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Are Data Lakes a Better Way to Analyze Member Data? Understanding the Benefits & Problems with Data Lakes

BY BREWSTER KNOWLTON

What sets a data lake apart from a data warehouse? And why might one be a better data analytics game changer for your credit union over another? A data expert gives it to CUB readers straight regarding both the upsides to and the  drawbacks of data lakes.

As more credit unions seek to up their competitive game through data and analytics, the debate between data warehouses and data lakes continues. While solution providers and analysts line up on both sides of the discussion, understanding the advantages and drawbacks of a data lake can help your credit union determine if it’s truly the best fit for your needs.

To clear any confusion, let’s recap the main distinctions between a data warehouse and a data lake from our previous  article. Both serve as data repositories; however, the data warehouse integrates primarily structured  data from multiple data sources into a centralized, single source of truth. That information is then made available to  run complex queries fast and efficiently. The data lake, on the other hand, offers credit unions the ability to store vast amounts of raw and unstructured data in its native form until it is ready for use. At that time, it is then transformed  for analytics, reporting and visualization.

The Data Lake Upsides
Boosts Competitive Advantage: As a tool, the data lake is helping to redefine the way credit unions analyze heaps of  unstructured data for business decision making. With the tremendous increase in competition, the need to analyze and utilize member data from all sources will be crucial. The data lake facilitates quick decision-making, advanced  predictive analytics and agile data-backed determinations.

Converges Data Sources: Data lakes can help resolve the nagging problem of accessibility and data integration.  Credit unions can start to pull together massive volumes of data from various sources for analytical purposes or for  undetermined future use storage. Rather than having dozens of independently managed collections of data, these  sources can be combined into the unmanaged data lake. 


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