Enterprise organizations were quick to realize that capturing data would be essential to creating business value. As a result, they rushed to create massive data lakes — only to discover that it often became difficult to extract value from the data when they couldn’t make sense of it. In many cases, organizations found that business users and data scientists were spending most of their time sorting through billions of fields represented in their data lakes — far too much data to manage manually.
Waterline Data recognized that the only way to solve this problem was through intelligent automation. The company’s automated data discovery platform crawls data assets, creates what it calls data fingerprints on a field-by-field basis, and automatically tags the data accordingly. This auto-tagging process becomes the basis for machine learning models that organizations refine over time as they interact with the data and manually add or adjust tags.
The goal, the company explains, is to enable organizations and their users to interact with the data in the terms and semantics that are meaningful to them. Working with both structured and semi-structured data, the company offers data catalog and governance tools that sit on top of its discovery platform to enable business users to “shop for data” and then pull it into their business intelligence or visualize tools of choice. Moreover, the company’s use of its fingerprinting approach enables organizations to continually update metadata and improve data tagging without requiring the platform to re-crawl unchanged data.
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