Standardizing Data Governance Implementation - Phase 1/6

The below is to help our Friends, Colleagues and Peers around the Globe and will be released in incremental postings. This format will allow you some time to assimilate and ask questions about the Data Governance Program plan I’ve created.

?

In today's data-driven world, it's more important than ever to have a standardized approach to implementing data governance globally. With so many organizations doing it differently, it can be challenging to get leadership buy-in and maintain credibility as data governance professionals.

?

That's why I'm excited to share my full project plan for implementing a formal data governance program. I believe transparency is key to building trust and credibility, so I'm putting it out there for all to see, ask questions, and add to it.

?

In the plan, I break down the major phases of implementation, with timeframes to give you a base to work from. My team created our own data governance software as a crawl and walk protocol before upgrading to a more established software like Collibra , Informatica , or Alation . Your timeline might look a little different than mine in the technical implementation component later on.

?I'm always looking to build my craft and expand my knowledge, and data governance is just one area I'm passionate about. Let's work together to standardize data governance and bring more value to our organizations. #datagovernance #transparency #collaboration

?

below are the 6 major phases I’ve outlined and I will explain them below in a little more detail.

Phase 1 – Planning and Preparation

Phase 1 - Task 1 - Assess current data landscape

This Task involves conducting a thorough analysis and evaluation of the organization's existing data environment. The primary goal is to gain a comprehensive understanding of the current state of data within the organization.

The information gathered during the "Assess Current Data Landscape" task serves as a foundation for subsequent phases of the data governance program. It helps in formulating appropriate data governance objectives, policies, and frameworks that address the specific needs and challenges identified during this assessment.

***An addition point I will make is... Internal assessments are an easy litmus test but a third party eye will usually give a more expert unbiased opinion. Don't hesitate to go outside your organization for that.

Data Inventory:

Identify and document all the data sources and repositories within the organization. This includes databases, data warehouses, file systems, cloud storage, and any other places where data is stored.

Data Quality Analysis:

Evaluate the quality of the existing data. Assess data accuracy, completeness, consistency, and reliability. Identify any data quality issues that may exist in the current datasets. Keep this to the critical data elements that make your business go or are regulatory.

Data Usage and Access:

Understand how data is currently being used within the organization. Identify the key business processes and activities that rely on data. Determine who has access to different data sets and how data is shared across teams.

Data Governance Maturity:

Assess the current level of data governance maturity within the organization. This includes examining existing data policies, procedures, and the overall governance framework (if any). Identify areas where governance practices may be lacking.

Regulatory Compliance:

Ensure that the organization is in compliance with relevant data protection and privacy regulations. Identify any potential risks or gaps in compliance. This step is crucial, especially if there are regulations such as GDPR, HIPAA, PIPEDA or other industry-specific requirements.

Data Security:

Evaluate the security measures in place to protect sensitive data. Assess access controls, encryption methods, and other security protocols. Identify potential vulnerabilities that may pose a risk to data security.

Stakeholder Interviews:

Conduct interviews with key stakeholders across different departments to gather insights into their data needs, challenges, and expectations. Understand the perspectives of both data producers and data consumers within the organization.

Technology Stack:

Examine the existing technology infrastructure supporting data management. Identify the tools and platforms used for data storage, analytics, and reporting. Assess the compatibility and effectiveness of the current technology stack.

?

Phase1 - Task 2 – Define Data Governance Objectives

This step lays the foundation for the entire data governance initiative and provides a roadmap for how data should be managed within the organization. Here's an explanation of the key steps involved in defining data governance objectives:

The defined data governance objectives serve as guiding principles for the development of policies, frameworks, and implementation plans in subsequent phases of the data governance program. They provide a clear direction for how data should be managed, protected, and leveraged to achieve organizational goals.

Align with Organizational Goals:

Ensure that data governance objectives align with the broader goals and objectives of the organization. Consider how effective data management contributes to overall business success, compliance, and strategic initiatives.

Stakeholder Involvement:

Engage key stakeholders from different departments to understand their priorities and expectations regarding data governance. Incorporate input from business units, IT, legal, compliance, and other relevant areas.

Define Scope and Boundaries:

Clearly define the scope of the data governance program. Determine which types of data, processes, and systems will be covered. Establish boundaries to clarify what falls within the scope of governance and what may be outside its purview.

