DND/CAF Data Governance Framework

On this page

1. Introduction

With the release of Canada’s Defence Policy: Strong, Secure, Engaged in 2017, it is acknowledged that modern militaries increasingly need to rely on networks and data to plan and carry out missions. The Department of National Defence and Canadian Armed Forces (DND/CAF) is therefore in the process of modernizing the business of defence, and with it, providing a greater focus on digital and data-driven ways of working. Well-governed data is foundational to the enablement of the integration, transformation, aggregation, and consumption of large amounts of data, and introduces the potential for data-driven innovations, such as artificial intelligence (AI), to be deployed at scale.

The Assistant Deputy Minister (Data, Innovation, Analytics) has been tasked by the Deputy Minister and Chief of Defence Staff with the development and implementation of a ‘robust data governance framework’Footnote 1. Currently, DND/CAF is creating petabytes of data with no framework on how to govern, use or exploit this strategic assetFootnote 2, thereby limiting the potential benefits that can be realized. DND/CAF has an opportunity to change this through the implementation of the DND/CAF Data Strategy, and the development and implementation of the Data Governance Framework (DGF) that supports operations and provides the potential for future data-driven activitiesFootnote 3 to expedite access to data and to support the operationalization of DND/CAF priorities.

The Data Governance Framework takes the key elements of the DND/CAF Data Strategy and creates a consistent, adaptable structure for data governance within DND/CAF. A key aspect of this framework is transitioning the department from accessing data on a ‘need to know’ basis to one that is focused on a ‘need to share’ basis through data integration, orchestration, and interoperability. In this way, DND/CAF has an opportunity to fully optimize the data at its disposal as a strategic asset to support military operations.

The implementation of the Data Governance Framework will enable a change in the conversation, from L1s ‘owning’ data, to one in which all those who collect, access, manage, and disseminates data understand that there is shared responsibility for ensuring that data are accessible, secure, trusted, and managed ethicallyFootnote 4. This framework will support the reduction of data silos, movement towards trusted authoritative resources, and production of good quality data that can inform the development of AI and other data-driven solutions.

The Data Governance Framework builds on existing organizational practices and formalizes them so that the structure clearly delineates who is responsible for what activity. As this framework is promulgated, there will be some activities implemented immediately and others implemented in the medium to long-term. For example, the governance committees already exist, and Data Advisors already offer advice and guidance on an ‘as required basis’. However, as L1s formally adopt the role of Data Governors and Data Officers, there will be a need to identify specific stewards to oversee, monitor, and enforce compliance with enterprise-wide data policies, standards, and guidelines. A viable data governance structure will complement the IT infrastructure to support access to data across DND/CAF.

The success of this Data Governance Framework will be measured by a clear set of metricsFootnote 5. Through performance measurement, it should become clear which parts of the Data Governance Framework need to improve, change, or pivot to meet the evolving needs of DND/CAF priorities. Sharing performance metrics across DND/CAF will enable L1 organizations to identify best practices and will promote a culture of internal transparency, accountability, and the use of data assets to support the business of defence.

Key Takeaway

  • The DGF is established to promote the implementation of Data Governance in accordance with the Defence Policy and DND/CAF Data Strategy mandates
  • The DGF builds on existing practices and formalizes them to increase accessibility, consistency, and standardization in data management across DND/CAF
  • The successes following DGF implementation will be measured, and amendments made as required to ensure its effectiveness
 

2. Charter

2.1 Scope

This framework identifies the data governance principles, operating model, execution, planning and oversight as well as a Data Stewardship Model which defines the roles and responsibilities of the following individuals and groups:

2.2 Principles

Following the principles established by the DND/CAF Data Strategy, DND/CAF will ensure that:

2.3 Outcomes

In developing and implementing a ‘robust data governance structure’Footnote 7 that supports the modernization of the business of defence, the outcomes are objectives that are achieved through the implementation of the Data Governance Framework. The Data Governance Framework implementation is not itself the desired outcome, but rather it is the vehicle toward the ultimate achievement of these outcomes. The expected outcomes following the implementation of the Data Governance Framework are:

Key Takeaways

  • The DGF identifies and describes the key roles and responsibilities of its Data Stewardship Model as well as Supporting roles within DND/CAF, and the relevant committees and working groups
  • Key principles are identified for the DGF which echo the DND/CAF Data Strategy position
  • The DGF is adaptive to each L1’s unique structure within DND/CAF
  • “Ownership” of data is replaced with the principle of “Accountability”
  • The DGF is used as a vehicle to achieve the desired and mandated outcomes
 

3. Operating Model and Accountabilities for Data Governance

3.1 Operating Model

In common with the culture of DND/CAF, data governance is a federated, hub and spoke model. It leverages existing authorities to formalise accountabilities and responsibilities for managing data. It introduces the Data Stewardship Model which will improve access and shareability of the data held within systems, and enable Data Consumers to receive usable, accurate and timely data.

ADM(DIA) as the functional authority is responsible for providing strategic direction on data management and analytics, as well as being responsible for disseminating best practices, standards, and recommended tools to support data quality, integration, and interoperabilityFootnote 12. L1s are responsible for amplifying this approach locally and collaborating with ADM(DIA) and other L1s on the use and promotion of use of DND/CAF dataFootnote 13. The Data Governance Framework operates in parallel to the Analytics in DND/CAF: Vision and Guiding PrinciplesFootnote 14 document, which together will ensure and provide the foundation for DND/CAF data management frameworks so that the management of data and analytics occurs in a holistic fashion.

3.2 Data Governance, Data Stewardship, and Data Management Activities

Data governance is the lynchpin that provides the department with the essential foundation required to facilitate its data management activities. Data governance is defined as the ‘logical structure for classifying, organizing, and communicating complex activities involved in making decisions about and taking action on enterprise data’Footnote 15. The clear identification of roles and responsibilities and the introduction of accountability of the data are large contributors to this essential foundation. A key deliverable from this activity is the development and implementation of data stewardship. In the DND/CAF Data StrategyFootnote 16, under the future state of Data Management pillar, it envisions that formalized data governance incorporates well-defined data stewardship roles and establishes a network of data stewards. Data stewardship is defined as the ‘formalization of accountability for the management of data assets on behalf of others and in the best interests of the organization’.

