Cambridge Semantics to Present at Gartner Data & Analytics Summit 2019

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Founder and CTO Sean Martin to Lead Discussion on Essential Value of Enterprise Data Fabrics for Transformational Analytics and Machine Learning Projects

Cambridge Semantics, the leading provider of modern data discovery, and integration software for enterprise data fabrics, today announced its participation at the Gartner Data & Analytics Summit 2019, March 18 to 21 in Orlando, Fla.

Sean Martin, Founder, and Chief Technology Officer will present, “Data Discovery and Integration in the Modern Data Fabric” on Thursday, March 21 at 10 a.m. Martin will provide an overview of data fabrics - the modern data management architecture driving digital transformation at enterprise speed and scale. He will also highlight how companies are using data discovery and integration in their data fabrics to exploit enterprise data for transformational analytic and machine learning projects. Successful data fabrics derive value from existing investments by applying a data discovery and integration layer to map all enterprise data in its original business context so that users can find and blend data from diverse siloed sources into analytics-ready data products on an on-demand basis.

“Organizations today continue to struggle with the daunting challenge of practically delivering highly integrated mixtures of their data assets that are needed to achieve maximum business value,” said Sean Martin, CTO, and founder of Cambridge Semantics. “The Gartner Data & Analytics Summit serves as the perfect venue to meet with industry peers to collaborate and exchange the latest knowledge and insights. We also look forward to discussing the ground-breaking capabilities of our newest release of Anzo®, our leading data discovery and integration platform.”

In his presentation, Martin will illustrate that most organizations have scores of data silos including data lakes and warehouses, and many have also implemented data catalogs to help locate individual data assets. However, without the final blending layer -- provided by Anzo -- weaving them all together into a data fabric, and then harmonizing and interlinking useful data resource combinations from within all these individual structured tables and stores of unstructured data including documents, emails, and logs, is far too resource-intensive to be practical, and often results in disappointment. He will talk about how only a modern discovery and integration layer based on semantic graph models truly allows companies to transform the way their enterprises can focus on data relationships and combine business meaning when interknitting their data from many sources to produce composite data products that solve specific problems for the business.

Cambridge Semantics will be exhibiting its award-winning product, Anzo®, which completes an organization’s enterprise data fabric architecture with a discovery and integration layer. Anzo uses a highly scalable in-memory semantic graph layer to create blended data products from multiple sources and retains business meaning and context to address specific business problems.

The Company will share several new advancements in key features, including “Data Layers” and “GraphMarts,” which are designed to enable IT to provide business users on-demand access to large numbers of sophisticated, blended data products, each containing far richer data, with more precision, leading to transformative organizational insights. These innovations deliver a generational shift over current data lake, data management and data analytics offerings.

For more information on Anzo®, visit:

About Cambridge Semantics
Cambridge Semantics Inc., The Smart Data Company®, is a modern data management and enterprise analytics software company that offers Anzo, a data discovery and integration layer to connect and bring meaning to all enterprise data.

Cambridge Semantics is based in Boston, Massachusetts.

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Media Contact:
Lora Wilson/Valerie Christopherson
Global Results Communications for Cambridge Semantics
+1 (949) 608-0276

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