By Carol Newcomb, Senior Consultant

They say that Data Governance is about People, Process and Organization.   Much of the design work in planning for data governance is around people’s roles and responsibilities, then designing the organizational structure that will provide authority for decisions to be made and enforced.   The processes, however, are not new.   They are probably already being practiced within your organization, just in a decentralized, informal way.   In this blog series, I discuss the processes for 1) investigating and isolating the data quality issues—Root Cause Analysis—, 2) starting to collect complete Metadata Definitions, and 3) performing Data Quality Analysis.   Only when your governance group has worked through each step, in order, will you be more likely to design the appropriate solution.

Root Cause Analysis

The process of data governance is fundamentally very simple.

1. Identify the data quality issues to address


2. Prioritize the portfolio of issues to isolate/tackle the most important


3. Perform Root Cause Analysis to determine the true source of the data issue


4. Design the corrective action


5. Formalize the correction through consideration & approval by the Data Governance organization


6. Implement the fix


7. Monitor the results

Root Cause Analysis is the process of identifying probable causes of a data issue, and isolating the contributing factors.   In order to resolve any particular issue, root cause analysis involves fact-finding, drilling into details of the problem, talking to the right people, and separating out other associated (but not contributing) factors.

A standard tool for supporting the detailed findings is the Ishikawa Diagram, below.   

• Diagnose the problem as if you are a physician or a detective. Consider all possible sources of the symptom. Don’t rule anything out yet!


• Boil the ocean—be exhaustive and creative.


• Don't practice problem solving before collecting all possible causes.


• Practice the ”5 Why’s”—don’t stop asking ”Why” until you have exhausted every conceivable potential reason.


• Rank the factors if possible.   Identify the Primary causes versus the Secondary or associated factors.


• Rule out each possible factor one at a time.   Justify why (you may need to come back to this later).


• Find all potential business process and data owners to involve them in your understanding of the possible sources of the problem.


• Share the findings with everyone involved in troubleshooting. They could rule out certain factors with their knowledge.


• Test your hypotheses with actual data.     


• Fix the problem and test again.


• Publish/share your findings and fixes.   Communicating your findings may reveal additional factors you hadn’t considered.

After a thorough Root Cause Analysis has been completed, Data Stewards should proceed to Metadata Analysis and Data Quality Analysis.   These two techniques will be discussed in my next blogs.

Carol
Newcomb is a Senior Consultant with Baseline Consulting. She
specializes in developing BI and data governance programs to drive
competitive advantage and fact-based decision making. Carol has
consulted for a variety of health care organizations, including Rush
Health Associates, Kaiser Permanente, OSF Healthcare, the Blue Cross
Blue Shield Association and more. While working at the Joint Commission
and Northwestern Memorial Hospital, she designed and conducted
scientific research projects and contributed to statistical analyses.