It’s no good to be a dinosaur in the financial sector. Not only are dinosaurs notoriously temperamental, but they can’t type. Oh, and they’re extinct. If branches don’t want to go the way of the dinosaur, then a little credit union digital transformation is their best hope.
(Hint: credit unions aren’t the only industry affected by digital transformation and the emerging primacy of data.)
While digital transformation is certainly the goal, it can’t just organically happen. Credit union digital transformation is a strategic process that incorporates several approaches, from digital engagement to data integration. In this blog, we’ll talk about the challenges of credit union data integration and collaborative analytics strategies.
Tying Together Data Sources
Typical credit unions have somewhere around six to eight data sources. Some have more. While having the data is certainly nice, it’s not much good to just sit on it.
Core and ancillary systems produce data at prodigious rates. These streams of data are all separate, too. Siloed data streams are great when you need to understand only the data produced by one source. However, individual sources of data have a nasty habit of not producing a clear, complete, actionable picture.
Making matters worse is that each system stores its data differently. If you want to perform data analysis on any of your credit union members, you have to check in on each system and pull different data sets from them.
This lack of robust credit union data integration hampers solid, actionable analytics. The first challenge for credit unions then is reconciling individual data streams into one single source of truth.