Data lakes have turned into data swamps. Metadata initiatives have derailed. As a result, data discovery and retrieval are ongoing, head-banging challenges. Data preparation also remains a weighty anchor on data scientists’ efforts. Meanwhile, new regulations carrying expensive penalties, such as the European Union’s (EU) General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) are magnifying the risks already inherent in data management chaos. Obviously, new tactics are needed because doing more of the same won’t solve any of these problems.
Indeed, companies are desperately seeking to organize their data in new and meaningful ways, and for good reason. A Gartner report, "Data catalogs are the new black in data management and analytics," says that “through 2019, 80% of data lakes will not include effective metadata management capabilities, making them inefficient. The report also states, “By 2019, data and analytics organizations that provide agile, curated internal and external datasets for a range of content authors will realize twice the business benefits of those that do not.” The gap between is significant.
In the universal search for a workable solution, data catalogs are quickly rising to the top of the list. This report from Io-Tahoe outlines some of the major considerations to help enterprises find the best AI-driven data catalogs that will work best for their organizations.
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