From this Data Governance Framework and the embedded Data Stewardship Model, data management activities are then established to support the data continuumFootnote 17. These data management activities align with the following Knowledge AreasFootnote 18:

Figure 1: DAMA DMBoK Knowledge Area Wheel

Figure 1: DAMA DMBoK Knowledge Area Wheel
Long description of Figure 1
  • The DAMA wheel demonstrates the knowledge areas of data management: data architecture, data modeling and design, data storage and operations, data security, data integration and interoperability, document and content management, reference and master data management, data warehousing and business intelligence, metadata management, data quality management, and data governance, which applies to all these areas through the exercise of authority and control over the management of data assets. These knowledge areas help to protect and enhance the value of data assets throughout its lifecycle.
  • The data lifecycle includes planning for data needs, designing and enabling data collection and management, creating and obtaining data, storing and maintaining data, using data, enhancing data, and disposing of data that is no longer required.

The Data management activities are shared across the department, with ADM(DIA) assuming the functional authority for the strategic management of data. A breakdown of roles and responsibilities for specific data management activities has been identified in the DND/CAF Data Strategy and will be further amplified within the Data Strategy Implementation Plan (DSIP).

Figure 2: DND/CAF Data Domains

Figure 2: DND/CAF Data Domains
Long description of Figure 2

Data DomainsFootnote * are grouped by function. There are three categories of Data Domains across DND/CAF: Corporate, Common and Operational.

  • Corporate data is captured in Data Domains that are managed only by Corporate L1s. Operational L1s do not manage these Data Domains.
  • Common data is captured in Data Domains that are managed by both Corporate an Operational L1s.
  • Operational data is captured in Data Domains that are managed only by Operational L1s. Corporate L1s do not manage these Data Domains.

Examples of Data Domains for each of the three categories include:

  • Corporate data
    • Corporate Administration
    • Resource Allocation
    • Departmental Reporting
  • Common data
    • Safety and Security
    • Employee Management
    • Conduct and Culture
    • Finance
    • Audit, Evaluation and Lessons Learned
    • Infrastructure and Environment
    • Material and Assets
    • Military Personnel
    • Information and Technology Management
  • Operational data
    • Intelligence Sensors
    • Pan-domain Situational Awareness
    • Operational Sensor and Effectors
    • Pan-domain Command and Control
    • Force Posture and Readiness Reporting

3.3 Data Domains

Data Domains are grouped by function. The current landscape of Defence Data Domains is depicted in the image (Figure 2). There are three categories of Data Domains across DND/CAF: Corporate, Common and Operational. Corporate L1s of DND support the Operational L1s of CAF to pursue the business of defence. Corporate L1s interact with Corporate or Common Data Domains, whereas Operational L1s interact with Operational or Common Data Domains. The list of Data Domains will remain evergreen and expansive as Data Officers discover the breadth of their environment and new business requirements or activities are introduced. Each L1 may interact with one or several Data Domains and can fulfill different roles within those Data Domains. For example, they may be stewards, advisors, consumers, or producers of the data within any given Data Domain. The nature of each of those roles bear different sets of responsibilities which are described in this document.

Data Domains can each have their own policies, sets of standards or best practices, and unique processes or tools for their data. Data Advisors of DND/CAF provide communications and insight on these, and L1 Data Officers must ensure their Data Domain Stewards are able to provide sufficient opportunity for guidance and awareness to their working-level stewards to ensure compliance. Where a Data Domain exists across multiple L1s, care should be taken by Data Domain Stewards, they must address any barriers with their Data Officer to promote horizontal alignment across DND/CAF by encouraging the engagement of other L1s and ADM(DIA).

3.4 Tiered Governance Structure

Following the principle of adaptive data governance, data is managed through a tiered governance structure that ranges from a strategic enterprise approach at the Defence Data Management Board (DDMB), down to working levels throughout all L1s.

Figure 3: Data Governance Structure

Figure 3: Data Governance Structure
Long description of Figure 3

Data is managed through a tiered governance structure that is overseen by DND/CAF, ADM(DIA), and Data Stewards, and ranges from a strategic enterprise approach at the Defence Data Management Board (DDMB), down to working levels throughout all L1s. These groups include (from top to bottom):

  • DDBM
  • DG DPO
  • DGWG
  • Data Stewardship Working Groups

At an individual level, the data governance roles include (from top to bottom)Footnote *:

  • ADM
  • DG
  • Director
  • Manager
  • Project/Operation

Other groups, external to DND/CAF, that are key considerations when thinking of the data governance structure include:

  • Public
  • Other GoC Departments
  • Other Governments
  • Other Partners

Below the data governance structure is the list of Data Management Knowledge Areas, which include: data modeling & design, data storage & operations, data security, data integration & interoperability, document & content management, reference & master data, data warehousing & business intelligence, metadata, data quality, and data architecture.

Two focal points below the list of knowledge areas include the Business (people and process) and the Technology (tools).

