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Infosolve Unveils Data Quality Platform


OpenDQ V2.0 offers data cleanup with a dash of ETL

By P. J. Connolly
November 15, 2007 — 
It may not seem possible for data to rot, but it does. The problem isn’t nearly as acute with neatly structured data; that’s relatively easy to keep fresh and solutions for that sort of problem have existed for years. Instead, unstructured data is the challenge facing enterprises today. Free text data in the form of documents and messages is the missing link for many organizations, foiling attempts at categorizing and integrating into a company’s knowledge assets.

Infosolve Technologies released on Oct. 23 OpenDQ version 2.0, a Java-based open source data quality and ETL (extract, transform and load) platform released under GPLv2. It includes reporting tools with dashboards for monitoring data quality, and customer rule development for flagging nonconforming records.

The company’s objective with OpenDQ V2.0 was to “provide businesses of all sizes innovative, cost-effective data integration and data quality solutions that maximize the quality of their data,” noted company vice president Subbu Manchiraju.

OpenDQ V2.0 offers users the choice of batch or interactive processing, offers workflow and scheduling capabilities, with integration options into other enterprise data processes using drag-and-drop components.

The data profiling tools allow users to develop standards while discovering hidden formats, patterns and rules. OpenDQ can generate reports on column minimums, maximums and averages, measure rule compliance across data sets and provide point-in-time data profiling history. Reports are available in common formats including CSV, HTML and PDF.

OpenDQ allows users to develop data dictionaries, and the company offers external data enhancement along demographic, firmographic and psychographic lines through a subscription service. The company uses grid computing technology from Sun Microsystems to support its services infrastructure.

‘Fuzzy Matching’
Infosolve includes “fuzzy matching” in OpenDQ, identifying relationships with inclusive algorithms. It enables master data and reference data comparisons, and offers matching capabilities across multiple data attributes and multiple sources.

Standardization and de-duplication are also in the purview of OpenDQ V2.0, which has the ability to compare against the master set, and offers auditing and logging of the de-duplication process. It also allows for removal of nonconforming data and so-called “noise.”

The unstructured data interface of OpenDQ uses natural language processing to capture key data elements from sources, and provides what the company claims is “seamless” integration of structured and unstructured data.

Manchiraju noted, “We see this expansion of Infosolve’s solutions as another step in our commitment to help businesses achieve maximum profitability, with comprehensive data integration and data quality management solutions.”


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