It has been a long journey from the early days of credit union business intelligence solutions. As your credit union leverages the benefits of a data analytics program, you will need all the capabilities required to make it easy for management teams, data scientists, and analysts to store data and extract information of any size, shape, and speed across multiple platforms. When it comes to planning and budgeting for the right tools, it’s important to know what products and tools are available and the differences between them.

Two key terms you may have been hearing:  Data Warehouse and Data Lake. Both have many definitions from various business savvy techies, but let’s dig deeper to help you understand what they are and how they are different.

What is a Data Warehouse?

Credit Unions use reports, dashboards, and analytics tools to extract insights from their data, monitor member transaction activity, and to support decision making. These reports, dashboards and analytics tools are most effective and efficient when powered by data warehouses which store modeled and structured data efficiently to deliver results quickly.

The data warehouse integrates data from multiple data sources including the core, loan origination systems, online banking platforms, CRM systems, and more  into one centralized, single source of truth. The data that is uploaded each day to the data warehouse is then made available to run complex queries fast and efficiently.  Information stored in a data warehouse is historical, spanning member and transaction information that has occurred over time. The data warehouse aggregates and structures information to provide a 360-degree view of your membership including their products, services, online banking utilization, credit and debit card usage and so much more.  With a data warehouse, the credit union can instantly gain insightful information for better decision-making, leading to improved business outcomes.

What is a Data Lake?

Like a data warehouse, a data lake is also a data storage repository. However, a data lake stores raw (both structured and unstructured) data using a flat architecture for storing data. In a nutshell, a data lake is a data storage and processing system where a credit union can place internal and external data that does not fit into a typical data warehouse.  In a data lake, vast amounts of raw data in its native form is stored.  The data lake retains ALL data and keeps it in its unrefined state that is then transformed and defined only when ready to consume.  Since the data lake stores data of all kinds, this allows highly skilled analysts and data scientists to explore the raw data in new ways, helping with projects that have diversified data. The data lake allows for complex algorithms to identify patterns and trends that will power real-time decision-making analytics and business opportunities.

Now that you are more familiar with the key basic differences between a data warehouse and data lake, the next step is to determine your organization’s needs and objectives to identify which one is right for the credit union. Stay tuned for our next article that outlines the pros and cons of a data lake.

The Knowlton Group can ensure your business goals are met with a data strategy assessment and a business intelligence roadmap.  To learn more about how data analytics can help drive your business practices, contact us today.




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  1. […] 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 […]

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