Many organizations are facing a flood of new data types and sources coming from big data, customer channels, social media, the Internet of Things (IoT), and numerous external sources (such as partners and third-party data providers). They know they need to disrupt “business as usual” because older DM best practices—and the ways a business gets value from data—don’t necessarily manage new data assets appropriately or generate business value.
Analytics is the overarching use case for a data lake. Sometimes analytics is the sole use case, as when a lake is the core of an analytics program or an extension of a data warehouse. Other times, analytics is a significant component within an operational solution, as with marketing or supply chain data lakes. Some data lakes are built purely for self-service data exploration and discovery, which often lead to visualization or some other form of analytics. Hence, with a data lake, the path to business value usually leads through analytics.
The cloud-based data lake has clear and compelling benefits. However, it also faces many challenges in the realm of data management. This report will now discuss these challenges and offer practical solutions.
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