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How can you scale your data quality assessment process for changing requirements?

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Data quality assessment is the process of evaluating the accuracy, completeness, consistency, and validity of your data sets. It is essential for ensuring that your data-driven decisions are based on reliable and relevant information. However, as your data sources, volumes, and needs change, your data quality assessment process may need to scale accordingly. How can you do that effectively and efficiently? Here are some tips to help you.

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1 Automate your data quality checks

One way to scale your data quality assessment process is to automate your data quality checks as much as possible. This means using tools and scripts that can perform data quality checks on a regular basis, such as data profiling, data cleansing, data validation, and data monitoring. Automation can help you save time, reduce errors, and ensure consistency in your data quality assessment process. However, automation also requires careful planning, testing, and maintenance to ensure that your data quality checks are accurate and relevant.

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Samarjit Luthra

Certified Six Sigma Black Belt and Lean consultant with a proven ability to deliver results
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We should start with Defining the scope and objectives of the Data quality assessment (DQA) . What are the data sources, indicators, and quality attributes that we want to assess? How often do we need to conduct the DQA? What are the expected outcomes and benefits of the DQA?

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2 Define your data quality metrics and thresholds

Another way to scale your data quality assessment process is to define your data quality metrics and thresholds clearly and consistently. Data quality metrics are the measures that you use to evaluate the quality of your data, such as accuracy, completeness, consistency, validity, timeliness, and usability. Data quality thresholds are the acceptable levels or ranges of your data quality metrics, such as the percentage of missing values, the number of duplicates, or the frequency of updates. Defining your data quality metrics and thresholds can help you establish your data quality standards, goals, and expectations, as well as identify and prioritize your data quality issues.

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Samarjit Luthra

Certified Six Sigma Black Belt and Lean consultant with a proven ability to deliver results
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Identify the data quality metrics and methods that we will use to measure and evaluate the data quality. We can use data profiling, data cleansing, data validation, and data mapping techniques to identify and resolve data quality issues. We can also use data quality dimensions such as consistency, accuracy, completeness, auditability, orderliness, uniqueness, and timeliness to assess the data quality.

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3 Document your data quality assessment process

A third way to scale your data quality assessment process is to document your data quality assessment process thoroughly and transparently. Documentation can help you communicate your data quality assessment process to your stakeholders, such as your data owners, data users, data analysts, and data consumers. Documentation can also help you track your data quality assessment activities, results, and actions, such as your data quality checks, data quality reports, data quality issues, and data quality improvements. Documentation can also help you review and refine your data quality assessment process over time, as your data requirements change.

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Samarjit Luthra

Certified Six Sigma Black Belt and Lean consultant with a proven ability to deliver results
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Develop a data quality assessment checklist or framework that summarizes the data quality standards, metrics, methods, and procedures that we will follow. We can also include the roles and responsibilities of the data quality team, the data quality tools and software that we will use, and the data quality documentation and reporting that we will produce.

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4 Integrate your data quality assessment process with your data governance framework

A fourth way to scale your data quality assessment process is to integrate your data quality assessment process with your data governance framework. Data governance is the set of policies, procedures, roles, and responsibilities that govern how you manage, use, and share your data across your organization. Integrating your data quality assessment process with your data governance framework can help you align your data quality objectives with your business objectives, ensure compliance with your data quality standards and regulations, and foster a culture of data quality awareness and accountability.

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5 Collaborate with your data stakeholders

A fifth way to scale your data quality assessment process is to collaborate with your data stakeholders throughout your data quality assessment process. Data stakeholders are the people who have an interest or a stake in your data, such as your data owners, data users, data analysts, and data consumers. Collaborating with your data stakeholders can help you understand their data needs, expectations, and feedback, as well as involve them in your data quality assessment activities, such as data quality checks, data quality reports, data quality issues, and data quality improvements. Collaborating with your data stakeholders can also help you build trust, credibility, and value in your data quality assessment process.

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6 Learn from your data quality assessment process

A sixth way to scale your data quality assessment process is to learn from your data quality assessment process continuously and systematically. Learning from your data quality assessment process means analyzing your data quality assessment results, actions, and outcomes, as well as identifying your data quality assessment strengths, weaknesses, opportunities, and threats. Learning from your data quality assessment process can help you improve your data quality assessment process, as well as your data quality performance, by applying your data quality assessment insights, lessons, and best practices.

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Samarjit Luthra

Certified Six Sigma Black Belt and Lean consultant with a proven ability to deliver results
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Review the data quality assessment results and findings and identify the areas for improvement. We can use root cause analysis, gap analysis, and impact analysis techniques to understand and prioritize the data quality issues and their implications.

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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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Manas Mishra

Data Governance & Data Quality Evangelist
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I would like to add that a good data quality team (especially the working group) will always work in a self-destructive mode; they will strive to bring about the cultural and process change that will enable and equip the concerned stakeholders (including people with no "data" in their title) in the organization to imbibe the data quality assessment processes and methodologies into their BAU." Thus, a good data quality team's objective is to "lead by example" and set the stage for all the relevant stakeholders to run the show on their own

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Samarjit Luthra

Certified Six Sigma Black Belt and Lean consultant with a proven ability to deliver results
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Implement the data quality improvement actions and monitor the data quality performance. We can use data quality management, data governance, and data stewardship practices to ensure that the data quality is maintained and improved over time.

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How can you scale your data quality assessment process for changing requirements?

1

2

3

4

5

6

7

1 Automate your data quality checks

2 Define your data quality metrics and thresholds

3 Document your data quality assessment process

4 Integrate your data quality assessment process with your data governance framework

5 Collaborate with your data stakeholders

6 Learn from your data quality assessment process

7 Here’s what else to consider

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How can you scale your data quality assessment process for changing requirements?

1

2

3

4

5

6

7

1 Automate your data quality checks

2 Define your data quality metrics and thresholds

3 Document your data quality assessment process

4 Integrate your data quality assessment process with your data governance framework

5 Collaborate with your data stakeholders

6 Learn from your data quality assessment process

7 Here’s what else to consider

Analytical Skills

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感谢您的反馈

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