Data Governance in FIs: Root Cause Analysis

This series focuses on Data Governance in Financial Institutions. Our first post introduced the fundamentals of Data Governance. This discussion centers on how to find root causes of problems in organizations and recommend actions to solve them.

 When analyzing deep issues and causes, it is important to take a comprehensive and holistic approach. Root Cause Analysis (RCA) is the systematic problem-solving approach intended to identify root causes of problems or events. It is based on the principle that problems are most effectively solved by correcting or eliminating the primary causes, rather than only addressing the symptoms. Properly done, RCA can help a Financial Institution implement an effective Data Governance program by investigating and addressing data quality issues. It can also help the FI design the appropriate data governance policies and data standards.

What is a Root Cause?

A root cause is an initiating event or condition in a cause-and-effect chain. It must be subject to change; that is, there is a definable factor that can be adjusted to create a positive outcome or to prevent a negative one.

A root cause must also meet four criteria:

  1. It is an underlying event that initiates a sequence of subsequent events
  2. It is logically and economically practical to identify
  3. It can be affected by management actions
  4. It is a practical basis to formulate and recommend corrective actions

The Process of RCA

There is no prescriptive process for RCA, but there are five steps that can help guide organizations:

5 steps for the Root Cause Analysis process

These steps are best completed via an iterative approach rather than a sequential one, to encourage regular participant feedback and continuous improvement based on that feedback.

Describe the Problem

When describing a problem, start with a factual statement of what is happening and why it is a problem. The following questions can also help when describing a problem:

  • When did the problem first occur?
  • Is it continuous or occasional?
  • Has the frequency of occurrence increased or decreased over time?
  • Who are the stakeholders and what processes are involved?

Gather the Data

Once the problem is defined, you can then begin gathering the data. Gathering the data usually entails collecting and reviewing examples of problem instances. You can also use these techniques to seek possible causes:

  • A review with subject matter experts (SMEs): does this root cause make sense?
  • A brainstorming session with SMEs and stakeholders: what do you think the problem could be?
  • Change analysis: what changed when the problem started?
  • Identification of archetypes: are there common patterns of behavior in systems?
  • Compare and contrast: when does the problem happen and when does it not?

Model Causal Changes

A causal model is a conceptual model that describes the causal mechanisms of a system. There are various techniques that can be used for modeling causal changes, but this blog will focus on the most common and widely useful techniques, including Five Whys, Fishbone Diagramming, and Causal Loops.

Five Whys

Five Whys is a good tool for identifying a single most prominent cause.

How to complete the Five Whys

  • Write down the specific problem to help formalize the problem and describe it completely.
  • Ask “why” the problem happens and write the answer down below the problem.
  • If the answer provided does not identify the root cause of the problem that you wrote down in Step 1, ask “why” again and write that answer down.
  • Loop back to step 3 until the team agrees that the problem’s root cause is identified. This may take fewer or more than five times.

Five Whys Example

5 whys diagram example

Fishbone Diagramming

Fishbone Diagramming is effective for causal hierarchy and linear chains with multiple causes.

How to complete the Fishbone Diagram:

  1. Identify the problem statement and write it at the mouth of the fish.
  2. Identify the major categories of causes of the problem and write them as branches of the main arrow for each of the major categories. Some examples include equipment or supply factors, environmental factors, rules/policy/procedure factors, and people/staff factors.
  3. Ask “why” a major category cause happens and write the answer as a branch from the appropriate category.
  4. Repeat the other categories by asking “why” about each cause.
  5. Write sub-causes branching off the cause branches.
  6. Ask “why” and generate deeper levels of causes and continue organizing them under related causes or categories until the root cause is identified.

Fish Diagram Example

Fishbone diagram example

 

Causal Loops

Causal Loops work well for complex situations that involve circles of influence.

How to complete Causal Loops:

  1. Identify the nouns or variables that are important to the issue.
  2. Fill the “verbs” by linking the variables together and determining how one variable affects the other. Generally, if two variables move in the same direction or have a positive relationship, the link would be denoted as an “s”. If the two variables move in an opposite direction or have a negative relationship, the link would be labeled by an “o”.
  3. Determine if the links in the loop will produce a reinforcing or balancing causal loop and label them accordingly. To determine the type of the loop, count the number of “o’s”. If there are an even number of “o’s” or none are present, it is a reinforcing loop. If there are an odd number of “o’s”, it is a balancing loop.
  4. Walk through the loops and “tell the story” to be sure the loops capture the behavior being described.

Causal Loop Example

Causal Loop example

 

The model causal changes techniques can be used alone or in combination with one another to get as much information on the problem as possible.

Identify Root Causes

When identifying the root cause(s), it is a good idea to ask questions like “Where can I remove or correct the issue?” or “Where can I minimize the effect?”. It is worth noting that good analysis is actionable analysis, so if there is not enough information to answer these questions, it may be a good idea to circle back to the previous steps in the process.

Recommend Actions

And finally, when recommending actions, we want to eliminate interference and errors, improve processes, and consider side-effects of actions. It is beneficial to plan ahead to predict the effects of your solution so you can spot potential failures before they happen. It is important to learn from underlying issues within the root cause so that you can apply what you learned to systematically prevent future issues. RCA may require multiple corrective actions but if a root cause is identified correctly, it is unlikely that problems will reoccur.

Conclusion

RCA is an essential way to perform a comprehensive and system-wide review of significant problems as well as the factors that led to them. By following the process of RCA above, you will be able to describe a problem, gather data, model casual changes, identify root causes, and ultimately recommend actions that lead to a long-term solution.

 

RELATED:

Data Governance in FIs: Intro to Data Governance

REFERENCES:

Carol Newcomb on The Data Roundtable. “A Data Governance Primer, Part 1: Finding the Root Cause.” The Data Roundtable, 4 Sept. 2013, https://blogs.sas.com/content/datamanagement/2013/09/04/a-data-governance-primer-part-1-finding-the-root-cause/.

“Causal Loop Construction: The Basics.” The Systems Thinker, 14 Jan. 2016, https://thesystemsthinker.com/causal-loop-construction-the-basics/.

Cause and Effect Analysis: Using Fishbone Diagram and 5 Whys, https://www.visual-paradigm.com/project-management/fishbone-diagram-and-5-whys/.

“Determine the Root Cause: 5 Whys.” ISixSigma, 27 Nov. 2018, https://www.isixsigma.com/tools-templates/cause-effect/determine-root-cause-5-whys/.

“ELearningCurve.” Information & Data Management Courses & Certification Online,                       https://ecm.elearningcurve.com/category_s/213.htm.

“Root Cause Analysis Explained: Definition, Examples, and Methods.” Tableau, https://www.tableau.com/learn/articles/root-cause-analysis.

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