Key Takeaways

  • The Data Governance Framework promotes a federated, hub-and-spoke model leveraging existing authorities
  • ADM(DIA) has functional authority to provide strategic direction on data management and analytics
  • Data Governance oversees the activities and ensures compliance in all 10 Data Management Knowledge Areas using a formalized network of Data Stewards
  • Data Domains within DND/CAF are designated by business function and exist in three categories: Operational, Common or Corporate
 

4. Oversight

4.1 ADM(Data, Innovation, Analytics)

4.1.1.0 Role of ADM(DIA)

ADM(DIA) is the functional authority for data management, data governance, analytics, data-enabled business transformation and certain data-driven applications such as artificial intelligence applicationsFootnote 21, and is also the departmental service lead under the Treasury Board Secretariat implementation of the Policy on Service and Digital.Footnote 22 ADM(DIA) is responsible for providing departmental leadership, direction, and services in implementing these activities for and on behalf of the DND/CAF. ADM(DIA) sets direction on data management and analytics through the Data Governance FrameworkFootnote 23 and the DAOD 6500 series on Data Management and Analytics. Data management frameworks activities will be established from the foundation and structure set out in the Data Governance Framework. In some circumstances, there will be collaboration with other L1s on the development of other DAODs on shared interests, for example, the potential to develop a policy instrument on metadata management.

Figure 4: Authority of ADM(DIA)

Figure 4: Authority of ADM(DIA)
Long description of Figure 4
  • ADM(DIA) is the functional authority for Data Management and Data Analytics which includes the Joint Directive on Defence Program Analytics and the Joint Directive on Data Management. ADM(DIA) is responsible for the DAOD 1000-6 Policy Framework on Data, Information, and Information Technology which comprises three areas: data management, analytics, and certain artificial intelligence applications. ADM(DIA) provides departmental leadership for issuing directives such as the DAOD 6500-0 Data Management and Analytics. This includes instructional DAODs on Master Data and Reference Data and directives in collaboration with ADM(IM) on DAOD on Metadata.

4.1.1.1 Scope of ADM(DIA)’s Data Governance Responsibilities

For the purposes of this framework, the ADM(DIA) is responsible for the following:

4.1.1.2 Data Governance Activities of ADM(DIA)

The specific activities that ADM(DIA) will undertake are as follows:

4.1.1.3 ADM(DIA)’s Relationship with Other Data-Related L1 Roles

The key relationships of ADM(DIA) include:

4.2 The Defence Data Management Board (DDMB)

The Defence Data Management Board (DDMB) is the most senior governance body convened by the ADM (DIA) to support its functional authorities in data management, analytics, data-centric innovation, and data-enabled business transformation. DDMB considers, shapes, directs, and monitors enterprise-wide data-driven transformation. DDMB also provides guidance and direction on Defence priorities, risks and issues pertaining to data and analytics to ensure that data are leveraged in all aspects of Defence programs, enhancing our defence capabilities and decision-making, and providing an information advantage during military operations.Footnote 26

Key Takeaway

  • ADM(DIA) is the functional authority for data management, data governance, analytics, data-enabled business transformation and certain artificial intelligence applications
  • ADM(DIA) Data Governance responsibilities include leadership, sponsorship, and guidance to senior leaders of DND/CAF as well as issuing policies on data management and analytics
  • ADM(DIA) must collaborate with all L1s including ADM(IM) and Data Advisors, and other Chief Data Officers from other Government of Canada departments
  • The DDMB is a senior governance body which addresses issues in cross-cutting data and analytics issues
 

5. Planning

5.1 Director General, Data Program Oversight Committee (DG DPO)

The role of the Committee is described in the Terms of ReferenceFootnote 27.

The Director General, Data Program Oversight Committee (DG DPO) has been established to provide strategic direction and oversight on the delivery of the DND/CAF Data Strategy, and its implementation plan.

DG DPO supports and aligns with Defence Data Management Board (DDMB) as required.

5.2 Within ADM(DIA)

5.2.1.0 The Data Governance Office (DGO)

The Data Governance Office (DGO) supports ADM(DIA) and DND/CAF by promoting and monitoring compliance to standards, policies, administrative orders, and directives concerning data governance and data management. It provides the platform to enable data stewards and facilitate collaboration across the organization on data related matters, including communication and change management along with training efforts. The Data Governance Manager will orchestrate the office and ensure there is organization-wide participation. The DGO is responsible for the oversight of the implementation of the Data Governance Framework and other related data management frameworks. All Data Governance program efforts are to be documented by the office.

The DGO exists within ADM(DIA). It is managed by the Data Governance Manager who reports to the Director of Data Policy and Digital Innovation (DPDI). DPDI is overseen by the CDO of DND/CAF who is the Director General of Data, Analytics, Strategy, and Innovation (DASI).

5.2.1.1 Role of the Data Governance Manager

The Data Governance Manager develops the policy instruments supporting the Data Governance Framework, and implements the following activities:

5.3 Within ADM(IM)

Information management specialists within ADM(IM) provide direction on records and document management services, archival services, open government services, forms management, and information architectureFootnote 30. ADM(IM) and ADM(DIA) collaborates on the release of ‘open’ datasets to the Open Government Portal, and on the development of policy instruments on information/data architectureFootnote 31 and metadata management.

ADM(IM) also operates as a collaborative partner in supporting the infrastructure and tools that support data management activities that are executed in accordance with the Data Governance Framework. ADM(IM) is responsible for data securityFootnote 32, technology management, and implementation of data management tools. ADM(DIA) provides guidance on data management and access requirements to data held within ADM(IM) systems. ADM(DIA) and ADM(IM) have a shared responsibility for data quality, data interoperability, reinforcement of data culture, delivering a data literacy program, (see Joint Directive on Data Management [PDF, 16.4MB], paragraph 10 (accessible only on the National Defence network)), digital transformation, and implementation of artificial intelligence and machine-learning tools.

5.3.1.0 Role of the Information System Design Authority (ISDA)

The Information System Design Authority (ISDA) role will enable the Defence CIO in the delivery of timely, trusted, and secure information capabilities that contribute to the success of the Defence Team. ISDA participation in the Defence Information Programme governance will support the development and implementation of digital Defence policy instruments and architectures.

Enabling Defence Information Programme resources and capabilities in a decentralized service model requires a strong design authority capable of integrating the Defence Information Strategy across the DND and the CAF. By engaging internally and externally, the ISDA will facilitate improved planning and coordination of design, development, acquisition, and management of Defence information capabilities to ensure alignment of the Defence Information Strategy with Government of Canada (GC) digital standards and Treasury Board requirements for enterprise architectures.

