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Last updated on 2024年8月25日

How do you follow data quality and governance principles?

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Data quality and governance are essential aspects of any business analysis project, especially when it involves data analysis and visualization. They ensure that the data you use and produce is accurate, consistent, reliable, secure, and compliant with relevant standards and regulations. But how do you follow data quality and governance principles in your work? Here are some tips and best practices to help you.

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1 Assess data sources and needs

Before you start collecting, processing, or analyzing data, you need to understand where it comes from, what it represents, how it is structured, and what it is used for. This will help you identify the data quality dimensions that matter most for your project, such as completeness, validity, timeliness, accuracy, and relevance. You also need to define the data requirements and expectations of your stakeholders, such as the business goals, the data analysis questions, the data visualization formats, and the data delivery methods.

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Kartik Sonar

Operations & SCM and CAE Enthusiast | Lean Six Sigma | PowerBI | SQL | Product Specialist | SME | NITIE (IIM Mumbai) || Abroad | Excellence | APAC, EMEA, NAM, Nordics | CPG | Ex - Sterlite, Varroc, Canpack |} TCS
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Understanding data's origins, implications, structure, and intended purpose is essential before you begin gathering, manipulating, or evaluating it. This will assist you in determining which aspects of data quality—such as precision, validity, timeliness, correctness, and relevance—are most important for your project. Along with defining the business objectives, data analysis questions, data visualization formats, & data delivery strategies, you also need to specify the expectations & requirements of your stakeholders with regard to the data.

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Rafi Chowdhury

Senior Business Analyst | Digital Transformation | Data-Driven Strategy | CRM & Marketing Automation | Agile/Scrum | SQL | SEO & Analytics | Process Reengineering | Salesforce, Marketo, HubSpot | Project Management (PM)
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To follow data quality and governance principles, start by establishing clear data standards and policies, ensuring consistency and accuracy. Implement robust data validation processes to detect and correct errors early. Regularly audit data for compliance with governance frameworks and maintain comprehensive documentation. Promote a culture of data stewardship, where all team members understand their role in maintaining data integrity. Use automated tools for data monitoring and enforce access controls to protect sensitive information, ensuring adherence to regulations and best practices.

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2 Implement data quality checks and controls

Once you have a clear picture of your data sources and needs, you need to ensure that the data you handle meets the quality standards and criteria you have set. This means that you need to implement data quality checks and controls throughout the data lifecycle, from acquisition to analysis to visualization. For example, you can use data profiling tools to inspect and evaluate the data for quality issues, such as missing values, outliers, duplicates, or inconsistencies. You can also use data cleansing tools to correct or remove the errors and improve the data quality. Additionally, you can use data validation tools to verify that the data conforms to the rules and formats you have defined.

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3 Document and communicate data quality results

Data quality is not only a technical issue, but also a business and communication issue. You need to document and communicate the data quality results to your stakeholders, such as the data owners, the data consumers, the data analysts, and the data visualizers. This will help them understand the strengths and limitations of the data, as well as the assumptions and decisions you have made based on the data quality assessment. You can use data quality reports, dashboards, or metadata to document and communicate the data quality results, such as the data quality metrics, the data quality issues, the data quality actions, and the data quality impacts.

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Kartik Sonar

Operations & SCM and CAE Enthusiast | Lean Six Sigma | PowerBI | SQL | Product Specialist | SME | NITIE (IIM Mumbai) || Abroad | Excellence | APAC, EMEA, NAM, Nordics | CPG | Ex - Sterlite, Varroc, Canpack |} TCS
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Following these - leverages the effective communication strategies, organizations that significantly improves data quality, build trust in data, and drive better decision-making. Data owners, consumers, analyzers, and visualizers are just a few of the stakeholders must be inform and document the outcomes of the data quality assessments. They will gain an understanding of the data advantages and disadvantages as well as presumptions and choices irrespective of the data quality assessment. To record and convey the data quality outcomes, including the data quality measurements, the data quality issues, the data quality actions, and the data quality consequences, one can use the data quality reports, dashboards, or metadata.

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4 Establish data governance roles and responsibilities

Data governance is the set of policies, processes, and standards that govern how data is created, collected, stored, accessed, used, and shared in an organization. Data governance ensures that data is managed in a way that supports the business objectives, complies with the legal and ethical obligations, and protects the data assets and stakeholders. To follow data governance principles, you need to establish clear data governance roles and responsibilities for your project, such as who owns the data, who can access the data, who can modify the data, who can approve the data changes, and who can monitor the data quality and compliance.

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Kartik Sonar

Operations & SCM and CAE Enthusiast | Lean Six Sigma | PowerBI | SQL | Product Specialist | SME | NITIE (IIM Mumbai) || Abroad | Excellence | APAC, EMEA, NAM, Nordics | CPG | Ex - Sterlite, Varroc, Canpack |} TCS
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1) Develop a comprehensive data governance framework: Define policies, procedures, and standards for data management. 2) Create a data governance committee: Establish a cross-functional team to oversee data governance initiatives. 3) Communicate data governance policies: Ensure all stakeholders understanding respective roles and responsibilities.

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5 Follow data governance rules and guidelines

Once you have defined the data governance roles and responsibilities, you need to follow the data governance rules and guidelines that apply to your project. These rules and guidelines may vary depending on the type, source, and purpose of the data, as well as the industry, domain, and organization you work in. For example, you may need to follow the data security and privacy rules, such as encrypting the data, anonymizing the data, or obtaining the data consent. You may also need to follow the data ethics and transparency rules, such as avoiding the data bias, disclosing the data sources, or explaining the data analysis methods.

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Kartik Sonar

Operations & SCM and CAE Enthusiast | Lean Six Sigma | PowerBI | SQL | Product Specialist | SME | NITIE (IIM Mumbai) || Abroad | Excellence | APAC, EMEA, NAM, Nordics | CPG | Ex - Sterlite, Varroc, Canpack |} TCS
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>Evaluating the kinds of information that your company gathers and retains. >Measuring data quality metrics - specifically for assessing data accuracy, accomplishment, consistency, timeliness, etc. >Implement Data Quality Tools: Use software solutions to automate data cleansing, validation, and profiling. >Training & Development / Talks: Deriving employees on various data quality defining best practices and the importance of data governance. >Ensuring data governance framework aligned with business objectives and regulatory needful.

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6 Review and improve data quality and governance

Data quality and governance are not static, but dynamic and evolving. You need to review and improve your data quality and governance practices regularly, as the data, the business, and the environment change over time. You can use data quality and governance audits, reviews, or feedback to evaluate and measure your data quality and governance performance, such as the data quality level, the data quality improvement, the data governance compliance, or the data governance maturity. You can also use data quality and governance improvement plans, actions, or projects to address and resolve the data quality and governance gaps, issues, or risks you have identified.

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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 follow data quality and governance principles?

1

2

3

4

5

6

7

1 Assess data sources and needs

2 Implement data quality checks and controls

3 Document and communicate data quality results

4 Establish data governance roles and responsibilities

5 Follow data governance rules and guidelines

6 Review and improve data quality and governance

7 Here’s what else to consider

Business Analysis

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How do you follow data quality and governance principles?

1

2

3

4

5

6

7

1 Assess data sources and needs

2 Implement data quality checks and controls

3 Document and communicate data quality results

4 Establish data governance roles and responsibilities

5 Follow data governance rules and guidelines

6 Review and improve data quality and governance

7 Here’s what else to consider

Business Analysis

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