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How do you improve data quality assessment?

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Data quality assessment is the process of measuring and evaluating the accuracy, completeness, consistency, timeliness, and validity of data. It is a crucial step for ensuring data governance, which is the practice of managing data as a strategic asset and ensuring its quality, security, and usability. However, data quality assessment can be challenging, especially when dealing with large, complex, and diverse data sets. How do you improve data quality assessment and make it more efficient and effective? Here are some tips and best practices to follow.

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George Firican

?? Award Winning Data Governance Leader | DataVenger | Founder of LightsOnData | Podcast Host: Lights On Data Show |…

1 Define data quality dimensions and criteria

Before you can assess data quality, you need to define what quality means for your data and how you will measure it. Data quality dimensions are the aspects of data that affect its fitness for use, such as accuracy, completeness, consistency, timeliness, and validity. Data quality criteria are the specific standards or rules that you will use to evaluate each dimension, such as acceptable error rates, missing values, data formats, or data sources. Defining data quality dimensions and criteria will help you establish clear and consistent expectations and goals for your data quality assessment.

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George Firican

?? Award Winning Data Governance Leader | DataVenger | Founder of LightsOnData | Podcast Host: Lights On Data Show | LinkedIn Top Voice 2024
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Here is a list of dimensions of data quality that you can consider for your needs: - Accuracy - Accessibility - Completeness - Consistency - Currency (and/or Timeliness depending on your definition) - Integrity - Precision - Representation - Validity

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2 Use a data quality framework and methodology

A data quality framework is a set of principles, processes, roles, and tools that guide and support data quality assessment and improvement. A data quality methodology is a systematic and structured approach to implementing the framework and conducting data quality assessment. Using a data quality framework and methodology will help you plan, execute, monitor, and report your data quality assessment activities and results. It will also help you align your data quality assessment with your data governance strategy and objectives.

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3 Apply data quality tools and techniques

Data quality tools and techniques are the software and methods that you use to perform data quality assessment tasks, such as data profiling, data cleansing, data validation, data monitoring, and data reporting. Data quality tools and techniques can help you automate, streamline, and enhance your data quality assessment process and outcomes. They can also help you identify, correct, prevent, and communicate data quality issues and opportunities. Some examples of data quality tools and techniques are data quality dashboards, data quality scorecards, data quality rules engines, data quality audits, and data quality surveys.

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4 Involve data stakeholders and users

Data stakeholders and users are the people who have an interest or a role in the creation, management, or consumption of data. They can include data owners, data stewards, data analysts, data scientists, business users, and customers. Involving data stakeholders and users in your data quality assessment will help you gain insights, feedback, and support from different perspectives and needs. It will also help you ensure that your data quality assessment is relevant, useful, and actionable for your data stakeholders and users.

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George Firican

?? Award Winning Data Governance Leader | DataVenger | Founder of LightsOnData | Podcast Host: Lights On Data Show | LinkedIn Top Voice 2024
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In my opinion, everyone is a data stakeholder and data quality is everyone's responsibility. In order for people to care, one should always address their WIIFM (What's In It For Me). Tie the data quality issues back to the organization's goals and needs, but also that of the unit and even the individual's.

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5 Implement data quality improvement actions

Data quality assessment is not an end in itself, but a means to an end: data quality improvement. Data quality improvement is the process of applying changes and enhancements to your data and data processes to increase their quality and value. Implementing data quality improvement actions will help you address and resolve the data quality issues and gaps that you identified in your data quality assessment. It will also help you achieve and maintain your data quality goals and standards.

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6 Review and refine your data quality assessment process

Data quality assessment is not a one-time event, but a continuous and iterative process. Data quality is not static, but dynamic and evolving. Therefore, you need to review and refine your data quality assessment process regularly and adapt it to the changing data environment and needs. Reviewing and refining your data quality assessment process will help you improve your data quality assessment performance and efficiency. It will also help you ensure that your data quality assessment is aligned with your data governance strategy and objectives.

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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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How do you improve data quality assessment?

1

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3

4

5

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1 Define data quality dimensions and criteria

2 Use a data quality framework and methodology

3 Apply data quality tools and techniques

4 Involve data stakeholders and users

5 Implement data quality improvement actions

6 Review and refine your data quality assessment process

7 Here’s what else to consider

Data Governance

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How do you improve data quality assessment?

1

2

3

4

5

6

7

1 Define data quality dimensions and criteria

2 Use a data quality framework and methodology

3 Apply data quality tools and techniques

4 Involve data stakeholders and users

5 Implement data quality improvement actions

6 Review and refine your data quality assessment process

7 Here’s what else to consider

Data Governance

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