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.
Hurdles to Credit Union Data Integration
The second challenge that credit unions face is in leveraging the data they’ve consolidated into their single sources of truth. Typical credit unions do not have enough data on their own to do meaningful predictive analytics.
Combine the lack of data resources with the different ways credit unions store and understand their data, and you have a significant hurdle to predictive analytics.
Just as before, it’s not always enough to have the data if you can’t use it. Especially for smaller credit unions who don’t generate enough data to perform meaningful predictive analytics, data pooling and collaborative analytics is the key to true credit union data integration.
Sharing data can be difficult too—like we mentioned before, all systems have different ways of storing and understanding data. That problem is only compounded when trying to integrate data from other credit unions’ systems.
Credit Union Collaboration
Credit union digital transformation affects the industry as much as it affects individual branches. Consequently, all credit unions have some stake in improving their data integration capabilities.
While sharing and understanding different data systems from different credit unions would be difficult for any data scientist, there’s an easy solution. Seamless collaborative analytics can be accomplished by moving to a common standard.
If credit unions find a data pooling platform that allows for robust data integration, they can perform collaborative analytics. Their information sets will be augmented by those of their peers. Credit union digital transformation is as much a cultural, collaborative transformation as it is a branch-specific one.
Unfortunately, credit union digital transformation is a time-sensitive proposition. The financial industry moves quickly, and in order to keep from going the way of the dinosaur, we need to take action.