DQ Improvement Jumpstart- Using the Conformed Dimensions of Data Quality

A 2018 Survey found that less than 3% of Companies are measuring all the dimensions of data quality! Your organization can learn how to communicate, to measure, and improve data quality faster using the Conformed Dimensions.

In this 3-6 hour session (depends on configuration and client needs), Dan Myers (Principle Information Quality Educator), explains how to measure data quality with the Conformed Dimensions of Data Quality (CDDQ) based on the following course objectives:

  1. Enable participants to practice diagnosing DQ issues using the Conformed Dimensions of DQ
  2. Explain available DQ detection, control, and improvement techniques and associated Conformed Dimensions of Data Quality
  3. Enable participants to leverage existing Conformed Dimensions Example Metrics

In the first section of the course we'll identify existing definitions for the Dimensions of Data Quality (DAMA, ISO, Redman, English…etc.) and then identify the similarity and gaps in order to identify what a Conformed set of Dimensions of Data Quality look like. Then we’ll insert real-world examples of poor data quality described using these Conformed Dimensions of Data Quality.

These include business KPIs of importance, such as:
•    Cost Impact- Poor Data Quality Leads to High Cost
•    Sales/ Revenue Impact- Poor Data Quality Leads to Decreased Sales
•    Customer Experience- Poor DQ Leads to Decreased Experience

During the second section of the course Dan provides real-world datasets and data quality improvement techniques used by practitioners. In this session we step through 16 DQ improvement techniques across 5 categories, providing examples and explanation for use.
1.    Validation Techniques (3)
2.    Completeness and Consistency Techniques (4)
3.    Data Profiling Techniques (4)
4.    Human Directed Audit Techniques (3)
5.    Human Input Techniques (2)

Each attendee will receive a DQ technique to Conformed Dimension grid so that they can quickly identify candidate techniques for improvement after DQ issue identification. Case studies/examples will be distributed and discussed so that attendees gain hands-on experience identifying issues and then evaluating the most appropriate DQ technique.

More Information available here in the DQ Jump-start Brochure

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