When determining if your company needs a data lake, keep in mind the types of data you’re working with, what you want to do with the data, the complexity of your data acquisition process, and your strategy for data management and governance, as well as the tools and skill sets that exist in your organization.

Companies today are also starting to look at the value of data lakes through a different lens—a data lake isn’t only about storing full-fidelity data. It’s also about users gaining a deeper understanding of business situations because they have more context than ever before, allowing them to accelerate analytics experiments.

Developed primarily to handle large volumes of big data, companies can typically move raw data via batch and/or stream into a data lake without transforming it. Enterprises rely on data lakes in key ways to help:

• Lower the total cost of ownership
• Simplify data management
• Prepare to incorporate artificial intelligence and machine learning 
• Speed up analytics
• Improve security and governance