The ISDA will ensure that Defence Information Programme resources and capabilities are consistent with Defence priorities and operations. Architectural designs and principles will be promulgated and used to assess proposed solutions and changes to existing solutions. Digital standards, best practices and reliable architectures will be shared so that they can be applied in a consistent manner throughout the life cycle of the digital resources and capabilities.Footnote 33

Key Takeaway

  • Strategic planning in accordance with the DND/CAF Data Strategy and other guiding documents is pursued primarily via the Director General, Data Program Oversight Committee (DG DPO)
  • Planning within ADM(DIA) in support of the implementation of the DGF is performed by the Data Governance Office (DGO)
  • The primary role within he DGO is the Data Governance Manager, they lead the data governance initiatives
  • ADM(IM) planning impacts the DGF by providing direction on information management, its available resources, and architectural possibilities to support DGF implementation
 

6. Execution

6.2 Implementation Approach

6.2.1.0 Overview

To determine the right approach to support an L1’s implementation of the Data Governance Framework, it is crucial to first understand the L1’s current state of maturity. It is possible that an L1 is at different levels of maturity for different activities, however, it is important to not tackle too much in one area and let other activities fall by the wayside. An effective approach concurrently focuses its efforts across the vast activities defined in the Data Governance Framework. The table below (Figure 5: DGF Implementation Approach Guidelines) provides examples of the stages of maturity within the Data Governance Framework implementation activities. These examples can be used to help gauge current maturity and frame potential next steps for an L1. This enables the L1 to mature at a “crawl, walk, run” pace, while recognizing that some L1s may be ahead of others in their data governance and data management initiatives, and some may adopt the framework with more ease. This approach is adaptable to each L1, and the table is intended to provide examples and general guidelines to guide the way forward.

6.2.2.0 Step-by-Step

To begin the implementation of the Data Governance Framework, it is most effective to perform the following sequence of actions:

Examples of the state of business throughout the implementation of the Data Governance Framework stages are demonstrated in the table below.

Figure 5: DGF Implementation Approach Guidelines

Crawl Walk Run
Key stakeholders are familiarized with the Data Governance Framework, there are plans for its future adoption. The Data Governance Framework is being adopted, at a minimum, for critical data assets. The Data Governance Framework has been adopted and is actively implemented through established business processes.
Data Governors, Data Officers – and, as required, Data Domain Stewards – have been identified. In addition to executive level and managerial level stewards, working-level stewards are identified for critical data assets. Each role of the Data Stewardship model at the executive or managerial level as well as working level are identified, and the model is applied across the L1.
There is a method determined and initiatives underway to identify all critical data assets. All critical data assets are identified and governed. All critical data – and possibly additional data assets – is (are) identified, governed, and monitored on a regular, ongoing basis.
There are engagements with ADM(DIA) to identify a suitable representative for the Data Governance Working Group. There is, at a minimum, an interim representative that regularly attends the Data Governance Working Group. There is sufficient presence from the L1 and regular, active participation in the Data Governance Working Group.
The concept of the Data Stewardship Working Group(s) is understood, and the Data Officer or Data Domain Steward are planning to establish it (them). There is some form of Data Stewardship Working Group though attendance of all the stewardship groups or frequency may be inconsistent. There is (are) sufficient Data Stewardship Working Group(s) and participation in it (them) for the domain that meet on a regular basis.
Data issues and risks are being identified but there are no resolution or escalation processes. Data issues and risks at the working level are addressed as required, at a minimum, for critical data assets. Data issues and risks that remain unresolved at the working level are escalated to ADM(DIA) and the Data Governance Working Group.
Data governance and data management metrics are being developed, there is awareness of ADM(DIA) KPIs and thresholds. Data governance and data management metrics are established and monitored for, at a minimum, critical data assets. There is knowledge and understanding of ADM(DIA) KPIs and thresholds. Data governance and data management metrics are established and monitored regularly against KPIs and thresholds.

6.3 Working Groups

6.3.1.0 Overview

Working groups facilitate the resolution of data issues through problem-solving discussions and activities. They also provide a means to escalate data issues as required. There are two levels of working groups, the Data Governance Working Group (DGWG) which provides a forum for the Data Officers and Data Advisors, and the Data Stewardship Working Groups (DSWGs) which provides a forum for the Data Domain Stewards, their working-level stewards, and other supporting roles as required.

6.3.2.0 Data Governance Working Group (DGWG)

The role of the DGWG is described in the Terms of Reference.Footnote 34 The working group has been developed to fulfill the following activities:

The DGWG has been established by the DG DPO Committee to facilitate the implementation of the Data Governance Framework as well as to identify and resolve data governance, data management and data access issues. The DGWG’s Terms of reference, in turn, should be read and understood in conjunction with the Data Governance Framework. Members of the Working Group are typically Director-level or Military equivalents who have been identified as their L1 Data Officer. The group will also promote data quality, security, usability, accessibility, and other best practices.Footnote 35

Issues that remain unresolved at the DGWG which may be candidates for escalation will first be reviewed by the Chief Data Officer. Following revision, a solution may be proposed, or the decision made to escalate the issue to the DG DPO Committee and/or the DDMB.

6.3.3.0 Data Stewardship Working Groups (DSWGs)

Each Data Domain Steward must create their own Data Stewardship Working Group. These working groups meet monthly, at a minimum. These working groups are a gathering of Data Stewards and, Data Custodians within the same Data Domain, but invitations can be extended to any supporting role as required to facilitate discussion, seek consultations, or resolve issues. There is no strict format to these working groups, each Data Domain Steward can design them as required. The working groups identify, resolve, and escalate data issues and risks, collaborate to design metrics which support ADM(DIA) data governance and data management KPIs and thresholds, as well as standardize processes and definitions within their domain to coincide with DND/CAF processes and definitions. Further, these working groups are responsible for identifying or appointing the working-level stewards, provide training materials to them and promote existing rules and policies.

