Immuta provides the industry’s only enterprise data management platform for artificial intelligence (AI), enabling organizations to easily operationalize data for increased access, control and visibility to drive their machine learning and artificial intelligence (AI) programs.
In the report, Gartner finds that “attention and emphasis on data management and governance around machine learning have intensified. Mature data science teams are looking for functionality around data lineage, privacy, risk management and access control.” Gartner recommends that data management and governance becomes a “point of emphasis within data science teams, and provide support through specialized technologies.”
“Through 2022, only 20 percent of organizations investing in information governance will succeed in scaling governance for digital business,” Peter Krensky, Svetlana Sicular, Jim Hare, Erick Brethenoux and Austin Kronz wrote in the report.
The Immuta platform enables algorithmic-driven organizations to quickly operationalize data for increased access, control and visibility to drive their machine learning and advanced analytics programs. The Immuta platform makes data self-serviceable, while enforcing policy controls dynamically on HDFS, Hive, Impala and Spark, as the data is queried. The results are rapidly-developed models that risk professionals and data scientists can trust.
“We’re focused on one simple thing: helping our global users accelerate their data science initiatives through better data management. The ability to deliver legal, compliant and ethical AI equals better business outcomes that consumers can trust,” said Andrew Gilman, Chief Revenue Officer, Immuta. “To us, inclusion in Gartner’s report further validates the importance of data access, policy enforcement, governance and having full control of the data used to drive machine learning and data science programs in the enterprise.”
We believe that the Gartner report is of value to all data scientists, data stewards, IT and legal professionals seeking to streamline the implementation of standardized and methodical data policies at scale and manage the risk of noncompliance or data misuse.
Gartner, Cool Vendors in Data Science and Machine Learning, Peter Krensky, Svetlana Sicular, Jim Hare, Erick Brethenoux, Austin Kronz, 11 September 2018.
Gartner does not endorse any vendor, product or service depicted in our research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.