Section 3: Data Governance

Section Overview

This section aims to provide an understanding of what data governance is and how it supports data literacy. The importance of data governance is highlighted and the concept of data architecture and the role of a data architect is explored.

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Data Governance

Data governance is the set of authorities, accountabilities, and controls to formalize and consistently guide the management of data assets. Data governance and data management are not the same thing. Data governance establishes how data should be managed through the creation of policies, standards, etc., while data management enacts those policies and standards to gather and use data. It is the organizational structures, data owners, policies, processes, and controls for the beginning to the end lifecycle of data. Data management is the implementation of data governance and enables the execution and controls to achieve data governance. Data governance does not exist without data management and vice versa; data governance without implementation is just documentation.

Consider the following definition from Fircan (2021b):

Data governance is “a discipline which provides the necessary policies, processes, standards, roles, and responsibilities needed to ensure that data is managed as an asset”.

In essence, data governance is the creation of consistent processes to define and provide the steps to clean data, make it accessible to the right individuals, and ensure that it is secure.

Watch the video below to learn more about the difference between data management and data governance:

In contrast to data governance, data management is the “business function of planning for, controlling, and delivering data” (Fircan, 2021b).

In essence, it is the act of implementing processes using technologies, the act of ensuring the proper management of access as part of the responsibilities of individuals in their role, etc.

Data governance establishes a mechanism to manage, control, and oversee data asset management practices. It includes defining the policies, standards, and guidelines for data management, monitoring, evaluating, and improving data asset management practices, as well as enabling practices through knowledge exchange and translation.

Data Governance Differs from Information Governance

As outlined in Section 1: Introduction to Data and Data Literacy, data are facts and statistics in their raw form. Information is data that has been processed, interpreted, organized, and presented to make it meaningful. Data is used in the creation of information.

The Data, Information, Knowledge, Wisdom Pyramid represents the relationship between data, information, knowledge, and wisdom. Each step up the pyramid adds value to the initial data. The more data is enriched with meaning and context, the more information is derived from it, which increases knowledge and creates wisdom. At the top of the pyramid, knowledge and insights have been turned into a learning experience. This guides our actions, which creates wisdom.

Similarly, data governance differs from information governance in that it is specific to the governance of data, not information. Data governance is specific to the governance of data and does not include an approach to governing information. The term data is not used interchangeably with the term information. The analysis, interpretation, and presentation of data to tell a story makes it information. Data governance pertains only to data; information governance pertains to information. Data governance is the subdomain of information governance that provides for the design and execution of data planning and data quality assurance to achieve the strategic information needs of an organization.

Why is Data Governance Important?

Data is an important asset for all organizations and requires active management. Data governance helps to ensure that data is usable, accessible, and protected. Effective data governance leads to better data analytics, which in turn leads to better decision-making and improved operations support. It helps to avoid data inconsistencies or errors, which can lead to poor decision-making or integrity issues.

Data governance ensures that data is:

• Secured: Data is protected from unauthorized access or tampering;
• Trustworthy: Data is correct and consistent;
• Documented: Data is described to make it easier to identify, use, retrieve, and manage;
• Managed: Data is considered a valuable resource and reliable methods are used to access, cleanse, store, and use data; and
• Audited: Data is examined to ensure quality, proper access, and storage.

Data governance also plays an essential role in regulatory compliance, ensuring that organizations are consistently compliant with all levels of regulatory requirements. This is key for minimizing risks and reducing operational and reputational costs.

At its core, data governance leads to improved data quality, decreased data management costs, and increased access to data for all stakeholders. The result is better decision-making and better organizational outcomes.

Key Functions to Enable the Delivery of Data Governance

There are many approaches to developing a data governance program within an organization. In order to better understand the application of data governance within an organization, as well as some of the key roles involved, let’s start with an example of a possible structure for the delivery of data services within an organization.

Data Governance Steering Committee

To provide strategic direction for the management and use of data, a data governance committee is established. The committee acts as the oversight body for strategic data management initiatives that are created to deliver organizational objectives.

The strategy for, and organizational-wide approach for, ensuring that governance programs span the whole corporation generally starts with a steering committee comprised of senior management. Templar (2017), author of Get Governed: Building World Class Data Governance Programs, says steering committee members’ responsibilities include setting the overall governance strategy with specific outcomes, championing the work of data stewards, and holding the organization accountable to timelines and outcomes.

This is an example of a model for data governance:

Data Owners

Templar (2017) says data owners are individuals responsible for ensuring that information within a specific data domain is governed across systems and lines of business. They are generally members of the steering committee. Data owners are responsible for activities such as:

• Approving data glossaries and other data definitions;
• Ensuring the accuracy of information across the enterprise;
• Directing data quality activities;
• Working with other data owners to resolve data issues; and
• Providing the steering committee with input on software solutions or policies.

Data Services Department

A data services department may be created within an organization to ensure that data is managed as a strategic asset throughout the organization. Data services departments are the doers. They enable the strategic direction provided by the data governance steering committee. It is the mandate of data services to:

• Ensure that trusted data is used to support organizational objectives;
• Ensure that data is used and managed in a secure and privacy-sensitive manner; and
• Ensure that data assets drive value across the organization.

Below is an example of a Data Services Department structure. This is only one example for illustration purposes – Data Services Departments can be structured in various ways.

The three functions defined above are ultimately responsible for ensuring that data assets are secured, trustworthy, documented, managed, and audited, and for meeting the strategic objectives of the data governance steering committee, all of which comprise the delivery of successful data governance.

Data governance ensures that data captured and the subsequent information derived is useful, available, and secure. Good data governance equates to better quality data, improved data analytics, and leads to evidence-based decision-making. It also helps to prevent inconsistencies or errors in data, which can result in data integrity concerns, lack of trust in the data, and poor decision-making.

In the video below, Professor George Fircan details his learning journey from a data governance perspective:

Data ethics involves the knowledge that allows a person to acquire, use, interpret, and share data in an ethical manner including recognizing legal and ethical issues (e.g., biases, privacy) (Statistics Canada, 2020). It is important because it provides parameters for what organizations can and cannot do with the data they collect. In this article, Dr. Mark van Rijmenam (2014) writes about the four guidelines organizations should adhere to regarding the ethical use of data: (1) Radical transparency; (2) Simplicity by design; (3) Preparation and security; and (4) Privacy.

In this blog, Stephen Watts (2020) speaks to the issues of data governance. His key messages focus on the importance of a high-quality governance strategy, what this involves, who needs to be involved within an organization, and implementing strategies to ensure data is used properly, securely, and in compliance with regulatory requirements.

A Key Facet in the Delivery of Data Governance: Data Architecture and the Role of a Data Architect

Data architecture is a one of the key facets of data governance and serves as a foundation for the delivery of a data governance model. The concepts of data governance and data architecture are interrelated – it is impossible to successfully deliver data governance without a solid architecture.

Data architecture is a set of rules, models, policies, or standards that govern which data is collected, and how it is acquired, stored, arranged, secured, accessed, and used (Olavsrud, 2022). It also involves the technologies used to collect, manage, and store the data and the controls placed upon those technologies.

Data architecture is important as it enables better understanding of the data. It provides guidelines for managing data throughout its lifecycle, from creation to destruction. It provides a “blueprint” of data assets, how they are collected, processed, and provisioned.

Data Governance and OCAP® Principles

According to the First Nations Information Governance Centre (n.d.):

The First Nations principles of OCAP® establish how First Nations’ data and information will be collected, protected, used, or shared. Standing for ownership, control, access, and possession, OCAP® is a tool to support strong information governance on the path to First Nations data sovereignty.

OCAP® was established in 1998 during a meeting of the National Steering Committee (NSC) of the First Nations and Inuit Regional Longitudinal Health Survey, a precursor to the First Nations Regional Health Survey (FNRHS, or RHS). Originally, OCAP® began as “OCA” with the members of the NSC affixing a “P” soon after to acknowledge the importance of First Nations’ people possessing their own data.

There is no law or concept in Western society that recognizes community rights and interests in their information, which is in large part why OCAP® was created. OCAP® ensures that First Nations own their information and respects the fact that they are stewards of their information, much in the same way that they are stewards over their own lands. It also reflects First Nation commitments to use and share information in a way that maximizes the benefit to a community, while minimizing harm.

Watch the video below, which covers the First Nations Information Governance Centre’s mission, provides an overview of its work, and explores the importance of OCAP® for First Nations people and communities:

Please go to Section 3: Indigenous Data Sovereignty in Chapter 2: Data for Equity? for more information about this topic.

Summary

Data governance is a set of accountabilities and controls to ensure that data is usable, accessible, and protected. Effective data governance leads to better data analytics, which in turn leads to better decision-making and improved operations support. Data architecture is the technology or infrastructure that plays a vital role in supporting data governance. The role of a data architect is instrumental in the creation of a viable data architecture.

Test Your Knowledge

Complete the following activity to assess how much you learned about the content that was covered in this section.