Move Over Data Warehouses, Here Come Data Lakes 

The role of data analytics is quickly expanding in the public sector. How the Government will get their arms around the vast amounts of data, however, continues to be a challenge. While data-driven decision making is the future, the Government needs reliable ways to access the data and turn it into information.

So, what does the future of Government analytics look like? The answer may reside in the shift from data warehouses to data lakes. A data lake is a storage repository that holds large amounts of raw, natural, unstructured data. The concept places all the collected data in one large lake, allowing Government agencies to determine how much data to pull out in a specified filter and structure. This differs from a data warehouse, where data is often structured in files or folders, and accessed through customized reports.

Data lakes allow agencies to plan for scalability, reduce managing costs, and remove silos. In a data lake environment, analysts dictate the types of analysis because static data warehouses, predetermined filters/fields, or data models do not limit them. When data is raw or undefined, analysts can make their own connections to drive the analysis.

Data lakes also prevent datasets from becoming outdated or disconnected. While some data elements may not be useful for today’s analytics, they could be in the future. Data lakes help create an environment that allows the Government to constantly connect data from different time periods and different sources to solve current and future issues.

As the amount of gathered data continues to grow and change, the Government must reexamine how to best structure its analytical platforms to meet the current challenges facing our country. Data lakes improve scalability, remove silos, and have the flexibility to meet unforeseen future challenges. As a result, they could offer a unique alternative as the future of Government analytics evolves.