest will survive. It’s time for bank leadership to buy the ticket and take a well-planned and adventurous flight into the clouds of new data analytics platforms. Store the tray tables and buckle up!
Darren Gleason is a consultant with Cornerstone Advisors. Follow him on LinkedIn.