DataFlux Accelerator for Customer Data Analysis

Understand your customer information faster by generating data quality scorecards

Organizations worldwide are turning to master data management (MDM), legacy data migrations or data consolidations to provide the foundation for improved customer relationships. Before you can integrate and improve this data, it's important to understand the strengths and weaknesses of your customer data. The DataFlux Accelerator for Customer Data Analysis can discover exactly what data problems exist in your company's customer data repositories, and then turn this knowledge into a detailed plan to fix those issues.

Based on best practices gleaned from years of experience with customers around the world, the DataFlux Accelerator for Customer Data Analysis provides pre-built scorecards to help you gauge the health and integrity of data – a critical component at the outset of any data management effort.

The Accelerator for Customer Data Analysis helps organizations:

  • Rapidly discover and analyze customer data quality problems
  • Utilize pre-built metrics for accuracy, completeness, consistency, structure, uniqueness and validity
  • Create a road map for data quality and data integration
  • Create data quality scorecards to keep data improvement initiatives on track

The Accelerator for Customer Data Analysis is used with the DataFlux Data Management Platform to provide an evolutionary approach to data quality and data integration initiatives, allowing you to easily transition from tactical data deployments to full-scale enterprise data management initiatives.

Features and Functionality

The Accelerator for Customer Data Analysis gives you the ability to produce complete metrics about individual data sets and pinpoint areas that need attention. By identifying problematic data early, you can drastically reduce the time required to build a comprehensive data quality program.

 

Features

  • Compare data sources and set thresholds for acceptable data
  • Measure key criteria such as data completeness, consistency and structure
  • Develop a baseline assessment of the current state of your customer information
  • Set milestones for data improvement and monitor your progress along the way

Functionality

  • Complete reporting scorecards that can be sent throughout the enterprise as a web-based report or PDF
  • Drill down to produce complete metrics about individual data sets and pinpoint areas that need attention
  • Retrieve reports that detail which data elements are outside of acceptable limits and receive a weighted score of the entire data set’s performance
  • Incorporate pre-built and customizable reports, charts and graphs
  • Create scorecards complete with full support of industry-standard process improvement programs, including Six Sigma
 

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This webcast with David Loshin of Knowledge Integrity Inc, Ron Agresta of DataFlux, and Ken Hausman of SAS offers instruction on how to gather best practices for using Master Data Management.

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