Big Data Quality

Poor data quality is consistently identified as a significant inhibitor to gaining real benefit from big data analytics. Without reliable information in your new data repositories and data lakes you cannot derive trusted insight from your big data investment.

Trillium Big Data allows business professionals and data stewards to quickly assess and improve the quality of Big Data. Our infinitely scalable, multidomain data quality platform runs natively against Hadoop and can be deployed in 90 days or less.

With Trillium Big Data you can:

  • Uncover data details and valuable information by profiling and analyzing multi-domain data to identify buying behaviors, areas of improvement and application results;
  • Build the best view of their global customer base in order to gain actionable consumer insights, analyze purchase history and better detect fraud
  • Power analytics platforms with reliable, fit-for-purpose data that support timely, accurate business decisions
  • Seamlessly extend existing Trillium data quality processes, workflows and business rules to maintain enterprise data quality standards

Trillium Big Data encompasses a full suite of data quality capabilities: 

  • Easily build and deploy data quality projects using Trillium’s graphical interface, without the need for MapReduce programming
  • Deploy data quality workflows as native, parallel MapReduce functions for optimal efficiency
  • Cleanse international datasets with support for data cleansing and matching with postal and country-code validation and enrichment natively within the Hadoop environment.
  • Integrate, parse, standardize, and match new and legacy customer and business data from multiple disparate sources
  • Analyze data quality workflow metrics, and adjust workflow rules and processes to meet business requirements

Contact us to learn more about Trillium Big Data

Related information:

Whitepaper: Big Data and the Data Quality imperative

ebook: Power Big Data with Reliable information

Datasheet: Trillium Big Data

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