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You’re in charge of data governance. How do you avoid the biggest risks?

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Data governance is the process of managing the quality, security, accessibility, and usability of your organization's data assets. As a data governance leader, you have a lot of responsibilities and challenges to face. How can you ensure that your data governance strategy is effective, efficient, and aligned with your business goals? Here are some tips to help you avoid the biggest risks in data governance.

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Chiran Kumar Pola

Senior Manager - Data Operations | IIM - AIML | DAMA - CDMP | CSPO | CSM | Data Governance & Stewardship | AI…

1 Understand your data landscape

The first step in data governance is to have a clear and comprehensive understanding of your data landscape. This means knowing what data sources you have, where they are located, how they are connected, who owns them, and what policies and standards apply to them. You can use tools like data catalogs, data dictionaries, data lineage, and data quality assessments to document and visualize your data landscape. This will help you identify gaps, issues, and opportunities for improvement in your data environment.

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2 Define your data roles and responsibilities

The second step in data governance is to define your data roles and responsibilities. This means assigning clear and accountable ownership and stewardship of your data assets, as well as defining the rights and obligations of your data consumers and producers. You can use tools like data governance frameworks, data governance committees, and data governance policies to establish and communicate your data roles and responsibilities. This will help you foster a culture of collaboration, trust, and accountability among your data stakeholders.

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3 Implement your data processes and controls

The third step in data governance is to implement your data processes and controls. This means establishing and enforcing the rules and standards for how your data is created, collected, stored, transformed, shared, and used. You can use tools like data quality rules, data validation, data security, data privacy, and data audits to implement and monitor your data processes and controls. This will help you ensure that your data is accurate, consistent, secure, compliant, and fit for purpose.

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Anbarasan Dhanushkodi

Data Architect | Data Consultant | Data Engineer
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I would also consider, depending on the country you are operating on, the government regulatory requirements such as Artcle 30 of GDPR in EU which describes the need of Record of Processing activities shortly called as RoPA.

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4 Enable your data capabilities and outcomes

The fourth step in data governance is to enable your data capabilities and outcomes. This means providing and supporting the tools, platforms, and skills that your data users need to access, analyze, and leverage your data for decision making and value creation. You can use tools like data warehouses, data lakes, data integration, data visualization, and data literacy to enable and enhance your data capabilities and outcomes. This will help you drive innovation, efficiency, and growth with your data.

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5 Monitor and improve your data performance

The fifth step in data governance is to monitor and improve your data performance. This means measuring and reporting on the quality, availability, usage, and impact of your data assets, as well as identifying and resolving any issues or risks that arise. You can use tools like data dashboards, data metrics, data feedback, and data reviews to monitor and improve your data performance. This will help you demonstrate the value and benefits of your data governance efforts, as well as adjust and optimize your data governance strategy as needed.

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6 Delegate and empower your data team

The sixth and final step in data governance is to delegate and empower your data team. This means trusting and supporting your data owners, stewards, analysts, and users to take ownership and responsibility of their data tasks and goals, as well as providing them with the resources and guidance they need to succeed. You can use tools like data training, data coaching, data recognition, and data incentives to delegate and empower your data team. This will help you build a strong and sustainable data governance culture and capability in your organization.

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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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Chiran Kumar Pola

Senior Manager - Data Operations | IIM - AIML | DAMA - CDMP | CSPO | CSM | Data Governance & Stewardship | AI Governance - Responsible AI | Data Vendors Management |
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To avoid the biggest risks in data governance, define clear roles and responsibilities, establish comprehensive policies and procedures, implement data quality controls, conduct regular data audits, enforce data security measures, provide ongoing training, and establish data governance oversight. These strategies help ensure accountability, data accuracy, compliance, security, and awareness, mitigating risks associated with data governance.

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Hussam AbdulWahab

Data Strategy & Governance - Senior Data Scientist (DASCA), Enterprise Architect (TOGAF) , CIPM , CDMP , Digital Transformation Practitioner
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Compliance is one of biggest risks that touch the organization core businesses so Ensure compliance with relevant regulations like GDPR, HIPAA, etc., through regular audits and update is maintained and avoid put the organization in critical risks by applying and follow non compliance practices for your data governance framework .

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You’re in charge of data governance. How do you avoid the biggest risks?

1

2

3

4

5

6

7

1 Understand your data landscape

2 Define your data roles and responsibilities

3 Implement your data processes and controls

4 Enable your data capabilities and outcomes

5 Monitor and improve your data performance

6 Delegate and empower your data team

7 Here’s what else to consider

Data Governance

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You’re in charge of data governance. How do you avoid the biggest risks?

1

2

3

4

5

6

7

1 Understand your data landscape

2 Define your data roles and responsibilities

3 Implement your data processes and controls

4 Enable your data capabilities and outcomes

5 Monitor and improve your data performance

6 Delegate and empower your data team

7 Here’s what else to consider

Data Governance

给文章评分

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