6.4 Data Stewardship Model

6.4.1.0 Overview

The Data Stewardship Model supports the execution of data governance and data management activities. Stewardship exists in multiple tiers from executive-level to working-level stewards. Executive-level or managerial-level stewards include Data Governors, Data Officers and Data Domain Stewards. Working-level stewards include Data Stewards, and Data Custodians. Where possible, the roles within the Data Stewardship model will be assigned to existing roles within DND/CAF to ease adaptability of the Data Governance Framework. This will minimize the need for net new resources, where existing resources can be leveraged, or their responsibilities recalibrated. Each L1 must designate these roles within their L1 to facilitate the implementation of the Data Governance Framework as it is the foundation to the implementation of all other data and data management policies, standards, best practices, and processes. The diagram below (Figure 6) demonstrates the hierarchy of these stewardship roles.

Figure 6: Stewardship Model & Personas

Figure 6: Stewardship Model & Personas
Long description of Figure 6

The DND/CAF Data Stewardship Model depicts the hierarchy of the various stewardship roles across the department that work to support the execution of data governance and data management activities.

The Chief Data Officer (CDO) is at the top of the hierarchy in the Data Stewardship Model. The CDO is “a corporate who is responsible for managing the enterprise’s data assets” (DAMA International). The CDO is needed to ensure that data is managed as an asset with the purpose of enabling strategic business decisions, broadening access and maintaining standards to mitigate risks, lower costs and increase efficiency organization-wide.

Stewardship roles below the CDO exist in multiple tiers from executive-level to working-level stewards. Each stewardship role is categorized into one of two larger groups: DGWD (Data Governance Working Group) and DSWG (Data Stewardship Working Groups).

Executive-level or managerial-level stewards include:

  • Data Governors – includes DDMB and DG DPO representative (DGWD)
  • Data Officers (DGWD)
  • Data Domain Stewards (DSWG)

Working-level stewards include:

  • Data Stewards (DSWG)
  • Data Custodians (DSWG)

Supporting roles facilitate the effective execution of data governance and data management activities through various stages: input, throughput, and output. Supporting roles include:

  • Data Producers (Input)
  • Data Advisors (Throughput)
  • Business Data Specialists (Throughput)
  • Data Professionals (Throughput)
  • Data Consumers (Output)

6.4.2.0 The Chief Data Officer (CDO)

The Chief Data Officer is the authority who ensures the resolution of data issues, oversees decisions on data management that have a horizontal impact across L1s, and uses their discretion to escalate issues as required to executive-level decision-making bodies. The Chief Data Officer oversees and encourages collaboration among Data Officers to ensure that each L1s’ interests in data decisions are heard and respected. The Chief Data Officer role is within ADM(DIA), they are the Director General of Data, Analytics, Strategy, and Innovation (DASI).

6.4.3.0 Role of the Data Governors

The DDMB and DG DPO L1 representatives are de facto Data Governors, they are accountable and responsible for the initiation of the implementation of the Data Governance Framework and its data stewardship model. Data Governors serve as champions and advocates for data management and analytics. They collect input on the role of data to support key L1 initiatives and gain understanding of and support for data and analytics activities. They are responsible for communicating the data and analytics DAODs to their organizations, provide feedback and data to guide policy and best practices over time; and provide explicit direction to Data Officers and Data Advisors on stewarding the data within their control.

6.4.3.1 Scope of the Data Governors

The DDMB Data Governor is ultimately accountable for ensuring the designation of a Data Officer for each L1, and Data Advisor for each Data Domain. The DG DPO Data Governor for each L1 is responsible for the identification of the Data Officer for their L1. The DG DPO is also responsible for deciding which L1 or organizational business unit will be responsible for which Data Domain and ensuring a Data Advisor is identified for those Data Domains. ADM(DIA) supports the Data Governors in pursuing these responsibilities.

6.4.3.2 Responsibilities of the Data Governors

The Data Governor’s responsibilities are as follows:

6.4.3.3 Data Governor’s Relationships with Other Data Roles

The key relationships of Data Governors include:

6.4.4.0 Role of the Data Officer

The Data Officer is a director-level representative who is ultimately accountable for their L1’s data management activities, and for promoting the horizontal alignment across DND/CAF L1s. They are responsible for attending the DGWG to provide their support in resolving data issues across DND/CAF. The Data Officer is a champion for data within their L1, they play a key role in enabling access and ethically leveraging these data assets for achieving DND/CAF goals. The Data Officer acts as the primary point of responsibility, accountability, and activity for the assessment, improvement, ongoing fitness for purpose, and overall conformance of that data. Data Officers appoint Data Domain Stewards as required to facilitate stewardship of the Data Domains within their L1 to facilitate their data governance and data management activities.

For a current list of Data Officers, please see the DGWG members under ADM (DIA) SharePoint siteFootnote 36 (accessible only on the National Defence network).

6.4.4.1 Scope of Data Officer Role

The Data Officer is responsible for the oversight, monitoring, and implementation of data standards and guidelines on the management of the L1. The vehicle by which they do so is by appointing Data Domain Stewards for each of the Data Domains within their L1. If issues are across multiple L1s they should initially be addressed by Data Officers at the DGWG. The Chief Data Officer will support as required. If necessary, the issue may be escalated to the DG DPO Committee and escalated further, if necessary, to the DDMB.

6.4.4.2 Responsibilities of the Data Officer

In collaboration with ADM(DIA), the main purpose of the Data Officer is to improve, protect, and collaborate with other L1s and Data Advisors on the data. In so doing, the Data Officers is responsible for assessing the current state of data access and data management activities, as well as ensuring that security, privacy and retention and disposition activities are conducted within their L1.

