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Last Updated Date May 26, 2021 |


When a potential data quality issue has been identified it is imperative to develop a business case that details the severity of the issue along with the benefits to be gained by implementing a data quality strategy. A strong business case can help build the necessary organizational support for funding a data quality initiative.


Building a business case around data quality often necessitates starting with a pilot project. The purpose of the pilot project is to document the anticipated return on investment (ROI) of a larger data quality project. It is important to ensure that the pilot is both manageable and achievable in a relatively short period of time. Build the business case by conducting a Data Quality Audit on a representative sample set of data, but set a reasonable scope so that the audit can be accomplished within a three to four week period.

At the conclusion of the Data Quality Audit a report should be prepared that captures the results of the investigation (i.e., invalid data, duplicate records, etc.) and extrapolates the expected cost savings that can be gained if an enterprise data quality initiative is pursued.

Below are the five key steps necessary to develop a business case for a Data Quality Audit. Following these steps also provides a solid foundation for detailing the business requirements for an Enterprise data quality initiative.

1. Identify a Test Source

    a. What sources are to be considered?

A representative sample set of data should be evaluated. This can be a cross-section of an enterprise data set or data from a specific department in which a potential data quality issue is expected to be found. If possible, also identify and include any data considered of high value or mission critical to the organization. Even if the percentage of errors is very low, the criticality of the data may justify a solution.

    b. What data within those table or files (priority, obsolete, dormant, incorrect) will be used?

Prior to conducting the Data Quality Audit, the type of data within each file should be documented. The results generated during the Audit should be tracked against the anticipated data types. For example, if 10% of the records are incorrectly flagged as priority (when they should be marked obsolete or dormant) any reporting based upon the results of this data will be skewed. In addition, consider the type of data relative to identifying problems. Errors on active records may be very relevant while those found on records flagged as obsolete may not justify a data quality solution.

2. Identify Issues

    a. What data needs to be fixed?

Any anticipated issues with the data should be identified or confirmed prior to conducting the Audit in order to ensure that the specific use cases are investigated.

    b. What data needs to be changed or enhanced?

A data dictionary should be created or made available to capture any anticipated values that should reside within a given data field. These values will be utilized via a reference lookup to analyze the level of conformity between the actual value and the recorded value in the reference dictionary. Additionally, any missing values should be updated based upon the documented data dictionary value.

    c. What is a representative set of business rules to demonstrate functionality?

Prior to conducting the Audit, a discussion should be held regarding the business rules that should be enforced in the provided data set. The intent is to use the expected business rules as a starting point for validation of the data during
the Audit. As new rules are likely to be identified during the Audit, having a starting point ensures that initial results can be quickly disseminated to key stakeholders via an initial data quality iteration that leverages the previously documented business rules.

3. Define Scope

    a. What can be achieved with which resources in the time available?

The scope of the Audit should be defined in order to ensure that a business case can be made for a data quality initiative within weeks, not months. The project should be seen as a pilot in order to validate the anticipated ROI if an enterprise initiative is pursued. Just as the scope should be well defined, commitments should be agreed upon prior to starting the project that the required resources (i.e., data steward, IT representative, business user) will be available as needed during the duration of the project. This will ensure that activities such as the data and business rule review remain on schedule.

    b. What milestones are critical to other parts of the project?

Any relationships between the outcome of the project and other initiatives within the organization should be identified up front. Although the Audit is a pilot project, the data quality results should be reusable on other projects within the organization. If there are specific milestones for the delivery of results, this should be incorporated into the project plan in order to ensure that other projects are not adversely impacted.

4. Highlight Resulting Issues

    a. Highlight typical issues for the Business, Data Owners, the Governance Team and Senior Management.

Upon conclusion of the Audit, the issues uncovered during the project should be summarized and presented to key stakeholders in a workshop setting. During the workshop, the results should be highlighted, along with any anticipated impact to the business of problems found. Discussion should include all consumers of the data to identify the overall scope of the problem and ensure all relevant costs of the issue are included when evaluating the ROI. In addition, the risks and  consequences to the business should be identified if a data quality solution is not implemented.

    b. Test the execution resolution of issues.

During the Audit, the resolution of identified issues should occur by leveraging Informatica Data Quality. During the workshop, the means to resolve the issues and the end results should be presented. The types of issues typically resolved include: address validation, ensuring conformity of data through the use of reference dictionaries and the identification and resolution of duplicate data.

5. Build Knowledge

    a. Gain confidence and knowledge of data quality management (DQM) strategies, conference room pilots, migrations, etc.

To reiterate, the intent of the Audit is to quantify the anticipated ROI within an organization if a data quality strategy is implemented. Additionally, knowledge about the data, the business rules and the potential strategy that can be leveraged throughout the entire organization should be captured.

    b. The rules employed will form the basis of an ongoing DQM Strategy in the target systems.

The identified rules should be incorporated into an existing data quality management strategy or utilized as the starting point for a new strategy moving forward.

The above steps are intended as a starting point for developing a framework for conducting a Data Quality Audit. From this Audit, the key stakeholders in an organization should have definitive proof as to the extent of the types of data quality issues within their organization and the anticipated ROI that can be achieved through the introduction of data quality throughout the organization.

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