How can you benefit from a data quality improvement program?

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Data quality is the degree to which your data meets the expectations and requirements of your users, customers, and stakeholders. Poor data quality can lead to inaccurate insights, inefficient processes, lost opportunities, and increased risks. A data quality improvement program is a systematic and continuous effort to enhance the reliability, validity, completeness, consistency, and timeliness of your data. In this article, you will learn how you can benefit from a data quality improvement program in the context of data governance.

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1 Assess your data quality

The first step in a data quality improvement program is to assess the current state of your data quality. You need to identify the sources, types, and uses of your data, as well as the data quality dimensions that are relevant for your business objectives. For example, you may want to measure the accuracy, completeness, validity, consistency, and timeliness of your customer data. You can use various tools and methods to collect, analyze, and report on your data quality metrics, such as data profiling, data audits, data quality dashboards, and data quality scorecards.

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Lindsay Pettai, CKM, A-CSM, CSPO

?? 3x Top LinkedIn Voice | Data ?? Governance Manager | Program Management | Project Management | Change Management PROSCI | Data Governance Leader | Senior Data Governance Manager | Data Governance Program Leader ??
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Identifying data sources, like customer transactions and interactions, and data types, such as purchase history and contact details, sets the foundation. Relevant data quality dimensions, like accuracy and completeness, align with business goals. Employing tools like data profiling reveals inconsistencies, while audits identify data discrepancies. Data quality dashboards and scorecards track metrics, such as timely order processing. This comprehensive assessment informs the strategic enhancement of data quality, ensuring reliable and precise customer information crucial for optimizing business operations and customer experiences.

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2 Define your data quality goals

The next step in a data quality improvement program is to define your data quality goals. You need to establish the desired level of data quality for each data quality dimension, as well as the criteria and indicators to measure your progress. You also need to align your data quality goals with your business goals and priorities, as well as the expectations and requirements of your data users, customers, and stakeholders. For example, you may want to improve the accuracy of your customer data by 10% within six months, and use customer satisfaction surveys and error rates as indicators.

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Christopher Hockey, IGP, CIPP/US

Senior Associate @ Alvarez & Marsal | Expert in Information Risk & Governance | Focused on Client-Centered Information Management Strategies | Your Ally in Navigating Complex Information Challenges
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In my experience, setting data quality goals is an ongoing journey. While establishing goals is important, understanding that data quality is not static is crucial. What constitutes high-quality data today might not be sufficient tomorrow. For instance, as your business evolves, the data that was once considered peripheral might become central to your decision-making process. Your data quality goals need to be flexible and adaptable to these changes. Another aspect to consider is the evolving nature of technology and regulatory landscapes. With advancements in technology, new ways of measuring and improving data quality emerge. Similarly, changes in data protection laws may require adjustments in how you handle certain types of data.

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3 Implement your data quality solutions

The third step in a data quality improvement program is to implement your data quality solutions. You need to identify and prioritize the root causes of your data quality issues, and design and execute the appropriate actions to address them. You can use various techniques and tools to improve your data quality, such as data cleansing, data validation, data standardization, data enrichment, data integration, and data monitoring. You also need to ensure that your data quality solutions are aligned with your data governance framework, policies, standards, and roles.

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4 Monitor and evaluate your data quality outcomes

The fourth step in a data quality improvement program is to monitor and evaluate your data quality outcomes. You need to track and measure the impact of your data quality solutions on your data quality metrics, as well as your business performance and customer satisfaction. You can use various tools and methods to monitor and evaluate your data quality outcomes, such as data quality dashboards, data quality scorecards, data quality reports, and data quality audits. You also need to collect and analyze feedback from your data users, customers, and stakeholders, and identify any gaps or opportunities for improvement.

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5 Communicate and share your data quality results

The fifth step in a data quality improvement program is to communicate and share your data quality results. You need to communicate and share your data quality goals, solutions, outcomes, and feedback with your data users, customers, stakeholders, and management. You can use various channels and formats to communicate and share your data quality results, such as presentations, newsletters, blogs, webinars, and workshops. You also need to celebrate and recognize your data quality achievements, and foster a data quality culture within your organization.

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6 Review and update your data quality program

The sixth and final step in a data quality improvement program is to review and update your data quality program. You need to review and update your data quality program periodically, based on your data quality outcomes, feedback, and changes in your business environment, objectives, and requirements. You also need to ensure that your data quality program is aligned with your data governance framework, policies, standards, and roles, and that you have the necessary resources, skills, and support to sustain and enhance your data quality program.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Mohammed Irfan CDMP, PSMII

Data architect with expertise in data management, data modeling, data quality, data governance, integration, ETL and database management. Bigdata | Teradata | Ab initio.
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In a financial institution, a data quality program yielded transformative benefits. By standardizing processes and cleansing datasets, they minimized errors in financial reports. This not only boosted regulatory compliance but also instilled trust among stakeholders. The improved data quality streamlined decision-making, reducing operational costs and enhancing the institution's reputation. The story highlights how investing in a data quality program not only ensures accuracy but also reaps extensive benefits across compliance, trust-building, and operational efficiency.

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How can you benefit from a data quality improvement program?

1

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3

4

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1 Assess your data quality

2 Define your data quality goals

3 Implement your data quality solutions

4 Monitor and evaluate your data quality outcomes

5 Communicate and share your data quality results

6 Review and update your data quality program

7 Here’s what else to consider

Data Governance

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How can you benefit from a data quality improvement program?

1

2

3

4

5

6

7

1 Assess your data quality

2 Define your data quality goals

3 Implement your data quality solutions

4 Monitor and evaluate your data quality outcomes

5 Communicate and share your data quality results

6 Review and update your data quality program

7 Here’s what else to consider

Data Governance

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