Data strategy

Objectives and outcomes

The objectives of the data strategy are to:

  • Manage data as an enterprise asset and institutionalize data governance;
  • Provide the tools and environment to allow all Defence Team members access to data they need;
  • Build data literacy so that Defence Team members can use data to create value for Department of National Defence and Canadian Armed Forces (DND/CAF); and
  • Create a culture where data are leveraged in all decisions, and all personnel are held accountable for their role in managing data.

The implementation of the data strategy will deliver the following outcomes:

  • Increased information advantage for Defence Team members, allowing them to operate effectively and safely around the world;
  • Improved ability of Defence Team members to evaluate, understand, and use data including for decision-making;
  • Increased agility in providing new and enhanced data-driven capabilities; and
  • Improved ability to report on the results, effectiveness, and efficiency of DND/CAF programs.

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Guiding Principles

The following guiding principles have been identified to help DND/CAF implement the data strategy:

  • Data are a shared asset: DND/CAF are stewards of the data, ensuring that data are created, collected, managed, and used to the benefit of DND/CAF, the Government of Canada, and Canadians;
  • Data are accessible: DND/CAF’s data holdings are accessible and available to those who need them;
  • Data are secure: DND/CAF’s data holdings are identified and secured based on sensitivity, privacy, and releasability;
  • Data are trusted: Data are governed to improve data quality; and
  • Data are managed ethically: Data are managed throughout their lifecycle to eliminate bias, ensure fitness for use, and adhere to the Code of Ethics.

The guiding principles will be used to evaluate decisions about investments in data, and how to collect, share, and use data.

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The vision statement for DND/CAF data is:

Data are leveraged in all aspects of Defence programs, enhancing our defence capabilities and decision-making, and providing an information advantage during military operations.

Long description follows
Figure 2: The vision for data within DND/CAF
Figure 2: Graph breakdown

The DND/CAF Data Strategy envisions leveraging data in all aspects of Defence programs, enhancing our defence capabilities and decision-making, and providing an information advantage during military operations, through applications such as advanced analytics, virtual reality, artificial intelligence, autonomous vehicles and biometrics.


This means that:

  • Defence Team members have an information advantage in military operations, built through the integration and aggregation of high quality data from a wide variety of internal and external sources;
  • Defence Team members have access to the data they need to identify and act upon opportunities to improve efficiency and effectiveness of Defence programs;
  • Defence Team members have the skills to find, critique, analyze, and interpret data
  • DND/CAF has a culture where people are expected to use data and;
  • DND/CAF explores and implements data-driven approaches to expand and enhance operational capabilities, such as:
    • Business intelligence and analytics for planning, reporting, and support to decision-making;
    • Advanced (i.e. predictive and prescriptive) analytics to provide foresight and recommendation, for example for predictive materiel maintenance and policy creation;
  • Virtual reality and simulation, for example in training;
  • Artificial intelligence, for example to automate low-value and repetitive tasks, such as summarizing searches or comparisons;
  • Augmented reality, for example for navigation during operations;
  • Autonomous vehicles (e.g. drones) and robotics, for example for surveillance or warehousing;
  • Additive manufacturing (i.e. 3D printing), for example to manufacture parts on demand; and
  • Biometrics, for example in intelligence.

The vision is detailed further in four pillars, described in the sections below: data management, data tools and environment, data literacy and skills, and data culture.

