Data lakes are coming on strong as a modern and practical way of managing the large volumes and broad range of data types and sources that enterprises are facing today. TDWI sees data lakes managing diverse data successfully for business-driven use cases, such as omnichannel marketing, multimodule ERP, the digital supply chain, and data warehouses extended for business analytics. Yet, even in business-driven examples like these, user organizations still haven’t achieved full business value and return on investment from their data lakes.
What’s inhibiting the business value of data lakes? The problem is not the data lake itself; a data lake is a very simple design pattern that is surprisingly easy to deploy and populate. The problem is that some users fail to give the lake and its users proper tooling in the middle layer of the architecture, which Data-as-a-Service (DaaS) can ably address. DaaS is a critical success factor because it can cope with some of the lake’s biggest challenges. First, the data of a lake is mostly raw source data that business end users have trouble understanding. Second, most lakes are deployed atop Hadoop, which is notoriously poor with business-friendly metadata and data cataloging. Third, most lakes involve environments of multiple data platforms, which make it difficult to get a unified view of available data. Finally, a data lake is a compliance infraction just waiting to happen without best practices and tool automation for data governance.
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