Data Governance Step by Step: Month 3

​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

​Data Governance Executive Presentation

Here's an example agenda for the executive presentation

I had one of the executive prepped up prior to the meeting and had his backing and support during the presentation. After the meeting, you should follow up with the chief executives to answer any questions and to get their feedback on the plan. The data governance lead should also develop a detailed timeline and project plan for establishing the data governance program. One of the key immediate next step is to establish the Data Governance Council (DGC). Solicit recommendations from the executives and host a similar kickoff with the DGC members.

Data Inventory

Catalog and document all data assets within your organization, including sources, formats, and locations. This will be stored within the Data Catalog. Read my blog post on "Data Catalog for Data Governance" for further guidance.

Data Dictionary

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

• Read my blog post on "Data Catalog for Data Governance" for further guidance.

​A data dictionary for data governance is a centralized repository of information about an organization's data. It provides a single source of truth for data definitions, relationships, and usage guidelines. This can help to improve data quality, consistency, and accessibility across the organization.

A data dictionary for data governance should include the following information:

• Data element name: The unique name of the data element.

• Data element definition: A clear and concise definition of the data element, including its purpose, meaning, and acceptable values.

• Data element type: The data type of the data element, such as text, number, date, or time.

• Data element size: The maximum length or size of the data element.

• Data element format: The format of the data element, such as date format, currency format, or number of decimal places.

• Data element constraints: Any constraints on the data element, such as valid values or required fields.

• Data element relationships: The relationships between the data element and other data elements.

• Data element usage guidelines: Guidelines for how the data element should be used, such as who is authorized to access it and how it can be used in reports.

In addition to the above information, a data dictionary for data governance may also include other information such as:

• Data element owner: The person or team responsible for the data element.

• Data element lineage: The history of the data element, including where it comes from, where it is used, and how it has changed over time.

• Data element quality metrics: Metrics that measure the quality of the data element, such as completeness, accuracy, and timeliness.

A data dictionary for data governance can be created and maintained using a variety of tools and technologies, such as spreadsheets, databases, or data governance software. The best tool or technology for the job will depend on the size and complexity of the organization's data environment.

Here are some of the benefits of using a data dictionary for data governance:

• Improved data quality: A data dictionary can help to improve data quality by providing a single source of truth for data definitions and usage guidelines. This can help to reduce errors and inconsistencies in data entry and usage.

• Increased data consistency: A data dictionary can help to increase data consistency by ensuring that data elements are defined and used in the same way across the organization. This can help to improve the accuracy and reliability of reports and analyses.

• Improved data accessibility: A data dictionary can help to improve data accessibility by providing users with a central place to learn about the organization's data. This can help users to find the data they need and to understand how to use it.

• Reduced risk: A data dictionary can help to reduce risk by providing a record of the organization's data assets and how they are used. This can help the organization to comply with regulations and to protect its data from unauthorized access or misuse.

A data dictionary is an essential tool for data governance. It can help organizations to improve data quality, consistency, accessibility, and security.

Finalize the data governance charter

With the feedback and guidance from the DGC, finalize the data governance charter within the month. The charter will have the data governance purpose, mission, benefits, expectations, roles & responsibilities, data governance framework, a typical roadmap and expected long-term governance journey.

• Read my blog post on "Establishing a Data Governance Charter" for further guidance.

​Here is an example of a data governance charter:

Data Governance Charter

Version: 1.0

Date: May-11-2022

1. Purpose

The purpose of this charter is to establish the data governance program for XYZ Corporation. The data governance program will ensure that the organization's data is managed in a consistent and effective manner, and that it is used to support the organization's business goals.

2. Scope

The data governance program will cover all of the organization's data, regardless of its format or location. This includes data that is stored on-premises, in the cloud, or with third-party vendors.

3. Goals and Objectives

The goals and objectives of the data governance program are to:

• Improve the quality, accuracy, and completeness of the organization's data.

• Ensure the security and confidentiality of the organization's data.

• Improve the accessibility and usability of the organization's data.

• Support the organization's compliance with applicable laws and regulations.

4. Roles and Responsibilities

The following are the key roles and responsibilities in the data governance program:

• Data Steward: Responsible for the oversight and management of a specific data asset.

• Data Quality Manager: Responsible for developing and implementing data quality policies and procedures.

• Data Security Officer: Responsible for developing and implementing data security policies and procedures.

• Data Governance Council: Responsible for overseeing the data governance program and making decisions about the program's direction.

5. Policies and Procedures

The data governance program will be implemented and managed in accordance with the following policies and procedures:

• Data Access Policy

• Data Security Policy

• Data Quality Policy

6. Communication and Reporting

The data governance team will communicate with stakeholders on a regular basis about the program's progress. The team will also report to the data governance council on a quarterly basis.

7. Executive Sponsorship

This charter has been reviewed and approved by the executive leadership team of XYZ Corporation.

8. Approval

This charter is approved by:

• [Name]

• [Title]

9. Signature

[Signature]

Define Change Management Process

​Change management in the context of data governance is the process of planning, implementing, and managing changes to data governance practices within an organization. It is essential for achieving the successful adoption of new standards and policies, and for aligning culture and behavior towards a data-driven mindset.

