Ultimately, data lakes enable enterprises to swiftly input variegated data types and make it easier to process and exploit. "Because all the data is stored as unaltered, queries provide a more accurate report with a greater depth of information reported about the data," says Irvine. Data lakes provide higher levels of information to executive management, revealing correlations between data that they may have overlooked, allowing them to make more intelligent decisions, Irvine notes.
Securing only the lake
"Data lakes can act as repositories of log file information, user information, and behavioral and transactional information about the user," says Steve Jones, Strategy Director, Big Data & Analytics, Capgemini. Enterprises can use massive amounts of data to establish a robust baseline of expected user behavior. With a fine grain model of normal behavior, data lakes can quickly and precisely detect anomalous behavior, intrusions, IP theft, and data leakage.
This data lake approach avoids the costs and performance lags of the other approach, which are associated with enriching every single piece of data with the right metadata and with validating every query and hit on every piece of information against the security policy, explains Jones.
While the level of security detail in the other data lake approach is laudable, says Jones, it is probably too expensive for most enterprises. "The raw data that data lakes can store is, however, useful in securing a cloud approach by performing threat, intrusion, and anomalous behavior analysis," says Jones.
CSOs need to know what they are trying to achieve. "Is it fine-grained security in the defense sector or simply a better way to create a 360-degree view of internal and external threats," asks Jones? "Understanding the real business challenge will help them undertake the right approach," says Jones.
For many, the simpler solution is the right one.
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