SAN FRANCISCO, Aug. 2, 2018 /PRNewswire/ — Panoply, a leading provider of Data Warehousing solutions, announced that the company has been identified as a Sample Vendor in the Gartner 2018 Hype Cycle for Data Science and Machine Learning report. Panoply was named in the Machine Learning-Enabled Data Management category.

“We’ve built Panoply with machine learning at the forefront of our self-optimizing data warehouse solution,” says Panoply Co-Founder and CEO Yaniv Leven. “We believe being named by Gartner as a Sample Vendor reinforces Panoply’s value to our customers.”

To Gartner, machine learning-enabled data management will offer benefits in the following areas:

• “Data integration — To support automation in order to simplify the integration development process, by recommending or even automating repetitive integration flow.”                            
• “MDM — MDM (master data management) solution vendors will increasingly focus on offering ML-driven configuration and optimization of record-matching and merging algorithms as a part of their information quality and semantics capabilities.”
• “Data quality and MDM — ML will be used to extend profiling, cleansing, linking, identifying and semantically reconciling master data in different data sources, to create and maintain ‘golden records.'”
• “DBMS (database management systems) — In addition to enhancing cost-based query optimization, ML will be used to automate many of the current manual management operations performed by database administrators, including the management of storage, indexes and partitions, and database tuning.”

We feel that Gartner’s described benefits of a machine-learning data warehouse are in place at multiple levels within Panoply. Our warehouse uses machine learning and artificial intelligence to automate day-to-day warehouse management and deliver lighting-fast results via self-optimizing queries and more.

Putting Panoply’s machine learning to use,Bob Vermulen at Shinesty says, “An attractive feature of Panoply was the way the warehouse learns and optimizes our queries in an automated fashion. We had no performance issues and the more times you re-run a query, the faster it comes back. That means I don’t have to spend time creating indexes and trying to optimize the queries myself.”

For more information, subscribers may see Gartner’s full report.

Gartner, Hype Cycle for Data Science and Machine Learning, 2018, Peter Krensky, Jim Hare, 23 July 2018.

Gartner does not endorse any vendor, product or service depicted in its 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.

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SOURCE Panoply