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DQLabs Mentioned in Gartner Research

DQLabs, a visionary in augmented data quality management, announced it has been recognized as a vendor using AI-enabled methods in helping organizations deliver quality data in the December 2020 Building Automation into Your Data Quality Initiatives report, by leading research and advisory company, Gartner.

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“We’re thrilled Gartner has recognized DQLabs,” said Raj Joseph, CEO, DQLabs, Inc. “We strive to provide organizations the ability to manage data smarter through our autonomous, continuous learning AI/ML product. For us, this recognition is a testament to our commitment and effort to offer industry leading data management capabilities to help our client partners maximize and realize their full revenue potential.”

According to the Gartner report, “With the growing volume and complexity of datasets entering organizations, automation is required to scale data quality practices. Using AI, the process is greatly accelerated and can greatly improve technical professionals’ capacity to detect and adjust to potential data quality problems before they impact data driven processes.”

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“In today’s data world, traditional methods of data management take time, cost and effort,” said Raj Joseph, CEO, DQLabs, Inc. “Not only that but when organizations change strategies, the underlying data shifts forcing to revisit rules, procedures created and play a constant catch up game of trying to understand data. DQLabs combines data catalog, governance, lineage and data privacy capabilities with a focus towards data quality enabling organizations to leverage good data immediately.”

DQLabs was named as a data preparation/data fabric vendor that utilizes AI-enabled methods to deliver quality data. The report also recommends organizations to seek Data-Driven AI-Enabled Data Quality Tools for Complex and Rapidly Changing Data Problems and Metadata-Driven, Top-Down Data Quality Solutions for Analytics Governance and MDM Use Cases. DQLabs with its comprehensive integrated modules support both root cause and data remediation efforts with fully transparency, auditability of automated data quality processes.

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