Data management

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Figure 3: The Data Management pillar
Figure 3: Text version


Manage data as an enterprise asset using well defined plans and processes throughout the data lifecycle


  • Informal data governance – “ownership”
  • No data quality framework, ad-hoc data quality initiatives
  • Rigid security practices, low risk mindset
  • Lack of enterprise data model and inventory
  • Limited master, metadata management practices


  • Formal data governance – “stewardship”
  • Data are regularly assessed against established framework, formal data quality initiatives
  • Master, reference, and metadata practices implemented
  • Flexible risk-based security approach
  • Documented enterprise data model, flows, inventory

The data management pillar focuses on managing data as an enterprise asset. In the future state:

  • Data governance is formalized, with data stewardship roles well defined and a network of data stewards established;
  • Data are regularly assessed against an established data quality framework, and formal data quality initiatives will be planned and implemented;
  • Master and reference data frameworks are implemented to improve data quality and interoperability;
  • A metadata framework is implemented, and metadata repositories are managed, integrated, and searchable;
  • Data security is managed through a flexible risk-based approach that secures data based on data sensitivities, user roles (including need to know), and classification; and
  • An enterprise data model and data flows are documented, and a data inventory maintained.

To get to that future state, DND/CAF will have to:

  • Implement data governance: Design and implement a data governance framework, including data stewardship;
  • Implement data quality approach: Design and implement a data quality framework, including quality assessment criteria;
  • Model DND/CAF data: Develop and maintain an Enterprise Data Model and data flows;
  • Evaluate data impacts: Embed data architecture in the Architecture Review Board and review initiatives against data principles;
  • Appropriately secure data: Develop and implement a risk-based data security approach that limits or grants access to data based on sensitivity and user roles (including need to know);
  • Implement a master data approach: Develop and implement a master data management approach;
  • Implement a metadata approach: Develop and implement a metadata management approach; and
  • Provide integrated data: Develop and implement a data integration framework and approach, covering operational data sources, administrative / corporate data sources, and open data sources.

Data tools and environment

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Figure 4: The Data Tools and Environment pillar.
Figure 4: Text version


Provide the tools and infrastructure to enable the Defence Team workforce to use data to create value


  • Few policies that identify how and when data can be collected, created, and used
  • Limited access to data tools to find and use data – “office productivity desktop”
  • Tools are not user-friendly
  • Disparate and disconnected data stores
  • IT-led data management


  • Data policies provide guidance and direction on how and when to create, collect, and use data
  • Self-serve, intuitive, easy-to-use tools for data discovery, analysis, visualization, management
  • Data tools are connected to data sources, including data warehouse(s), standalone repositories, and/or data lakes
  • Environments for experimentation
  • Business-led data management

The data tools and environment pillar focuses on creating the conditions that allow Defence Team members to create, collect, use, and manage data. In the future state:

  • Data policies provide guidance and direction on how and when to create, collect, and use data;
  • Intuitive, easy-to-use tools for data discovery, analysis, visualization and management are provided to all Defence Team members to increase self-service capabilities;
  • Data tools are connected to enterprise data repositories, including data warehouse(s), standalone repositories, and/or data lakes;
  • Environments exist to allow personnel to experiment with using data, and to innovate using data; and
  • Data management is led by business requirements, not IT requirements.

To get to that future state, DND/CAF will have to:

  • Implement data policies: Develop data policies and directives that conform to domestic and international legal obligations;
  • Resolve policy conflicts: Evaluate current policies, directives, and legislation to identify and resolve conflicts and duplications;
  • Develop data management tools roadmap: Identify requirements for, and develop a roadmap of, data management tools for data governance, data quality, data discovery, and data visualization;
  • Implement data management tools: Procure, implement, and provide access to common data management tools for all personnel; and
  • Provide data environments: Provide technical environments, such as sandboxes, where Defence Team members can explore and experiment with data.

Data literacy and skills

Long description follows
Figure 5: The Data Literacy and Skills pillar
Figure 5: Text version


Create a data-literate and skilled workforce capable of using data to create value for DND/CAF


  • Personnel have limited understanding of the definition of data
  • Personnel have limited understanding of their role in maintaining data
  • Personnel do not have the skills to critically assess data to identify risk and evaluate quality
  • Personnel do not understand how to use data to improve performance and innovate


  • Personnel understand the definition of data, and how DND/CAF captures, collects, and uses data
  • Personnel have the skills to find data, evaluate data quality, identify and mitigate risks of using data, and analyze and interpret data
  • Personnel have the ability to identify opportunities to use data to create value for DND/CAF
  • Personnel understand their role in maintaining data quality

