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Exasol Partners With Protegrity to Help Organisations Run Analytics Against Sensitive and Private Data Securely and at Scale

  • Joint Solution Ensures That Sensitive Data Is Always Protected No Matter Where It Resides – On-Premises, in the Cloud or in a Hybrid Infrastructure 

Exasol, the high-performance analytics database, announced a strategic partnership with Protegrity, a global leader in data security that protects sensitive data everywhere and future-proofs businesses as data-privacy regulations evolve. Together, Exasol and Protegrity enable organisations to run analytics against sensitive and private data securely and at scale. The new joint solution leverages the native Protegrity connector and the in-memory, MPP engine from Exasol, providing customers with advanced tokenization technology to identify, protect and analyse sensitive data faster than ever before.

Offering flexible deployment options, this powerful new solution ensures that sensitive data is always protected no matter where it resides on-premises, in the cloud or in a hybrid infrastructure.

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Commenting on the partnership, Malte Sönnichsen, Manager, Data Warehouse at EOS Technology Solutions GmbH, a joint customer of Exasol and Protegrity said, “The combination of Exasol’s in-memory analytics database and Protegrity’s data-protection capabilities has allowed us to set up a secured data platform that reduces administration overhead and future-proofs our business against evolving privacy regulations. The joint solution has enabled us to rethink our existing data warehouse architecture in support of our broader data-driven transformation goals, allowing EOS to increase our analytics performance and efficiency without exposing sensitive customer data.”

As organisations satisfy regulatory requirements such as HIPAA, PCI DSS, and GDPR, they must securely tap into large amounts of data with confidence. Many are turning to the Exasol and Protegrity solution to identify, secure and analyse sensitive data in-memory and scale linearly, unlocking new insights and hastening innovations while ensuring that no sensitive data is compromised.

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“We are thrilled to partner with Protegrity,” said Ricardo Arriaga, Director, Technology Alliances at Exasol. “Our combined solution relieves organisations from the burden of managing data security and compliance. Now, they are free to expand their analytics paradigm by incorporating all types of data – sensitive data and other generally available data – into their analytics initiatives. This, in turn, gives them deeper, more comprehensive insights, faster time to new discoveries and more rapid innovation with data.”

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While organisations are moving towards data-driven decision making, many have been hampered by concerns over managing data security and compliance requirements in their analytics workflow. This new joint solution dispels those fears by enabling them to run analytics against sensitive and private data securely and at scale.

“Too often, data-security policy enforcement stifles the pace of innovation for AI and analytics, creating time-consuming barriers that diminish businesses’ abilities to generate insights from sensitive data to drive better business decisions,” said Rick Farnell, President and CEO of Protegrity. “Our joint solution enables customers to easily identify and protect sensitive data – faster than ever before – to speed up mission-critical analytics initiatives. This allows businesses to unlock new insights from data sets containing sensitive information to accelerate innovation, while ensuring no data is compromised.”

The joint solution also allows organisations to include sensitive data in artificial intelligence (AI) and machine learning (ML) models. AI and ML projects require training algorithms with a variety of data, including sensitive data, within the enterprise. Data science teams can now access sensitive data that is anonymised in Exasol, so they can test out more scenarios than they could previously to improve model accuracy and reduce AI bias. To accelerate AI in production, the data science team can run models directly on Exasol’s in-memory engine, without exporting the data to a different system.

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