According to Gartner, the data lake’s present position in the “Trough of Disillusionment” came after a “Peak of Inflated Expectations,” and we all saw that happen—the boundless excitement and the grasping of its potential, combined with the complex architecture to make it all come to be. But things have changed. The Cloud has created new possibilities. Data teams are sinking in their teeth. The stages of enlightenment and productivity are coming or, arguably, they are officially here.
Organizations capture so much data—both structured and unstructured—and in a perfect world, this data can deliver unprecedented insights. We can build and create with more confidence and speed, spending our time on more impactful work with this data on our side.
The early adopters were the first generation of data lake users. They explored the possibilities and learned many lessons. Now, we are on the brink of data lakes 2.0. Organizations that didn't take action in the first rounds can jump straight into the second, but if they don't have the Machine Learning (ML) and Data Ops to surround their data lakes, they may still have a problem. To take advantage of the data in your data lake, you need Data Operations in place which give you the ability to rapidly move data, frame it, analyze it, and deploy models created out of its insights into production.