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Data Quality Tools and Technologies

Prerequisite: None

One-Day Course

Once you recognize that poor data quality impacts your business, how do you go about eliminating the sources of data flaws? Data quality tools and technologies are employed in different ways to help discover data issues, isolate their source, correct the data (if necessary), and adjust the processes to prevent unexpected data from entering the environment in the first place.

This full-day course explores different data error paradigms and approaches for addressing those errors using data quality tools. We also look at the different tool types, how they are intended to work, and how to develop best practices for standardizing the way the tools are used across the enterprise. Finally, we review how to assess business needs to determine which tools are most appropriate to be integrated into your organization.

This course is intended to help organizations figure out how to use the data quality tools they already have, and institute processes for employing those tools to achieve data governance, inspection and control, cleansing, data preparation, and master data management. Attendees will learn how the following approaches work and how they integrate with an enterprise data quality program.

You Will Learn

  • Data parsing and standardization
  • Record linkage/matching
  • Data scrubbing/cleansing
  • Data enhancement
  • Data profiling
  • Data auditing/monitoring

Geared To

  • Anyone with an implementation role in a DW, BI, data reengineering, data integration, or information quality project with a need for high-quality data


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