The responsibilities are as follows:

6.4.4.3 Data Officer’s Relationships with Other Data Roles

The Data Officer’s role is collaborative and interacts with Data Officers from other L1s and with supporting roles as required.

Specifically, the Data Officer works closely with:

6.4.5.0 Role of the Data Domain Steward

The Data Domain Steward is the subject matter expert of a Data Domain within their L1, they are knowledgeable of their Data Domain’s policies, standards, best practices, and processes. They are responsible for propagating this knowledge within their L1 to working-level stewards (and to other Supporting Roles as required) and overseeing the implementation. They ensure the assessment, improvement, ongoing fitness for purpose, and overall conformance of that data with their Data Domain, within their L1. They formulate a Data Stewardship Working Group within their L1 to facilitate working-level discussions on data-related matters within their Data Domain with their L1. If a data issue cannot be resolved within their Data Stewardship Working Group, the Data Domain Steward escalates the issue to their L1 Data Officer for resolution. If the issue exists within a Data Domain that spans across multiple L1s and cannot be resolved amongst Data Domain Stewards, the data issue is escalated to the Data Officer who then facilitates horizontal discussions.

6.4.5.1 Scope of Data Domain Steward

The Data Domain Steward is responsible for the propagation and overseeing the implementation of their Data Domain’s policies, standards, best practices, and processes within their L1. They are responsible for the oversight of data, monitoring the management of data entities within that Data Domain within their L1, and enforcement of data processes, standards, and best practices. When deviations from the DAODs and data guidelines and standards are detected and not resolved through regular resolution processes, the Data Domain Steward is responsible for the escalation to their Data Officer.

6.4.5.2 Responsibilities of Data Domain Steward

The responsibilities are as follows:

6.4.5.3 Data Domain Steward’s Relationships with Other Data Roles

The Data Domain Steward’s role is very collaborative and interacts with working-level stewards, and supporting roles within the Data Domain, within their L1. They report on data-related matters to their Data Officer.

Specifically, the Data Domain Steward works closely with:

6.4.6.0 Role of the Data Steward

Data Stewards are working-level stewards; typically, this title is designated by a Data Domain Steward to an existing incumbent who is actively managing data on a regular basis. They are directly responsible for analyzing and maintaining data quality, access and other data management outcomes and activities. Data Stewards ensure there is alignment with business operations and the management of the data lifecycle as well as monitor and act on data quality issues and risks.

6.4.6.1 Scope of the Data Steward

Data Stewards are responsible for the implementation of data governance and data management activities within their operational responsibilities. The Data Steward reports on data issues and activities to their Data Domain Steward and collaborates with other working-level stewards and other Supporting Roles.

6.4.6.2 Responsibilities of the Data Steward

The responsibilities are as follows; this list is not exhaustive and can be adapted as required for each Data Domain:

6.4.6.3 Data Steward’s Relationship with Other Data Roles

6.4.7.0 Role of the Data Custodian

Data Custodians technical working-level stewards; typically, this role or title is designated to an existing incumbent who is actively managing technology solutions. Data Custodians are the technical support of data stewardship activities and have detailed knowledge on how applications, data stores, and Extract, Transform and Load or Extract, Load, and Transform processes. Data Custodian activities includes data integration, database administration, and data quality specialists. Under the data governance approach, they identify opportunities to enable data sharing through data integration and interoperability across the department.

Within ADM(IM)’s DIMSecur, subject matter expertise on security is founded on the need to ensure that sensitive information under the department’s control is protected according to the Policy on Government Security and any relevant legislation, policy or agreement. Information security functional specialists are responsible for ensuring protection of confidentiality, integrity, and availability of data and information. In respect of the Data Governance Framework, ADM(DIA) will collaborate with ADM(IM)’s DIMSecur on developing data-centric risk and data-centric security approaches across the department.

6.4.7.1 Scope of the Data Custodian

Data Custodians provide technical support and collaborate with working-level stewards to implement technical solutions and perform technical activities required to facilitate data governance and data management activities. They can be identified by ADM(IM), or they are other technical resources who can provide these services.

6.4.7.2 Responsibilities of the Data Custodian

The responsibilities are as follows (this list is not exhaustive and can be adapted as required for each Data Domain):

6.4.7.3 Data Custodian’s Relationship with Other Data Roles

6.5 Supporting Roles

6.5.1.0 Overview

Supporting Roles of the Data Stewardship Model facilitate the effective execution of data governance and data management activities. The involvement of these roles will provide varying lenses which promote more dynamic development of data governance and data management practices within DND/CAF. Data Officers and Data Domain Stewards should provide sufficient opportunity for the necessary Supporting Roles to collaborate. The Supporting Roles ensure the practices are fit-for-purpose and can be practically applied across all L1s. The Data Governance Framework does not prescribe responsibilities of Supporting Roles but acknowledges the impact they have on the responsibilities and capabilities of each role in its Data Stewardship Model.

6.5.2.0 Analytics Support Centre, Managers’ Coordination Committee (ASC MCC)

Analytics Support Centre – Managers Coordination Committee (ASC – MCC) is the working level data governance body convened by the Director of Policy and Digital Innovation, ADM (DIA) to support its functional authorities in analytics. The ASC – MCC is a working-level forum to provide advice on analytics and escalates issues requiring shaping or approval to higher level governance bodies.

The MCC is a managers-level committee and aligns with and supports the existing data governance committees. Members are expected to work with their identified L1 Data Officers from the DGWG on issues concerning data management, governance, and access. Issues and items can then be escalated up to the DG DPO and DDMB as required.Footnote 43

6.5.3.0 Data Citizen

In DND/CAF, everyone is considered a Data Citizen. It is important to recognize that not everyone will be in contact with data or actively managing data on a day-to-day basis. However, everyone has the responsibility in those scenarios as they arise to respect data management best practices, use the data appropriately, and to abide by relevant privacy and security requirements. They have the responsibility of diligence in data-related matters. There must be sufficient communication and awareness within DND/CAF to express this inherent responsibility and to promote an effective data culture.