Establish Key Performance Indicators (KPIs):

Identify measurable KPIs that will be used to assess the success of the data governance program. These KPIs could include data quality metrics, compliance levels, efficiency improvements, and other relevant performance indicators.

Prioritize Data Assets:

Prioritize critical data assets based on their importance to the organization's operations and strategic objectives. This prioritization helps in allocating resources effectively and focusing efforts on the most valuable data.

Risk Assessment:

Conduct a risk assessment to identify potential risks and challenges related to data management. This includes risks associated with data quality, security, compliance, and other factors. Define objectives that address and mitigate these risks.

Define Data Ownership and Accountability:

Clearly define roles and responsibilities for data ownership and accountability. Specify who is responsible for maintaining the quality, security, and integrity of specific datasets. This helps in establishing a clear chain of responsibility.

Ensure Legal and Regulatory Compliance:

Establish objectives that ensure the organization's compliance with relevant data protection and privacy regulations. Define processes for handling sensitive data in accordance with legal requirements.

Promote Data Accessibility and Usability:

Set objectives to enhance data accessibility and usability for authorized users. Ensure that data is readily available to those who need it, while also maintaining proper controls and security measures.

Facilitate Collaboration:

Define objectives that promote collaboration between different departments and stakeholders. Encourage cross-functional communication and cooperation in data-related initiatives.

?

Phase1 - Task 3 – Establish governance team

This team plays a crucial role in defining policies, implementing processes, and ensuring ongoing adherence to data governance principles. Here's an explanation of the key steps involved in establishing a governance team:

The established governance team is instrumental in driving the success of the data governance program. Their collaboration, expertise, and commitment contribute to the effective management and utilization of data within the organization.

?

Identify Key Roles:

Define key roles and positions within the data governance team. Typical roles include a Data Governance Officer, Data Steward(s), Data Custodian(s), and other relevant positions. Clearly outline the responsibilities and expectations for each role.

Leadership Sponsorship:

Obtain sponsorship and support from senior leadership. A senior executive, such as a Chief Data Officer (CDO) or another C-level executive, should provide the necessary authority and resources for the data governance initiative.

Cross-Functional Representation:

Ensure that the governance team includes representatives from different functional areas within the organization. This may include representatives from IT, business units, compliance, legal, and other relevant departments to provide diverse perspectives.

Data Governance Officer (DGO):

Appoint a Data Governance Officer or equivalent leadership role. This individual is typically responsible for leading the governance team, setting the strategic direction, and ensuring alignment with organizational goals.

Data Stewards:

Designate Data Stewards who are responsible for specific data domains or business units. Data Stewards act as custodians of data quality, integrity, and compliance within their assigned areas.

Data Custodians:

Appoint Data Custodians responsible for the technical implementation and management of data assets. These individuals ensure that data is stored, processed, and secured in accordance with governance policies.

Training and Skill Development:

Provide training and skill development opportunities for members of the governance team. Ensure that team members are equipped with the necessary knowledge and skills to fulfill their roles effectively.

Communication and Change Management:

Develop a communication plan to inform the organization about the establishment of the data governance team and its objectives. Implement change management strategies to ensure a smooth transition to the new governance framework.

Define Decision-Making Processes:

Clearly define decision-making processes within the governance team. Establish protocols for resolving disputes, prioritizing initiatives, and escalating issues when necessary.

Collaborate with Existing Committees:

If applicable, collaborate with existing committees or groups related to data management, compliance, or IT governance. Leverage existing expertise and structures to support the data governance program.

Establish Governance Framework:

Work collaboratively to establish the overall governance framework. Define policies, procedures, and guidelines that will guide data management practices across the organization.

Regular Meetings and Reporting:

Schedule regular meetings for the governance team to discuss progress, challenges, and upcoming initiatives. Implement reporting mechanisms to keep stakeholders informed about the status of data governance efforts.

?

If you and your organization have taken these steps you have a great chance of understanding the where you are, what you have, who is going to do it. Then next steps are How are you going to do this? It seems so big! Like I’ve said in previous posts. How do you eat an elephant? You chunk it down of course!

Watch out for Phase 2 of my implementation program in a week or so. Questions, comments or feedback I’m always open for discussion. Drop me a message or comment.