Dr. Thomas C.Redman
Getting in Front of Data Quality
Assignment 1 of 5:
Documenting The Process Management Cycle
Create a document outlining the 7 stages of the Process Management Cycle below.
Ensure you understand each stage of the cycle and refer back to the presentation if required.
Identify a situation in your organisation that could be used for a trial implementation of the 7 steps of the Process Management Cycle.
The Process Management Cycle for Data Quality (by Tom Redman)
Assignment 2 of 5:
Adapt Tom Redman’s analogy of the ‘Data Lake’ for your own data landscape
Undertake an investigation to learn how your organisation currently ‘cleans up its data lake’.
Speak to people and learn how the costs of 'waste from data pollutants' are incurred.
Document how your organisation would benefit from removing pollutants at source instead of downstream in the data lake.
Deliver your findings in the form of a white paper or presentation to senior stakeholders.
IMPORTANT: With the term ‘Data Lake’, Tom is not referring to the modern term for a data store in the Cloud. Tom is instead referring to a data collection point that is downstream of an operational system.
Assignment 3 of 5:
Describe the role of the I.T. (Tech) function in your organisation
Discuss with your peers the role of I.T. in your organisation.
Document the advantages and disadvantages of the current I.T. approach and its impact on data quality based on the recommendations in the keynote presentation by Tom Redman.
Assignment 4 of 5:
Review the sentiment of critical data creators and data customers
List the most important data creators and data customers in your organisation or area of focus.
Discuss their opinions on the quality of data they create or consume to gain some initial findings.
Try to learn what issues the creators and customers of data experience and what impact they cause.
Rank the issues by priority and discuss with peers how you could permanently resolve each issue at the start of the information chain.
Assignment 5 of 5:
Review the sentiment of critical data creators and data customers
Study the following articles by Tom Redman to learn more about the data quality processes he helped pioneer during his time at AT&T and the Friday Afternoon Measurement approach:
Even the tiniest error can cost millions – Harvard Business Review, August, 2014
Improve data quality for competitive advantage – Sloan Management Review, January, 1995
Assess Whether You Have a Data Quality Problem (The Friday Afternoon Measurement approach) - Harvard Business Review, July, 2016
Only 3% of Companies’ Data Meets Basic Quality Standards - Harvard Business Review, July, 2016
In the examples given, the organisation is able to quantify the cost of errors and identify root-causes using a method of tracking data quality through different processes.
How can this tracking approach work on your data?
Try taking a small number of records (start with 5 or less) and track them through key events and processes to see how they are impacted by data quality.
Use the ‘Friday Afternoon Measurement’ approach above for further guidance.