Data Quality Management Improves Organizational Knowledge
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Data Quality Management Improves Organizational Knowledge

Laura Sebastian-Coleman, Ph.D. Data Quality Director, Prudential
Laura Sebastian-Coleman, Ph.D. Data Quality Director, Prudential

Laura Sebastian-Coleman, Ph.D. Data Quality Director, Prudential

It is commonplace to say that data is an “asset,” but most organizations do not treat data as they treat their other assets. They are unaware of the quantity or condition of their data. The organizations create data for their immediate needs and without considering the potential for a broader use. Moreover, they do not differentiate between data that is valuable or the one that has little importance. Few provide guidance for employees about how to get value from data. Fewer still recognize the costs of low-quality data or the benefits of high-quality data.

The failure to manage data as an asset prevents organizations from taking advantage of the knowledge and insight they could gain from their data and the ways that they can derive value from their data. In many organizations, lack of understanding of their own data is a huge blind spot, which is also a terrible waste of resources.

Data quality management helps address this blind spot. At its simplest, data quality management is the application of product quality management methodologies to data. It aims to improve the quality of data and to sustain the levels of data quality that an organization needs to deliver on its mission and serve its customers, regardless of how an organization defines its mission and customers. Data quality practitioners must help the organization answer and act on the findings from three fundamental questions:

• What do we mean by high-quality data?

• How do we detect low-quality or poor-quality data?

• What action we take when data does not meet quality standards?

To answer these questions, data quality practitioners must help the organization:

• Define Expectations for Quality in the form of standards, rules, models, and requirements from data consumers. Defining standards is a means of clarifying expectations for quality so that these expectations can be met.

Figure: Three questions at the core of data quality management (From Laura Sebastian-

Coleman, Meeting the Challenges of Data Quality Management, Elsevier 2022)

• Assess Data Quality to determine whether data meets requirements. Assessment can take place as part of data analysis for project work, data quality improvement projects, data quality monitoring, or through data use. Monitoring is not an end-in-itself. Its main goal is to help data consumers by creating a feedback loop to data producers.

• Take Action so that data does meet expectations. Data assessment may clarify data quality requirements or identify new requirements.

Most organizations lack the understanding of their own data, which is a terrible waste of resources. Data quality management helps improve and sustain the data quality that an organization needs to deliver on its mission and serve its customers 

o For data that is critical to the organization’s goals, taking action entails determining the root causes of problems, remediating costly issues, and using insight about root causes to improve business and technical processes,reduce risks, and remove the obstacles to data use. Ideally, it includes implementing controls to prevent future issues.

o For data that is not critical to the organization’s goals, taking action may mean deciding not to take action, and instead choosing to live with identified issues and their associated risks – but doing so intentionally, not accidentally.

In my role at Prudential Financial, Inc. (“PFI”), a US-based global financial services and asset manager, I find these questions help me partner effectively with our business to become a more data driven company.

Asking these questions and helping the organization answer them is necessary to identify improvement opportunities. As importantly, the activities necessary to answer the three questions (setting standards, assessing data, and taking action) will build out explicit knowledge about the organization’s data. Perhaps the biggest benefit is increased awareness of the ways data binds the organization together. This awareness will help people in different parts of the organization work together more productively. Done well, data quality management provides a means of developing organizational self-knowledge through data knowledge.

Prudential Financial Inc. of the United States is not affiliated with Prudential PLC, headquartered in the United Kingdom, or with Prudential Assurance Company, a subsidiary of M&G PLC, also headquartered in the United Kingdom.

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