Self-service modern analytics on the GovCloud

Lockheed Martin is a data-driven company with a massive variety and volume of data. To extract the most value from its information assets, the company is constantly exploring ways to enable effective self-service scenarios. Ramesh Krishnan and Steve Morgan discuss Lockheed Martin's journey into modern analytics and explore its analytics platform focused on leveraging AWS GovCloud.

Information management at Lockheed Martin, the world’s largest government contractor, comes with a variety of challenges. The company is in a highly regulated industry, it has a very rigorous security process, and it has a massive variety and volume of data. To extract the most value from its information assets, the company is constantly exploring ways to enable effective self-service scenarios. Ramesh Krishnan and Steve Morgan discuss Lockheed Martin’s journey into modern analytics and explore its analytics platform focused on leveraging AWS GovCloud. Building and deploying a data lake to AWS GovCloud has allowed Lockheed Martin to rapidly prototype and realize value in several cases. However, it has brought with it a few challenges. Internal customers range from operations analysts and human resources managers to seasoned data scientists. Each user comes with their own set of goals, but they all share a common need: to have an analytics environment that accelerates their time to value without needing to involve multiple layers of the IT organization. Ramesh and Steve explain how the enterprise data lake team is working to enable as many self-service scenarios as possible and are continuing to develop best practices for ingesting data in to the lake and automatically creating schemas to be queried in Presto. The goal is to have seamless integration between the company’s various analytics tools so that the end user has no need to understand the magic happening on the backend. Ramesh and Steve cover the benefits the company has seen, like faster query times and the ability to analyze multiple datasets in ways that were not previously possible, as well as some challenges the team has faced, such as the need for automating cleanup of user-created AWS resources and creating the appropriate safeguards to protect users from themselves.