9-months to Data Governance: Step by Step

I'll share a step by step guide on how to implement data governance along with templates of questionnaires, surveys and deliverables. This will be the most comprehensive collection of materials you'll need to setup your data governance program. I'd setup data governance at a healthcare EMR company and a drug development clinical research organization with over 100K combined employees and hundreds of business areas to govern.

Data governance is crucial for organizations as it ensures that data is managed, controlled, and protected adequately. It helps establish a framework for data-related policies, procedures, and standards. Data governance promotes collaboration and transparency across different departments and stakeholders. It also supports data integration, master data management, and data lifecycle management. Data governance is essential for maximizing the value and minimizing the risks associated with organizational data.

​With effective data governance, organizations can ensure data accuracy, quality, and consistency. Data governance helps guarantee that the data used by an organization is accurate, complete, and up-to-date by ensuring data quality. The importance lies in avoiding poor quality data to prevent serious consequences such as financial losses or reputational damage caused by incorrect business decisions.

​Data governance helps protect sensitive data, such as personal information or confidential business information. It is extremely important to protect sensitive data to avoid serious consequences, like data breaches and penalties. Data governance improves data security by implementing measures like access controls and encryption. This is important because data security breaches can lead to financial losses or reputational damage

​Data governance ensures organizations meet compliance requirements for privacy and financial reporting. Failing to comply with these regulations can cause significant penalties or legal action. It also enables compliance with regulations and privacy laws, such as GDPR and CCPA.

​Data governance helps organizations manage and use data for business operations. This includes ensuring that data is accessible to those who need it, as well as being able to track and audit data usage. Data governance facilitates better decision-making by providing reliable and trustworthy data.

Imagine you're walking into a large organization with a mandate to enable data governance. In a series of posts, I'll go through a methodical proven guides to establishing a data governance program. I'll share details on how to get buy-in from senior leadership, how to address setbacks, how to monitor and incremental progress on your data governance journey.

The 9-month step-by-step guide for establishing data governance will be along these lines:

Expect to see variation on the details as I dive deeper into each month's activities. Here are the general activities to consider to plan your monthly milestones.

​Month 1:

1. Identify Business Structure: The business areas and its structure guides the next set of activities. Gather Organization's vision, mission, values. Gather business goals, objectives and current imperatives.

2. Identify Data Influencers: These are members of the business and business operational teams who are the data owners. Conduct a survey or assessment to identify types of data assets managed, key challenges, and opportunities.

3. Assess your organization's readiness: This involves a broader reach to help quantify and qualify the information shared by the data owners. Conduct a survey or assessment to understand your organization's current data management practices, culture, and maturity level. Identify any gaps, challenges, or areas for improvement.

4. Identify current Data Policies: Gather data policies, standards, compliance requirements, regulatory requirements.

5. Assess current data architecture

​Month 2:

1. Identify key data assets and systems: Which data assets are most critical to your business? Which systems are used to collect, store, and manage these data assets?

2. Data Quality Assessment: Evaluate the quality of your data to identify issues like inaccuracies, duplication, and inconsistency. This will be impactful to get buy-in from the executive committee.

3. Identify Project Management Approach: Do you have a PMO (project management office)? Assess project management process used and specific project 'gates' that gates data activities. Identify gaps.

4. Categorize Business Needs and Key Challenges: Based on the surveys and assessments conducted during the previous month, build out use case priority and impact matrix.

5. Establish a data governance framework: This framework should define the roles and responsibilities of key stakeholders, as well as the policies and procedures that will govern the use and management of data.

6. Define Data Governance Roles & Responsibilities

7. Define the scope and goals of your data governance program: What do you want to achieve with data governance? Do you want to improve data quality, enhance compliance, or optimize data utilization? Once you know your goals, you can tailor your implementation plan accordingly. What problems are you trying to solve, and what benefits do you expect to gain? Set clear objectives by defining the goals and objectives of your data governance program. Note, this is at a draft stage at this point. You'll need to review with the governance steering committee to finalize.

8. Data Governance Charter: Develop a draft data governance charter, stakeholder map and policy outline. Review it by the leadership influencers.

