Written by Tamr
Earlier this week, Gartner named Tamr as one of its “Cool Vendors in Data Integration and Data Quality, 2015.” For any startup, especially one in a fiercely competitive, high-stakes sector like ours, we believe this kind of distinction helps affirm both progress to date as well as future direction.
Gartner, in this report, is actually sharing a vision for the market that neatly aligns with ours. We have already discussed many of Gartner’s key findings and recommendations in this blog, and thought a review would serve as a high-level primer on Tamr and our unique approach to data integration and quality.
For example, in The Big (C-Level) Data Conundrum, Tamr advisor Frank Moss echoes a recommendation for CIOs and CDOs made by Gartner:
Assess the business value that can be derived from integrating unstructured, semistructured and structured data to use cases and business scenarios.*
Moss addresses his remarks as well to the C-suite, writing “I urge you to immediately take stock of the distance between your enterprise’s current reality and the Big Data Promise of a truly unified view of your customers, suppliers and employees.”
In a recent piece on scalable data integration, this year’s Turing Award winner (and Tamr Co-Founder and CTO) Michael Stonebraker takes a broad view of the “data integration and data quality capabilities” that Gartner says are “of critical importance to the information infrastructure that enables digital business.”* Specifically, Mike offers 5 Tenets for Success in scalable data integration using 3rd-generation systems, like Tamr’s, that use machine learning to make automatic integration decision … then asking a human for help only when required.
Daniel Bruckner, also a Tamr Co-Founder, expanded on this 3rd-gen approach in a highly engaging post titled Rise of the [Data Preparation] Machines, which touches on Gartner’s recommendation for CIOs and CDOs to:
Explore the limits of automation and machine-learning in data discovery within big data sources, and the data relationships across them, to address the exponential rise in big and small data sources.*
In a Data Visibility piece explaining our launch of Catalog as a free, standalone data cataloging tool, we addressed Gartner’s finding that the proliferation of sources are making it “increasingly difficult to understand the information most relevant to drive forward their digital business agenda.”*
Jerry Held took on the issue of, in Gartner’s terms, “a growing demand for data discovery and integration technology that is … independent of its physical location”* in a provocative post on a Data Lake controversy that Gartner itself prompted last year.
And finally, in a post on matching imperfect data across sources through string similarity, we dove deep into what Gartner cites as an “increasing need [for organizations] to understand not only the data within diverse data sources, but also the relationships across them.”*
* Gartner, Inc. — “Cool Vendors in Data Integration and Data Quality, 2015,” 8 April 2015
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