AtScale Partners With DataRobot to Drive Collaboration Between Data Science and Business Intelligence Teams
AtScale, the leading provider of intelligent data virtualization, announced a partnership with DataRobot, the leading enterprise AI platform, to deliver a turnkey approach to predictive and descriptive data analysis. The partnership builds a bridge between data science and business intelligence to incorporate and share the same KPIs used for predictive and prescriptive business decisions.
“We are thrilled to partner with DataRobot to accelerate our go-to-market within the data science community. DataRobot’s vision for maximizing business value via AI is unparalleled, and the combination of our capabilities is another step toward democratization of predictive and prescriptive analytics”
“We are thrilled to partner with DataRobot to accelerate our go-to-market within the data science community. DataRobot’s vision for maximizing business value via AI is unparalleled, and the combination of our capabilities is another step toward democratization of predictive and prescriptive analytics,” said Christopher Lynch, executive chairman and CEO, AtScale.
Organizations globally are moving AI predictions into day-to-day business operations. The combination of DataRobot and AtScale provides a consumable enterprise metrics hub with consistent, governed KPIs and a simplified interface for automated feature creation utilizing DataRobot’s Feature Importance and Impact functionality. AtScale uniquely makes DataRobot predictions consumable to business intelligence and reporting workstreams, providing both technical and non-technical stakeholders access to predictions using the tool of their choice.
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“Our global market presence and diverse customer base continue to highlight the need for greater unity between the data science and business intelligence communities,” said Jeremy Achin, co-founder and CEO, DataRobot. “AtScale’s intelligent data virtualization adds a new business constituency in the enterprise who can incorporate AI predictions for operational decision making.”
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