9. Data Catalog: Begin developing a data dictionary/catalog.

10. Identify members for Executive Steering Committee

​Month 3:

1. Data Governance Executive Presentation: This step is critical to the success of the program. All the preceding activities and this month's activities are geared toward convincing the senior leadership on the benefits of the data governance and to get them to sponsor and evangelize the program.

2. Data Inventory: Catalog and document all data assets within your organization, including sources, formats, and locations. This will be stored within the Data Catalog.

3. Data Dictionary: Develop a data dictionary to define data terms, standards, and metadata. This will be stored within the Data Catalog.

4. Finalize the data governance charter

5. Define Change Management Process

6. Define Learning Management Process

​Month 4:

1. Develop data quality standards. Data quality standards define the expectations for the accuracy, completeness, consistency, and timeliness of data. By developing and implementing data quality standards, you can ensure that your data is reliable and trustworthy.

2. Develop data principles, policies, and procedures for key areas like data quality, metadata, security, and privacy.

3. Establish Data Governance Council (DGC). With the help of the executive committee and with consultation with business leaders, establish DGC that consists of business leaders who'll take data governance decisions and prioritize data governance activities. This team will be accountable for progressing on the data maturity.

4. Data Governance Council: Form a data governance council that meets regularly to review progress, address issues, and make decisions. DGC Kickoff.

5. Finalize Data Governance Roles & Responsibilities: The governance roles & responsibilities require the input of the DGC to finalize.

6. Data Governance Metrics: Establish KPIs and metrics to measure the effectiveness of your data governance program.

7. Data Governance Tools: Select and implement data governance tools, such as data cataloging, metadata management, and data quality software.

8. Exhibit quick wins: This will help to build on success

​Month 5:

1. Develop a data governance roadmap. A data governance roadmap outlines the steps that you will take to implement and mature your data governance program over time. The roadmap should be aligned with your organization's overall business strategy and goals.

2. Implement data governance processes. These processes should cover the full lifecycle of data, from creation and collection to storage, access, and use. Data governance processes can help to ensure that data is managed consistently and securely across the organization.

3. Data Classification: Implement data classification based on sensitivity and importance.

4. Establish data governance controls. Data governance controls are measures that are put in place to protect data and ensure compliance with policies and procedures. Examples of data governance controls include data access controls, encryption, and audit logging.

5. Train stakeholders. It is important to train all stakeholders on the data governance framework, policies, and procedures. This will help to ensure that everyone understands their roles and responsibilities, and that data is managed consistently across the organization.

​Months 6-9: These are listed in no specific order. I'll go into activity details in my subsequent posts.

• Implement the data governance roadmap: This will involve rolling out the data governance framework, policies, and procedures to all relevant stakeholders. It will also involve implementing data governance controls and training stakeholders.

• Refine policies and procedures based on feedback and metrics.

• Perform risk assessments on critical data assets.

• Establish data retention and archiving policies.

• Develop processes for maintaining data governance artifacts (glossary, policies, procedures, metrics).

• Create plan to expand data governance into new domains or assets.

• Conduct assessment of program effectiveness and roadmap next steps.

• Expand data governance scope to additional business domains or assets.

• Report metrics and progress to executive sponsors and company leadership.

• Support adoption of enhanced data governance practices across the organization.

• Celebrate successes and publicize positive outcomes of data governance program.

• Plan for ongoing sustaining of data governance practices and continuous improvement.

• Monitor and measure data governance performance: It is important to monitor and measure the performance of your data governance program on an ongoing basis. This will help you to identify any areas for improvement and to ensure that your program is meeting its goals. Plan an audit of data governance program effectiveness.

• Data Quality and Compliance Audits: Conduct regular data quality and compliance audits to ensure adherence to data governance policies.

• Feedback and Adaptation: Collect feedback from data stewards, data users, and other stakeholders to continuously improve your data governance program.

• Scaling: As your organization grows and the data landscape evolves, be prepared to scale and adapt your data governance program accordingly.

The specific steps that you need to take to establish data governance will vary depending on your organization's size, industry, and data maturity level. However, by following these steps, you can develop a comprehensive and effective data governance program that will help you to manage and leverage your data assets more effectively.

Sash Barige

4/20/2022