Vision for Analytics in the DND/CAF


To elaborate on what was outlined in the DND/CAF Data Strategy, and to more clearly set ambitions of the DND/CAF in that space, an analytics-specific vision statement is provided below:

To provide Defence Team members with near real-time trusted data-driven decision support, anywhere in the world

That vision is deliberately ambitious and implies many practical characteristics for Defence Team members. They include:

  • Consumers will have secure access to a set of baseline analytics tools by default, and will be able to access analytics products through multiple channels, from anywhere in the world, 24/7/365, including from classified networks, where appropriate;
  • Data presented in analytics products will be updated in near real-time, where necessary;
  • Analytics products will be created using integrated data sets of structured and unstructured data from a wide variety of sources, including partner data and open data; and
  • A range of tools fit for different types of analytics, from commercial business intelligence tools to open-source data science tools, will be available to enablers across the enterprise.

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Guiding Principles

The following guiding principles will help DND/CAF to make decisions about investments in analytics and the future development of analytics products.  The principles complement those of the DND/CAF Data Strategy.Footnote 1 The guiding principles of the DND/CAF Data Strategy are: data are a shared asset; data are accessible; data are secure; data are trusted; and, data are managed ethically.

  • Purposeful:Analytics products are developed to satisfy a specific business question or operational outcome, providing the right information in the right format, and tools are fit-for-purpose.  Focusing on outcomes drives how analytics products are designed, modeled and delivered to the consumers, and ensures analytics products deliver maximum value.
  • User-friendly:  Analytics products and tools are intuitive to use, readily accessible, searchable, and easily understood.  Analytics tools, data science tools, and the analytics products created using them (e.g. dashboards, maps, predictive models), must be easily accessible and usable from anywhere (including from theatres of operations where applicable) and at any time.  Analytics products may be incorporated into existing systems, views or operations.  Senior decision makers should be able to consume key products in just a few clicks.  The amount of “friction” to access analytics (e.g. account creation process, onboarding mechanism, password protection) should be minimized to the extent possible.
  • Governed: Analytics products are developed using defined validation and approval processes.  Product certification/validation will ensure that products have the necessary quality and integrity.  Additionally, clear authorities, accountabilities and responsibilities are needed to ensure that analytics efforts are accurate, aligned to business needs, and worthwhile.  Governance also ensures that analytics processes and products are ethical (e.g. through the use of Government of Canada’s Algorithmic Impact Assessment), and secure.
  • Trusted: Analytics products and the processes used to create and maintain them are documented so users understand data sources, visualizations, transformations, and limitations.  By being transparent about the methods used to create analytics products, and having versioned source code available for examination, the results can be reproduced and independently verified, and consumers will have greater trust in the insights provided by those analytics products.
  • Consistent: Analytics products are aligned to the requirements set out by functional authorities for their subject areas.  Analytics products that address the same issue or area (e.g. finance, HR) should be in the same format, present the same metrics, and be accessible from the same system of records.  By doing so, comparisons over time and between organizations can be more easily accomplished.

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Although there is a wide range of analytics stakeholders in the DND/CAF, they can be broadly categorized as:

  • Consumers:  Stakeholders who consume analytics products as part of their decision-making.  They can be any Defence Team member, at any level, both military and civilian.
  • Enablers:  Stakeholders who develop or support the development and implementation of analytics products.  They can be any Defence Team member at any level, and either military or civilian; they may also be contractors with specialized skills.

It should be noted that any individual may be a consumer or enabler at different points in time.

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Analytics Capabilities

The following analytics capabilities will be available:

  • Self-serve capabilities:  Access to data queries and data exploration within lines of business, descriptive analytics (i.e. BI tools and products), and some diagnostic analytics;
  • Specialized capabilities:  Specialist-provided data integration from different sources/lines of business, advanced diagnostic analytics, predictive analytics, prescriptive analytics, and artificial intelligence;
  • Governance and coordination capabilities:  Specialist-provided governance processes, standards and best practices; and
  • User support capabilities:  Enabling support including business intake, data enablement, user permissions, acceleration support, training, as well as maintenance and IT operations.

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Infrastructure and Tools

A proper infrastructure is required to build and consume analytics products, including the required analytics tools (BI tools, advanced analytics tools, and data science tools), computing power, and platforms / environments (staging environments, data hubs, data warehouses and data lakes).

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While out-of-scope for this document, the successful adoption of analytics is also dependent on having the appropriate data management foundations in place, as well as having the data to analyze. These include

  • Data management foundation:  Strong data management processes throughout the data lifecycle are important to the success of analytics, specifically data governance, data quality, data security, data architecture, and document and content management; and
  • Data sources:  Having a wide variety of data sources and data types (e.g. structured and unstructured) available increases the value generated from analytics.  Application data, internet of things (IOT) and sensor data, partner data, open data, and standalone data should all be available for use in analytics.

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Roles and Responsibilities

The successful delivery of the Analytics vision requires the commitment and resources of stakeholders across the Defence enterprise, as described in the following table:

ADM (DIA) ADM (IM) All L1s
  • Establish and lead governance for analytics
  • Develop standards and best practices for analytics
  • Coordinate activities among L1s including data architecture and master data management
  • Provide standardized training, and identify training requirements
  • Facilitate data discovery
  • Consolidate business requirements for analytics tools and environments, and provide user acceptance of tools and environments
  • Provide input to IM’s roadmap for analytics tools and environments
  • Identify business requirements for analytics environments, and provides user acceptance of environments
  • Ensure self-serve analytics capabilities are in place for L1s to use
  • Develop a value realization framework to measure and track progress
    Develop a set of contract vehicles for full-serve analytics capabilities
  • Create data supply chain processes
  • Develop Extract, Transform, Load (ETL) scripts and Application Programming Interface (API) to provide data integration and ingestion
  • Design and implement affordable analytics capabilities to meet departmental user requirements
  • Maintain a roadmap and architecture for analytics tools and environments
  • Operate the infrastructure and tools to minimize downtime and maximize availability
  • Upgrade infrastructure to provide bandwidth for near real-time data streaming
  • Support data architecture, data management and data quality initiatives
  • Establish and manage master data management processes for their data domain
  • Lead data quality initiatives
  • Consume analytics as part of planning and decision-making processes
  • Develop descriptive and diagnostic analytics to support key decisions
  • Gather and provide requirements for advanced diagnostic analytics, predictive analytics, or prescriptive analytics in support of key decisions
  • Submit requests for multi-source data integration
  • Provide analytics support to users within the L1 organisation
  • Participate in analytics governance
  • Build, share and grant access to analytics products, tools, techniques and best practices as part of a community of interest

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Footnote 1

The guiding principles of the DND/CAF Data Strategy are: data are a shared asset; data are accessible; data are secure; data are trusted; and, data are managed ethically.

Return to footnote Return to footnote 1 referrer

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