6.5.4.0 Data Advisor

Data Advisors are the DND/CAF authority of a Data Domain and are regularly consulted by L1s to ensure data activities adhere to the Data Domain’s relevant policies, standards, best practices, and processes. They ensure alignment with the critical priorities and goals of their Data Domain to support DND/CAF business of defence. They have authority to create policies and standards for their Data Domain. They must ensure their policies, standards, best practices, and processes for their Data Domains are sufficiently communicated to the Data Officers to ensure that the Data Officers can adequately equip their Data Domain Stewards with the required knowledge. The ADM(DIA) and the Data Advisors consult on data policy and standards creation to ensure consistency across the DND/CAF. There is one Data Advisor per Data Domain. The Data Advisors must have representation at the DGWG.

6.5.5.0 Data Consumer

Data Consumers express their business needs which identify desired outcomes and targets. This provides a benchmark to signify how data governance and data management activities should be designed to ensure that they are fit-for-purpose to support those needs. Data Consumers play an important role in maintaining the data’s integrity while using and re-using data created, collected, and managed within DND/CAF. These are the users of the data, and ultimately, this group’s needs must be fulfilled to facilitate business activities. Data Consumer input should be considered in all decisions involving data.

The role of the Data Consumer is supported by enabling access, use and re-use of data within the Data Domains to support the business of defence. The associated Data Manager(s) is (are) responsible for ensuring that data is consumed according to any specific security classification and privacy requirements identified by ADM(DIA) and the Data Advisors. Considerations such as these are critical when transplanting or transferring data from one environment to another.

Data Consumers exist at multiple HR classification levels. The level of data literacy required to assess and analyze data to support the business of defence differs across these HR classification levels. ADM(DIA) training team will work with Data Consumers on supporting data literacy and culture through DND/CAF.

6.5.6.0 Data Producer

While Data Consumers use the data, Data Producers are those who create it. Data Producers are the ultimate source of data. The data can be sourced from across the DND/CAF, from external partners, other government departments, third parties, and so on. This means that Data Producers are not solely within the DND/CAF. However, within the DND/CAF, Data Producers are often simultaneously taking on other roles, perhaps as working-level stewards, or other Supporting Roles. The role they play as producers of the data is critical to the management of data as they are the creators.

6.5.7.0 Business Data Specialist

Business Data Specialists ensure that data governance and data management activities are fit-for-purpose. They play a key role in reviewing the data to ensure that it meets defined criteria before the data is released and can be consumed and used for ensuing activities. They are subject matter experts (SMEs) within the business units who – unlike Data Stewards – are not actively engaging in data management activities with the data. Rather, the Business Data Specialists are individuals who are SMEs of the business within the L1 or Data Domain.

The Business Data Specialist is responsible for identifying deviations from the DAODs and subject-specific guidelines and standards and resolving them through automated or manual practices and tools.Footnote 44 Business Data Specialists actively engage with working-level stewards and attend the DGWG or DGSWs as required.

6.5.8.0 Data Professional

Data Professionals are onboarded to provide expertise in particular areas or activities of data management or information technology. They are hired specifically to a role or position as a professional to provide a service, whether they are a full-time employee or consultant. There are varying types of Data Professionals who hold many different titles such as Data Scientists, Data Engineers, Data Architects, Data Analysts, and many others. The role they play, their scope of work and their responsibilities are defined within their statements of work and are unique to each initiative they support. The Data Professionals are expected, as required, to attend the DGWG and DSWGs, and to support and consult with data stewards of all levels.

Key Takeaway

  • The implementation of the DGF is executed on a crawl-walk-run basis, acknowledging the varying data governance maturity levels and available human and monetary resourcing of each L1.
  • There are two levels of problem-solving working groups for data issues, the executive-level DGWG and the Data Domain, working-level DSWGs.
  • The Data Stewardship model outlines the stewardship responsibilities of executive-level stewards (Data Governors, the Chief Data Officer, and the Data Officers) as well as the working-level stewards (Data Domain Stewards, Data Stewards and Data Custodians).
  • Supporting roles are described to contextualize the roles within the Data Stewardship model, these roles influence day-to-day data management activities (Data Advisors, Data Producers, Data Consumers, Business Data Specialists and Data Professionals).
 

Appendix A: References

Functional Authority documentation:

Internal Policy Instruments:

DND and ADM(DIA) documentation:

Treasury Board of Canada Secretariat Policy Instruments:

Legislation and other sources:

Appendix B: Definitions

analytics (analytique)
Systematic computational transformation of data into insights, for the purpose of making better decisions. (Defence Terminology Bank record number 696416)
artificial intelligence (intelligence artificielle)
Information technology that performs tasks that would ordinarily require biological brainpower to accomplish, such as making sense of spoken language, learning behaviours, or solving problems. (Directive on Automated Decision-Making, Treasury Board)
big data (mégadonnées)
Data produced in high volume, at high speed and in various formats, and which is too complex to be handled with traditional data-processing software. (Defence Terminology Bank record number 695863)
business intelligence (intelligence d'affaires)
The applications, infrastructure, tools and best practices that enable access to, and analysis of, information to improve and optimize decisions and performance. (Defence Terminology Bank record number 695860)
data (données)

Set of values of subjects with respect to qualitative or quantitative variables representing facts, statistics, or items of information in a formalized manner suitable for communication, reinterpretation, or processing. (Policy on Service and Digital, Treasury Board)

Note – In the DND and the CAF, data is created, collected and used both in military operations and exercises, and in corporate administrative processes.

data architecture (architecture des données)

An integrated collection of master design documents at different levels of abstraction that govern how data is collected, stored, arranged, used and removed.