Change management in data governance culture and impact can be defined as the process of enabling people to adopt and implement new data governance practices in a way that is both effective and sustainable. It involves creating awareness and understanding of the changes, providing training and support, and managing resistance.

The following are some key elements of change management for data governance culture and impact:

• Executive sponsorship: Data governance change initiatives require the active support and sponsorship of senior leaders. This helps to ensure that the changes are aligned with the organization's overall goals and strategies, and that they are given the necessary resources and attention.

• Communication: Effective communication is essential for successful change management. It is important to communicate the benefits of the changes, the timeline for implementation, and the roles and responsibilities of stakeholders.

• Training and support: Stakeholders need to be trained on the new data governance practices and provided with the support they need to implement them. This may include training on new tools and technologies, as well as coaching and mentoring.

• Resistance management: It is natural for people to resist change. It is important to anticipate potential sources of resistance and develop strategies for managing them. This may involve addressing concerns, providing clarity, and offering incentives for adoption.

By effectively managing change, organizations can create a data governance culture that supports continuous improvement and enables them to leverage data as a strategic asset.

Here are some specific examples of how change management can be used to manage data governance culture and impact:

• Creating a data governance charter: A data governance charter is a document that outlines the organization's data governance vision, goals, and principles. It is an important tool for communicating the importance of data governance and for getting buy-in from stakeholders.

• Establishing a data governance council: A data governance council is a cross-functional team that is responsible for overseeing the implementation and maintenance of the data governance program. The council can help to ensure that the program is aligned with the organization's overall goals and strategies, and that it is meeting the needs of stakeholders.

• Developing data governance policies and procedures: Data governance policies and procedures provide guidance on how to manage data in a consistent and compliant manner. They can help to improve data quality, accessibility, and security.

• Implementing data governance tools and technologies: There are a variety of data governance tools and technologies available that can help organizations to implement their data governance programs. These tools can help to automate tasks, improve visibility, and track progress.

• Providing training and support to stakeholders: Stakeholders need to be trained on the new data governance policies and procedures, as well as on any new tools and technologies that are being implemented. It is also important to provide ongoing support to stakeholders as they implement the new data governance practices.

Organizations can use change management to create a data governance culture that supports continuous improvement and enables them to leverage data as a strategic asset.

Define Learning Management Process

​Learning management is the process of creating, delivering, and managing learning activities and resources to achieve specific learning outcomes. In the context of data governance culture, data literacy, employee training, and support, learning management can be used to:

• Increase awareness and understanding of data governance: Learning management can be used to develop and deliver training on data governance concepts, principles, and practices. This training can help employees to understand the importance of data governance, their role in supporting it, and how to comply with the organization's data governance policies and procedures.

• Build data literacy skills: Learning management can be used to develop and deliver training on data literacy topics such as data collection, cleaning, analysis, and visualization. This training can help employees to understand and use data to inform their decision-making and to collaborate more effectively with others.

• Provide ongoing support to employees: Learning management can be used to provide employees with access to resources and support as they implement the organization's data governance practices and use data to make better decisions. This support may include access to training materials, job aids, and expert support.

To establish learning management to manage data governance culture, data literacy, employee training, and support, organizations should consider the following steps:

1. Define learning objectives: What do you want employees to learn about data governance, data literacy, and employee training and support? Once you have defined your learning objectives, you can develop training and resources that are aligned with these objectives.

2. Identify target audience: Who needs to learn about data governance, data literacy, and employee training and support? Once you have identified your target audience, you can develop training and resources that are tailored to their specific needs.

3. Select learning delivery methods: There are a variety of learning delivery methods available, such as instructor-led training, e-learning, and on-the-job training. Select the learning delivery methods that are most appropriate for your target audience and your learning objectives.

4. Develop training materials and resources: If you are developing your own training materials and resources, be sure to align them with your learning objectives and target audience. You may also want to consider using existing training materials and resources, such as those offered by industry associations or professional organizations.

5. Implement and monitor your learning management program: Once you have developed your training materials and resources, you need to implement your learning management program. This includes making the training available to employees and tracking their progress. You should also monitor your learning management program to ensure that it is meeting its objectives.

Here are some specific examples of how learning management can be used to manage data governance culture, data literacy, employee training, and support:

• Develop and deliver an online data governance training course: This course could cover topics such as the importance of data governance, the organization's data governance policies and procedures, and how to use data governance tools and technologies.

• Create a data literacy learning library: This library could include articles, videos, and other resources on data literacy topics such as data collection, cleaning, analysis, and visualization.

• Offer regular data governance workshops: These workshops could provide employees with an opportunity to learn about new data governance initiatives and to get answers to their questions.

• Establish a data governance community of practice: This community could provide employees with a forum to share best practices, learn from each other, and get help with data governance challenges.

By implementing a learning management program, organizations can help employees to develop the knowledge and skills they need to support data governance, build data literacy, and make better decisions. This can lead to a more data-driven culture and improved organizational performance.

Sash Barige

May/25/2022