Syncsort Releases Trillium DQ for Big Data

Syncsort has released Trillium DQ for Big Data, providing data profiling and data quality capabilities in a single solution.

"Recent Syncsort research revealed more than 72 percent of respondents reported sub-optimal data quality negatively impacting business decisions," said Tendü Yogurtçu, chief technology officer at Syncsort, in a statement. "Almost half also found untrustworthy results or inaccurate insights from analytics were due to a lack of quality in the data fed into downstream applications, such as AI and machine learning. By providing integrated data profiling, cleansing, standardization, and matching on distributed and cloud platforms, we are empowering organizations to resolve the data quality issues and drive significant business value from their data."

Trillium DQ for Big Data is an integrated solution that delivers profiling, cleansing, standardization, and matching, including strong entity resolution on distributed architectures, on premises and in the cloud.

"Analysis of data quality process flow results is essential for data analysts to support continuous data quality improvement and deliver optimal results within targeted time windows," Yogurtçu said further. "With the new Syncsort Trillium product, we are enabling them to easily profile large, more diverse data sources in a few simple steps, explore the results of the profiling from a business-friendly user interface to discover new insights and issues, and monitor the quality of their data to allow delivery of reports and findings to business leaders. Data analysts and data quality specialists can also design, develop, and deploy highly-scalable data quality solutions in their data pipelines or in rea -time to cleanse, standardize, match, and resolve entities without technical expertise in Big Data and distributed architectures."

Highlights of Trillium DQ for Big Data include the following:

• Single solution to support a variety of Big Data quality use cases for understanding issues, challenges, and value of the data in the data lake, including third-party data, requirements to utilize, govern, and trust key business information, and advanced analytics to build data quality solutions that provide a 360-degree view in support of critical analytical and compliance requirements (e.g. fraud detection, anti-money laundering, omnichannel marketing, predictive analytics, data science, and machine learning).
• Scalable data profiling of high volumes of data and comprehensive matching of complex entities to help build a 360-degree view of key entities (e.g. customers) for business-critical applications and AI pipelines within the data lake.
• An interface with hundreds of built-in business rules and drill-down capabilities to get insight into issues and anomalies.
• Design once, deploy anywhere design for deploying data quality jobs natively to Big Data execution frameworks, including Hadoop MapReduce and Spark, or through real-time services.

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