Note – Data architecture is classified by descriptions of all the containers and paths that data takes through an organization's systems. (Defence Terminology Bank record number 695861)

data asset (ressource des données)
An entity comprised of data from any source that can be governed and managed and that has potential to provide value or produce benefit. This can include data sets, databases, big data, and system and application output files. (Defence Terminology Bank record number 696417)
data governance (gouvernance des données)

A system of decision rights and accountabilities applicable to data-related processes.

Note – This system describes which action can be taken on which data asset, by whom, under which circumstances and using which methods. (Defence Terminology Bank record number 695865)

data integration (intégration des données)
The movement and consolidation of data within and between data stores, applications and organizations, into consistent forms either physical or virtual. (Defence Terminology Bank record number 695866)
data management (gestion des données)
The development, execution and supervision of plans, policies, programs and practices that deliver, control, protect and enhance the value of data and information assets throughout their life cycles. (Defence Terminology Bank record number 27521)
data quality (qualité des données)
A degree or level of confidence that the data provided meets requirements of the data user in terms of characteristics such as accuracy, completeness and reliability. (Defence Terminology Bank record number 33436)
digital (numérique)
Processes, practices and technologies related to the production, storage, processing, dissemination and exchange of electronic information and data. It refers to, among other things, information and communications technologies, infrastructures, and the information and data they produce and collect. (Policy on Service and Digital, Treasury Board)
domain (domaine)
A specific field of knowledge or expertise. (Defence Terminology Bank record number 21857)
enterprise data model (modèle de données d’entreprise)
A holistic, enterprise-level, implementation-independent conceptual or logical data model that provides a common and consistent view of data across the enterprise. (Defence Terminology Bank Record number 696418)
information (information)
The representation that a human or a machine assigns to data, facts or knowledge by means of known conventions such as reports, events, processes, decisions, ideas or opinions in any medium or form. (Defence Terminology Bank record number 696374)
information asset (ressource d’information)

A body of information defined and managed as a single unit so the body of information can be understood, shared, protected and exploited effectively.

Note – An information asset has recognizable and manageable value, risk and content for DND business and CAF operations. (Defence Terminology Bank record number 696375)

interoperability (interopérabilité)
The ability of different types of electronic devices, networks, operating systems, and applications to work together effectively, without prior communication, to exchange information in a useful and meaningful manner. (Policy on Service and Digital, Treasury Board)
master data (données maîtres)

Data that provides the context for business activity data in the form of common and abstract concepts that relate to the activity.

Note – Master data includes the details (definitions and identifiers) of internal and external objects involved in business transactions such as customers, products, employees and vendors. (Defence Terminology Bank record number 695867)

metadata (métadonnées)
Data that describes data itself, the concepts the data represents and the connections between the data and concepts. (Defence Terminology Bank record number 695869)
predictive analytics (analyse prédictive)
The use of regression analysis, forecasting, multivariate statistics, pattern matching, predictive modeling or other techniques to provide insight as to what could happen in the future. (Defence Terminology Bank record number 696419)
reference data (données de référence)
Data that defines a set of permissible values that are used by other data fields and do not generally change, for example, units of measurement and country codes. (Defence Terminology Bank Record number 696420)

Appendix C: L1 Data Considerations

Corporate Secretariat (Corp Sec)

Within the Corp Sec, the Directorate Access to Information and Privacy (DAIP) is responsible for the administration of both the Access to Information Act and the Privacy Act, which allow individuals right of access to information that is contained in government records. DAIP is also responsible for providing guidance and direction relating to the management of personal information to ensure compliance by the DND and the CAF with the administration of the Privacy Act.

ADM(Policy)

ADM(Pol) provides support on policy development related to data, including data sharing, ethics, and the use of future data-driven capabilities. ADM(DIA) will need to coordinate with ADM(Pol) to create coherent data policies for DND/CAF.

Judge Advocate General (JAG)/Canadian Forces Legal Advisor (CFLA)

The Office of the JAG and the DND/CF LA support the Data Governance Framework when providing legal advice when requested by a data steward, in relation to any law or legal obligations governing the collection, use, disclosure, retention, disposition, or storage of that data. Legal experts within JAG/CFLA ensure that the department meets regulatory, statutory, and compliance requirements, and is the lead respondent involved in litigation, investigation, or legal action.

In particular, DND/CFLA has primary responsibility for advising on legal aspects of the application of the Access to Information Act and the Privacy Act, as well as for supporting DND/CAF in meeting its obligation to produce information in relation to legal proceedings.

ADM(Defence Research and Development Canada)

ADM(DRDC) holds the functional authority for Scientific Integrity Policy and advises Data Governors of the DDMB representatives on data-centered innovations.

ADM(Review Services) – Regulation and Compliance

Across DND, subject matter experts play a key role in identifying, assessing, and mitigating data-centered risk within the department. The target state is that data have a reasonable and suitable guarantee of authenticity and reliability. In particular, that threats are identified, and the data has protection from loss, corruption, and the threat of privacy or security breaches.

Appendix D: Out of Scope

During the Data Governance Framework consultations, many valid concerns and comments were raised which foreshadowed the requirements for additional documents and resources from ADM(DIA). These resources will be made available, but are not within the scope of the Data Governance Framework itself:

Appendix E: Contributors

Special thanks are shared with the following organizations who contributed to the content and revision of the Data Governance Framework.

  • ADM(Fin)
  • ADM(DIA)
  • ADM(DRDC)
  • ADM(HR-CIV)
  • ADM(IE)
  • OMBUDSMAN
  • ADM(Mat)
  • ADM(PA)
  • ADM(RS)
  • CANSOFCOM
  • CARMY
  • CFINTCOM
  • CMP
  • Corp Sec
  • JAG / CFLA
  • RCAF
  • RCN
  • SJS
  • VCDS
  • ADM(IM)
  • CJOC
 

Appendix F: Citations

Report a problem or mistake on this page
Please select all that apply:

Thank you for your help!

You will not receive a reply. For enquiries, contact us.

Date modified: