Managing Data Quality - Tools Alone Are Not Enough
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Managing Data Quality - Tools Alone Are Not Enough

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

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

Many people think of data quality as a purely technical problem that can be solved through tools. But it is not as simple as that. Data represents the activities of an organization – its business processes executed through its technology. The enterprise is an overall system that produces, obtains, and uses data.

An enterprise is like the human body: a cohesive overall entity, comprising a set of systems and processes that turn inputs into outputs, bring about growth, and enable the enterprise to perpetuate itself and meet its goals. To understand the body, we view it through the lens of sub-systems (respiratory, digestive, circulatory, and so on). Each bodily system can be understood as a thing in itself, with tissues, organs, and distinct functions. Each can also be understood in relation to the other systems of the body.

Within an organization, data is not only a thing itself; it is also the means by which different parts of the organization interact. Recognition of the role data plays is likely the origin of assertions that data is the “life blood” of the or ganization.

Data quality management should be understood in relation to the goals and overall strategy of an organization, its management and culture, and its business processes and its technical architecture. Knowledge of the enterprise and its goals should prompt data quality practitioners to ask better questions about context and better understand the size and shape of any given data-related challenge in context. This perspective also helps practitioners apply a range of tools and methodologies as they address challenges of different shapes and sizes. I’ve experienced this firsthand through my work with data at many organizations and I apply these principles in my current role at Prudential Financial, Inc. (“PFI”), a US-based global financial servicescompany. Asking better questions is allowing us to tap into 145 years of data to create better solutions to the financial challenges of our consumers.

Many people think of data quality as a purely technical problem that can be solved through tools.

Whatever their specific work, most organizations face five challenges in managing data quality. These challenges involve data itself, the processes, people, and technology connected to data, as well as the organization’s cultural practices and behaviors toward data. (See Figure 2). It is critical for any organization to manage people, processes, and technology. Data presents specific challenges for each and for the interactions between them. I refer to these challenges as:

Data – The Meaning Challenge: Understanding how data encodes information about objects, events, and people. Data has human-created, technologically-imposed, and “organic” properties.

• People make choices about how to create data (what characteristics to represent, how to represent them)

• MTechnical requirements for storing and using data constrain those choices

• Data production and uses change over time

These conditions affect how people interpret and use data. Meeting the meaning challenge requires knowledge of “data-as-data.”

Process – The Quality Challenge: Helping people within the organization see the connection between process quality and data quality. Data is both an input to and an output of organizational processes. Meeting the process challenge requires that processes can be defined and controlled so they result in higher-quality, more reliable data.

Technology – The Balance Challenge: Understanding the role of technology in creating and managing data. Data is both dependent on and independent from technology. Choices about technology impact the creation, accessibility, the use of data, and, ultimately, its quality. Meeting the technology challenge requires balance between data requirements and the desire for ever-new technology. People – The Literacy Challenge: Ensuring that data producers and data consumers build knowledge, develop skills, and have access to the information they need to use and interpret the organization’s data.

Culture and Organization – The Accountability Challenge: Establishing accountability for data and leadership commitment to quality to get value from data. This involves more than lip service. It requires culture change; for example, establishing responsibility for data quality along the data supply chain and implementing enterprise oversight of data via formal data governance practices.

These challenges are interconnected. Knowledge of data-as-data is fundamental to both data literacy and collaboration. The processes and technologies an organization uses to get its work done reflect its culture. Changing them to produce better data requires changing the culture. Defining accountability for data and desired behaviors around data requires understanding how data works in general, as well as how it is created and used by people and integrated into processes via technology .

Success in meeting the other four challenges is directly dependent on addressing the accountability challenge. People in organizations will not change their behaviors, increase their understanding of data, make process improvements, or adopt technologies to support high-quality data unless leadership recognizes data as a valuable asset and treats it as such.

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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