By now, I am certain you are aware of just how much data surrounds us today. Data now comes from every direction, and every place. From your smartphone to your smart appliances, anything connected to the internet has potential to provide companies with invaluable information. Information that (hopefully) will be used to make your experience—as a product or service-buying consumer—better. And, banks are absolutely no different in this regard in how they can use such information to improve their products, customer experience, and financial outcomes. However, achieving such success with data hinges on a company’s ability to quickly access the data, derive meaningful conclusions, and act promptly.

I remember the first time I was introduced to bank data. We had just finished building a (SQL-based) data warehouse, and I was a part of a financial institution’s team responsible for the effort. I was astonished at the breadth and the depth of the data tables we were able to identify and import. Admittedly, due to the overwhelming possibilities of what all this data could do, we had to start small and focus on small wins–like calculating and trending net customer growth.

The point of this data warehouse endeavor early on was what it led to an ability to not only develop more sophisticated and robust analyses but also quickly access data. Whether you have a centralized or decentralized team of analysts, they must all be equipped to easily and skillfully pull data from a central point and have the confidence in its quality.

To derive meaningful conclusions, product experts or experienced bankers must be involved in the analysis the entire way. Not to influence the results, but rather to ensure findings are reasonable. Over time, the more education and support the data team receives, the more advanced their analysts will become. The nirvana in the evolution is when your team of analysts becomes capable of conducting their own proactive data projects and has the ability to derive conclusions and make recommendations before a manager even asks!

Finally, information derived from data analyses is worthless if it cannot be acted upon or acted upon quickly. Many data analyses likely entail some marketing element and the value must be known. It could be conducting a market basket analysis to understand your bank’s share of wallet or loyalty within your customer base. It’s almost certain you will identify a gap that could possibly be filled with a simple marketing campaign. Or, you could be studying the profitability of a set of customers to identify any missed opportunities. This can lead to new pricing decisions or expansion initiatives. The relationship between the data analytics team and the Marketing and Sales departments must be efficiently strong.

Over time, your data projects will gain complexity and sophistication. They can include credit and price modeling, channel network diagrams, risk decision trees, and regressions. You can sync up with marketing programs to automate customer emails. You can also leverage your data to use in setting performance goals. While analytics can be a very powerful asset for any company, management must have access to the data and be quick to put it to good use, and move like Jagger.