WHAT ARE DATA GOVERNANCE AND MANAGEMENT?

Let’s start with a clear definition of data. Data is defined as qualitative or quantitative information that is collected for reference or analysis, and includes stories, facts, measurements, values, qualities, or observations. Data is a type of structured information.

For the purposes of this Toolkit, we are focusing on data related to Indigenous people’s social and economic well-being (i.e. socioeconomic data). The term “data” will be used throughout the Toolkit in this way.

Your SGIG probably already has some kind of vision or goal statement about hopes for the future, including what “well-being” looks like for the community and its people. Good data governance and management systems are a key tool in helping you work towards that future. It’s important that you also have a vision, principles, and objectives specifically for data governance and management:

• Vision: What you want your data governance and management system to achieve for you
• Principles: Values that guide the direction of your data work
• Objectives: Specific actions to be taken to achieve the vision

Developing these components is about process as much as product. It involves bringing your community and government together to build a foundation and support for data sovereignty. Without this foundation, your data journey will be like having a great car but no clear destination. Or as the saying goes, “If you don’t know where you’re going, any road will get you there.” 

Vision, principles, and objectives are explored later in this section in First Steps: Visioning and Mapping a Path Forward.

As discussed in the Introduction to Information Governance and Management, data governance is a component of information governance because data is a type of information. The term “information” in this context refers to all types of information held by an organization, in formats including oral histories and traditions, paper, electronic records (documents), images, and communications. These formats all include cultural teachings, long ago stories, and ceremonial knowledge.

Your information governance structures will form the foundation for your data governance. Information governance includes the laws, policies, and decision making processes that ensure information (including data) is managed properly. This governance provides the guidance and oversight function.

Although this Toolkit is focused on data, it is important to know that data governance is part of a larger picture of information governance. Much of the work you will do on data governance cannot be done without dealing with broader information governance implications.

The act of governance is an exercise of authority, direction, control, and management. Data governance includes the laws, policies, and decision making processes that ensure data is managed properly. This governance provides guidance and oversight for your data.

The key components of data governance include:

• Decision making body structures and roles (who in the organization has authority to decide what data is collected and managed)
• Support team roles (including job descriptions for roles such as data manager, data analyst, privacy officer)
• Legal/regulatory and policy framework (laws, regulations, and policies that affect the requirements for managing data)
• Accountability mechanisms (who in the organization is responsible for making sure data is kept secure and isn’t misused)
• Key relationships (e.g. relationships between SGIG departments, relationships with Statistics Canada)

For more information about these components, go to Data Governance.

Data management includes all the activities involved with managing data effectively.

The key components of data management include:

• Acquiring data
• Processing and analyzing data
• Communicating data to knowledge users
• Implementing and maintaining technology
• Describing data (documentation and metadata)
• Managing data quality
• Storing and protecting data

For more information about these components, go to Data Management and Acquiring and Working with Data.