When you’re in the operations part of a credit union or a community bank, your data is everything. Customers usually only think of their own sensitive financial data, but you’re also tracking their spending patterns, they’re behaviors, the products that they use, their engagement with the institution – all so that you can retain them and offer even better service.
Makes sense, no? It does, but it leaves you with more data than you know what to do with.
What is a Data Warehouse?
You’ve heard of a data warehouse, and the concept makes sense – a place to store your data. You don’t even want to think about how many files, emails, and spreadsheets galore you have in your archive – it’s the reports that the analysts produce from all of that data that’s really important to advancing the business.
The problem is that a data warehouse doesn’t just happen. It needs to actually be organized in a way that makes sense, which means hiring a team to actually sort and cultivate that infrastructure. If you have the team on-hand it can work, but if not then it may be cost prohibitive.
What is a Data Lake?
You’ve also heard of a data lake, which is another intriguing option. You’ve heard that a data lake is almost like a pond that can store everything. This doesn’t just mean those spreadsheets and archived reports. It also means recordings of client phone calls, videos of client and teller interaction in branches, still images, and other pieces of data that may be less traditional.
Yes, it’s true that a data lake can store just about everything, but it doesn’t focus on categorizing things at all. Instead it keeps them in their raw format, so you can have a spreadsheet next to a movie file next to a phone recording. It’s all in there, sure, but it doesn’t really do the work of sorting through what’s what.
That raw formatting can be beneficial for AI and machine learning, where you can program the machines to actually decipher what’s in there (analyze transcripts of those phone recordings, etc.) and then work with that data. The trouble is that AI may not be part of your organization, or it may not be beneficial for the goals that you’re trying to reach.
What is a Data Lakehouse?
Instead, there’s a middle ground that may suit your needs perfectly. It’s called a data lakehouse, and it functions as a hybrid between a data lake and a data warehouse. A data lake house is beneficial for financial institutions because it can store all of that raw data in its native format, but it stores it in ways that are easily accessible and compatible with your business intelligence systems.
Why the Gemineye Data Lakehouse is an Excellent Choice
Here at Gemineye, our Data Lakehouse is a crowd-pleaser for both the technical and non-technical at your credit union or community bank. The Gemineye Data Lakehouse harnesses the best of both worlds in storage accessibility, leveraging world-class technology from Microsoft and Databricks. What’s more, it has been designed from the ground up to meet the very unique needs of credit unions and community banks – we don’t work with any others.
Interested in learning more about our innovative structure, user-friendly tools, solution-centric model? Schedule a non-salesy demo with our team.