Sisense Improves Position on the Ability to Execute Axis in the Visionaries Quadrant Compared to the Previous Year

Sisense, disrupting the BI market by simplifying business analytics for complex data, announced that it has been recognized as a Visionary in the Gartner Magic Quadrant for Analytics and Business Intelligence Platforms report for 2019.

Building on the momentum from last year, Sisense received recognition for its completeness of vision, and was positioned among the highest Visionary vendors on the ‘ability to execute’ axis. This year’s results was attributable in part to what Gartner Peer Insights reviewers have written about Sisense.

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“In our experience, when Sisense promises something – in the sales process, in the marketing materials, or on the phone – it gets delivered.” (VP Safety – Air Canada)

“Sisense works hard to deliver maximum value to its clients, and over the past year this has resulted in a substantial and growing footprint in the enterprise space,” said Sisense Chief Executive Officer Amir Orad. “We believe inclusion in this year’s report highlighted our product growth, and enterprise-specific product innovations, which we feel poise us for major growth in the year ahead. We are proud that we have been able to show growth in product innovation, and growth in our client base, while also continuing to deliver the highest quality of service and support to the companies using our product.”

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The acknowledgement from Gartner follows, what we believe is, a blockbuster year for Sisense. Sisense closed an $80 million Series E round in 2018, bringing total investment in the company to around $200 million. Sisense opened a European headquarters based in London, and introduced a number of major product enhancements. Sisense also launched Sisense Hunch, a new class of big data analytics called a Data Cognition Engine, which puts the power of big data to the edge of the Internet of Things (IoT), effectively turning sensors, phones, and wearables into supercomputers. Sisense Hunch reduces terabytes of data to a neural network of a few megabytes that can be queried to find patterns and outliers.

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