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You’re about to implement a data governance program. How can you ensure it’s successful?

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Data governance is the process of defining and implementing policies, standards, roles, and responsibilities for managing data across an organization. It aims to ensure data quality, security, compliance, and value. However, implementing a data governance program is not a one-time project, but a continuous journey that requires planning, alignment, and evaluation. How can you ensure that your data governance program is successful and delivers the expected benefits? Here are some key steps to follow.

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Vishva Deepak Tripathi, BRMP?, ITIL4?, Databricks

Data & AI ||??? 7x LinkedIn Top Voice ?? || cloud migration & Development || Helping businesses to build data AI…
Valli Nayagam

Associate Director, Data Governance @ Informatica | Data Governance, Data Security

1 Assess your current state

Before you start designing your data governance program, you need to understand your current data landscape, challenges, and opportunities. You can conduct a data assessment to identify the sources, types, volumes, and flows of data in your organization, as well as the data quality issues, risks, and gaps. You can also evaluate the existing data policies, processes, roles, and tools, and how they align with your business goals and regulatory requirements. This will help you define the scope, objectives, and priorities of your data governance program.

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Valli Nayagam

Associate Director, Data Governance @ Informatica | Data Governance, Data Security
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Begin by identifying the business objectives, desired outcomes, key stakeholders, and the necessary data to achieve these objectives. People, Policy, and Process are crucial for Data Governance (DG). Clearly define the goals and objectives of your data governance plan. Initially focus on a subset of the organization and gradually expand to other areas. Evaluate any existing policies, processes, and roles, or develop a DG framework from scratch if necessary. This framework should encompass aspects such as data quality, data security, data privacy, and data sharing, including the possibility of establishing a data marketplace.

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Rajat Bansal, MMA

Strategic Leader - Data & Analytics | Dad | Data Governance | Data Management | Analytics | 18+ yrs exp: Fortune 100 | Queen's University MMA
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The DG success journey begins by defining what success means for that company - in near-term, mid-term and long-term. It is important to define success correctly. Many teams define success as completing N number of DG things across certain areas (e.g. setting up framework, committee, processes, etc.). All this takes a lot of time and effort to say the least, and despite success in achieving those DG goals, often this success remains hollow and under-valued. Why? Because until it is demonstrated that DG is directly contributing in achieving the "targeted" "business" goals, the value of DG remains unproven, unrealized. DG Success = Business Value Delivered Lastly, select the right KPIs to measure the value and success of the DG program.

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2 Define your data governance framework

A data governance framework is a set of guidelines and best practices that govern how data is created, collected, stored, accessed, used, and shared in your organization. It includes the data governance vision, mission, principles, goals, and metrics, as well as the data governance roles and responsibilities, such as data owners, stewards, custodians, and users. It also defines the data governance processes, such as data lifecycle management, data quality management, data security management, and data compliance management. Additionally, it specifies the data governance tools and platforms, such as data catalogs, data dictionaries, data quality tools, and data governance software.

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Vishva Deepak Tripathi, BRMP?, ITIL4?, Databricks

Data & AI ||??? 7x LinkedIn Top Voice ?? || cloud migration & Development || Helping businesses to build data AI strategy with data migration || data Architect - IT || Process Automation || Automation Strategy || GCP
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Framework is an integral part of the process, governed by rules. In today's context, data is akin to oil, serving as the foundation for trade and decision-making. To ensure long-term viability, frameworks must adhere to specific rules and classifications. Categorizing data and establishing roles and regulations for each level simplifies data handling and access. Security and data quality are paramount across all levels. Adherence to standardized rules and defined roles, coupled with accountability, is essential. Data stewardship and transparency are pivotal, requiring every individual to uphold the regulations set by the data governance team.

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3 Establish your data governance council

A data governance council is a cross-functional group of senior leaders and stakeholders who oversee and guide the data governance program. They are responsible for setting the data governance strategy, approving the data governance policies and standards, resolving data governance issues and conflicts, and monitoring the data governance performance and outcomes. The data governance council should meet regularly and communicate effectively with the rest of the organization to ensure alignment and support for the data governance program.

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Vishva Deepak Tripathi, BRMP?, ITIL4?, Databricks

Data & AI ||??? 7x LinkedIn Top Voice ?? || cloud migration & Development || Helping businesses to build data AI strategy with data migration || data Architect - IT || Process Automation || Automation Strategy || GCP
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Establishing a robust data strategy within an enterprise environment requires the active involvement of a data governance council, which assumes a central role. This council is instrumental in identifying problems and proposing solutions to address them. Additionally, it identifies the groups, business units, or applications impacted by proposed changes. Effective communication and collaboration with stakeholders are crucial for emphasizing the importance of these changes and instilling confidence in their implementation.

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4 Implement your data governance policies and standards

Data governance policies and standards are the rules and expectations that regulate how data is handled and used in your organization. They cover topics such as data definitions, data quality, data security, data privacy, data ethics, and data compliance. You need to implement your data governance policies and standards across your data architecture, data systems, data processes, and data culture. You also need to educate and train your data users on the data governance policies and standards, and enforce them through audits, reviews, and feedback.

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5 Monitor and measure your data governance performance

To ensure that your data governance program is effective and impactful, you need to monitor and measure your data governance performance against your predefined goals and metrics. You can use various methods and tools to collect, analyze, and report on your data governance performance indicators, such as data quality scores, data security incidents, data compliance rates, and data value creation. You also need to solicit feedback and input from your data users and stakeholders on their satisfaction and challenges with the data governance program.

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6 Review and improve your data governance program

Data governance is not a static or fixed process, but a dynamic and iterative one that adapts to the changing needs and expectations of your organization and the external environment. You need to review and improve your data governance program periodically to ensure that it remains relevant, efficient, and effective. You can use the data governance performance results and feedback to identify the strengths, weaknesses, opportunities, and threats of your data governance program, and implement corrective and preventive actions to address them. You can also leverage new technologies, trends, and best practices to enhance your data governance program.

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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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You’re about to implement a data governance program. How can you ensure it’s successful?

1

2

3

4

5

6

7

1 Assess your current state

2 Define your data governance framework

3 Establish your data governance council

4 Implement your data governance policies and standards

5 Monitor and measure your data governance performance

6 Review and improve your data governance program

7 Here’s what else to consider

Data Architecture

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You’re about to implement a data governance program. How can you ensure it’s successful?

1

2

3

4

5

6

7

1 Assess your current state

2 Define your data governance framework

3 Establish your data governance council

4 Implement your data governance policies and standards

5 Monitor and measure your data governance performance

6 Review and improve your data governance program

7 Here’s what else to consider

Data Architecture

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