The data literacy and skills pillar focuses on creating a skilled workforce that not only understands data in all its forms and at all points in the lifecycle, but also has the skills to use data to make decisions, to innovate, and to create value through improved efficiency and effectiveness. In the desired future state, Defence Team members:

  • Understand the definition of data, and how data are collected, captured, and used both within their L1 and across DND/CAF;
  • Have the skills required to find data, evaluate its quality, identify and mitigate risks associated with using that data; and analyze and interpret data;
  • Identify opportunities to use data in new reports and new ways to create value for DND/CAF; and
  • Understand their role in managing data throughout its lifecycle, and across DND/CAF.

To get to that future state, DND/CAF will have to:

  • Define data literacy: Define the skills and competencies required to be data literate, and develop a framework to evaluate data literacy;
  • Evaluate data literacy: Define target data literacy levels for DND/CAF, and evaluate the level of data literacy throughout DND/CAF;
  • Develop data literacy training: Develop training and tools to improve data literacy, both formally and informally;
  • Develop data literacy plan: Develop and implement a plan to increase data literacy throughout DND/CAF;
  • Develop data literacy staffing plan: Identify critical positions within DND/CAF that require higher data literacy, and identify opportunities to staff those positions through targeted hiring and other approaches such as job rotation; and
  • Develop HR strategy: Identify opportunities to sustain data literacy such as through new career paths and work streams, career and succession planning, and new occupations/ job categories.

Data culture

Long description follows
Figure 6: The Data Culture pillar
Figure 6: Text version


Create a culture where data is valued, and the use of data is habitual


  • The potential value of data is not well understood
  • The use of data in decision-making is not yet expected, nor is it trusted when it is used
  • Data are an after-thought in the design and delivery of programs and processes
  • The benefit of using data is not considered when assessing risk


  • Data are recognized as an asset that requires effort to manage and maintain
  • Data are leveraged in decision-making
  • Data are considered during design of programs and processes
  • Data are trusted
  • Leadership and personnel have greater risk tolerance

The data culture pillar focuses on creating a culture where not only are Defence Team members encouraged and expected to use data, but also excited about its potential. In the desired future state:

  • The time and resources required to manage data are considered part of doing business, and are planned and tracked accordingly;
  • Data are leveraged in decision-making, and Defence Team members are held accountable for presenting data-based recommendations;
  • Personnel are unafraid to present data-based reports, even if the results and conclusions are negative or run counter to known preferences;
  • Personnel trust that DND/CAF has managed data appropriately, and therefore that the data are reliable; and
  • Leadership and personnel are willing to tolerate and accept greater risk in using data if it generates enough benefit.

To get to that future state, DND/CAF will have to:

  • Develop change management approach: Develop a change management approach plan to obtain buy-in, and increase readiness for a data-driven organization;
  • Perform stakeholder analyses: Analyze stakeholders to identify both data champions and resistance to using data to further DND/CAF goals;
  • Prepare communications: Develop and share compelling messaging and lessons learned to encourage and entice Defence Team members to use data;
  • Create accountability for data: Identify ways to hold personnel at all levels in the organization accountable for managing data and maintaining data quality;
  • Reinforce data culture: Reinforce and reward behaviours that lead to better quality data; and
  • Experiment with data: Provide opportunities for personnel to experiment with data, and share ideas about how data can be used to create value for DND/CAF.

“Building skills and data literacy in the department will be a challenge – old habits die hard”

- Interview with DND executive

Responsibility for data management

The Accountabilities, Responsibilities and Authorities (ARA) framework is being reviewed and updated to reflect the creation of ADM DIA, and new accountabilities and responsibilities for data management across the department. New and/or updated policies and directives, as well as the data governance framework, will formalize the responsibilities for data management within DND/CAF in support of the ARAs.

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