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Data Digest: Boosting Data Analytics with Secure Data, Best in-Memory Databases, and Building Efficient Teams

Securing data allows data scientists to focus on what they do best, plus choosing the best in-memory database for your environment and building efficient teams.

How to Secure Your Most Sensitive Data and Boost Data Analytics
(Source: Tech Republic)

Only 5 percent of data collected is highly sensitive, but that 5 percent can get you in big trouble if you do not properly secure it. It can be hard for data scientists to both secure and analyze data coming in, so adopting tools such as masking and encryption for real-time data streams will make your data more secure and provide more time for your data scientists to analyze.

Choosing the Best In-Memory Database
(Source: Tech Target)

There are many in-memory database systems on the market right now, so it can be hard to tell which one would work best for your enterprise before buying it. This article provides a thought-out list of each major option and for what enterprises each is best suited.

Building the Most Efficient Team with Data Scientists and Data Engineers
(Source: Computer World)

Data scientists need a foundation on which to do their most productive work, but not all of them can build that foundation. That is where the data engineer steps in. Hiring data engineers to make the ground-level database for the data scientists to analyze is a crucial separation of tasks.  Learn how to get the best data engineers.

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Quint Turner is an editorial intern at TDWI and an undergraduate English student at Skidmore College. Follow his blog at

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