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All issuesVolume 304, Issue 2IT NewsBig Data

7 Key Security Criteria For Data Tools: A Buyer's Guide

insideBIGDATA, Saturday, July 15,2023

How many specialized SaaS tools does your company have for moving, analyzing, and profiling data across departments? If you're like most digital companies today, the answer is 'more and more.'

In the last decade, the decentralization of data competencies and increased adoption of cloud-based deployment models has resulted in a wider-than-ever range of data tools on the market, and a greater-than-ever need to use them. They include:

  • Extract, transform, load (ETL) and extract, load, transform (ELT) platforms
  • Information platforms as a service (iPaaS)
  • Data warehousing solutions
  • Data visualization solutions
  • ML/AI engines that ingest data models and build AI engineering models on top of them
  • Using such tools to build a composable data architecture is key to staying agile in the fast-evolving world of digital business, but it raises two important questions about security: How can we ensure that vendors of SaaS data solutions are keeping our data and our customers' data safe? What security mechanisms do these tools offer to help us as users prevent the leakage of sensitive data?

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