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.


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.



Data Governance in FIs: Intro to Data Governance


Carol Newcomb on The Data Roundtable. “A Data Governance Primer, Part 1: Finding the Root Cause.” The Data Roundtable, 4 Sept. 2013,

“Causal Loop Construction: The Basics.” The Systems Thinker, 14 Jan. 2016,

Cause and Effect Analysis: Using Fishbone Diagram and 5 Whys,

“Determine the Root Cause: 5 Whys.” ISixSigma, 27 Nov. 2018,

“ELearningCurve.” Information & Data Management Courses & Certification Online,             

“Root Cause Analysis Explained: Definition, Examples, and Methods.” Tableau,

Data Governance in FIs: Intro to Data Governance

This blog series will focus on Data Governance in Financial Institutions. Our first post introduces data governance fundamentals. It will be followed by a discussion of root cause analysis, metadata, the five stages of data governance deployment, and a final blog that crafts a business case for data governance.

Today’s industry leaders recognize data among their top enterprise assets. According to Gartner, the leading global research firm, 20-25% of enterprise value is directly attributed to the quality of its data. However, Financial Institutions (FIs) often underutilize this key business driver by not establishing a formal data strategy.

Let’s look at some of the challenges to building a data strategy, opportunities for implementing a data strategy, critical components of a successful DG program, and the aspects of data that can be governed. We also want to discuss some potential consequences of poor DG implementation and the most important step to mitigate the risk of it occurring in a Financial Institution.

What is Data Governance?

Data Governance (DG) serves as the framework for defining the who, what, when, how, where and why of your formal data strategy. Through the collection of policies, roles, and processes, DG ensures the proper definition, management, and use of data towards achieving enterprise goals.

Challenges of Building a Data Strategy

Too often, the largest hindrance to building a data- and analytics-driven enterprise is the enterprise itself. For historical reasons, data tends to be siloed within internal business units, resulting in disparate collections of overlapping yet inconsistent data. Given that data is built and accumulated over time in various places in the organization, often via mergers and acquisitions, it can be difficult and time-consuming to gather and use the data.

Without a transparent view of enterprise-wide data, credible decision making becomes nearly impossible. More time is spent gathering and consolidating the data than analyzing it. The goal, then, of DG is to break down the silos in which data becomes segregated and foster a holistic approach towards managing common data. Common data creates a shared understanding of data information and is of paramount importance when sharing data between different systems and/or groups of people.

With the proper implementation of DG standards (data naming, quality, security, architecture, etc.), a firm can realize a variety of optimization-based benefits.

Data Strategy Opportunities

An enterprise that properly implements and executes DG creates opportunities for enhanced productivity.

For example, if an enterprise works with large data sets, having defined naming standards allows for data consistency across all commonly used domains (i.e., Customer, Transactions, Employee, etc.) within the enterprise. This results in increased productivity and a competitive advantage relative to other firms.

As DG improves operational efficiencies, FIs can expect increased customer satisfaction rates, attracting both a loyal following from current customers and new prospects.

Critical Components of a Successful Data Governance Program

FIs have a lot of information as part of their normal business processes so it may be difficult to identify what data needs to be governed.

It is important to note that not all data needs to be governed. There are two types of data that do not need DG: department-specific data and application data not needed for regulatory reporting or cross-department communication.

However, there are three key types of data that should be governed to provide reliable information that can be leveraged across all departments of the FI:

  • Strategic data is unique and usually created within the company, providing a competitive advantage to the firm. A few examples include data about customer insight, market insight, and risk models.
  • Critical data ‘materially affects’ most external reporting, risk management, and/or supports critical business functions. This includes financial data, supply chain data, and counterparty data.
  • Shared data is used in multiple business processes in which the definition, quality, and format needs to be synchronized. For example, customer data for marketing, customer service and sales, and counterparty data for risk management and pricing.

Critical Data Aspects

Beyond the data itself, there are multiple aspects of data that are critical to govern. A successful program will consider the following:


Data Ownership: The possession of and responsibility for information

Data Handling: Ensuring that research data is stored, archived or disposed of in a safe and secure manner

Data Allowable Values: Some data types let you specify that a property is restricted to a set of values

Meta Data: A set of data that describes and gives information about other data

Data Storing: The recording of information in a storage medium

Data Architecture: The structure of an organization’s logical and physical data assets and data management resources

Data Quality: The state of qualitative or quantitative pieces of information

Data Definitions: The origin of a field that references a data domain and determines the data type and the format of data entry

Data Reporting: Collecting and formatting raw information and translating it into a digestible format to assess business performance

Poor DG Consequences

A word of caution: There is such thing as poor DG implementation. If your program is poorly built, the enterprise will suffer.

Building inefficient processes, for example, can delay timelines for tasks like data retrieval and data analysis.

An inferior DG implementation may also create compliance issues. If the program is difficult to understand, enterprise employees may disregard your guidelines.

Overall, if DG is applied within internal silos, it cannot be optimized across the organization. The segregation of data that internal silos create needs to be broken down to achieve the goal of managing common data.

How to Mitigate Poor DG Risk

The entire FI must “buy in” to a DG program to be most effective. Without assistance from both data practices and business functions in the rollout of DG program initiatives, the program will likely fail. It is the responsibility of the business, IT, and internal operations facets to be fully engaged and coordinated within the implementation of DG program initiatives.

What’s Next?

Now that we have outlined what a successful Data Governance program includes, it is time to discuss Root Cause Analysis. Our next post in this series will discuss how to find root causes in FIs and recommend actions to solve problems that you may face when implementing a DG program.



“ELearningCurve.” Information & Data Management Courses & Certification Online, 

 Data Ownership, 

 Data Handling, 

 “Administering and Working with Oracle Enterprise Data Management Cloud.” Oracle Help Center, 24 Nov. 2021, 

 “Metadata.” Wikipedia, Wikimedia Foundation, 23 Dec. 2021, 

 “What Is Data Storage?” IBM, 

 Olavsrud, Thor, and Senior Writer. “What Is Data Architecture? A Framework for Managing Data.” CIO, 24 Jan. 2022,

 “What Is Data Quality? Definition and Faqs.” OmniSci,

 “Data Definitions.” IBM, 

 “What Is Data Reporting and Why It’s Important?” Sisense, 21 May 2021,


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