Executive Summary

As an organization mandated to defend Canada and protect Canadians, DND/CAF must make decisions effectively using the best data available.  Increasing the adoption of analytics in Defence is essential to making more data-driven decisions.  For these purposes, analytics involves the computational transformation of data into insights.  This includes:

  • The creation and consumption of data analytics products including reports, dashboards, and advanced analytics models;
  • Tools that allow the creation and consumption of analytics, the environments where analytics products are developed and tested, and data platforms where data can be ingested and processed for analytics;
  • Information Technology (IT) support for the analytics tools, environments, and data warehouses and data lakes;
  • User support to stakeholders, including clients and enablers, for analytics tools and processes.

The launch of Defence Program Analytics (DPA) in 2017 started a coordinated effort to increase adoption of analytics, but the scope of this effort was largely focused on business intelligence (BI) leveraging corporate data.  With the launch of the DND/CAF Data Strategy in September 2019, there is an opportunity to establish a new vision for analytics that will better meet the needs of decision-makers and stakeholders across the organization:

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

This new vision outlines:

  • Stakeholders, including consumers (e.g. decision-makers, analysts) and enablers (e.g. data scientists, data engineers, product managers);
  • Analytics capabilities, including self-serve (e.g. data exploration, diagnostic analytics), full-serve (e.g. predictive and prescriptive analytics), governance, and user support (e.g. training);
  • Tools and infrastructure, including analytics tools (e.g. BI tools, and data science tools), data repositories (e.g. data warehouse(s) and data lake(s)), and environments (e.g. staging, analytics);
  • A process for data ingestion, analysis, insight creation, and consumption; and
  • Dependencies on the Data Strategy implementation to provide the data management foundation (e.g. data quality, data governance, data architecture, data integration), and access to data sources (e.g. transaction data, sensor data, open data, partner data).

To support the vision, guiding principles are presented to explain why analytics in the DND/CAF must be purposeful, user-friendly, governed, trusted, and consistent.

The implementation of the vision is dependent on having modern IT for data and analytics, flexible analytics’ platforms and tools, and the skilled personnel to use and operate them.  It also requires executive support to promote analytics and provide the resources necessary.  A high-level roadmap for this is outlined in three stages:  building capacity, building infrastructure, and continuous improvement.

Long description follows
Figure 1: Vision for analytics
Figure 1: Vision for analytics

DND/CAF Vision for Analytics
To provide Defence Team members with trusted near real-time data-driven decision support, anywhere in the world

Guiding Principles
Purposeful, User-friendly, Governed, Trusted, Consistent

Product managers, Data stewards, Data engineers, Authors, Data scientists, Data operations, User support, Technical support, IT operations

Analytics Capabilities
Self-Serve Analytics

  • Query line-of-business data
  • Descriptive analytics
  • Diagnostic analytics

Advanced Analytics

  • Multi-source data integration
  • Advanced diagnostic analytics
  • Predictive analytics
  • Prescriptive analytics
  • Artificial intelligence

User Support

  • Business intake
  • Data enablement
  • Acceleration support
  • Training
  • User permissions
  • Maintenance
  • IT operations


  • Governance
  • Standards and best practices

Intranet-connected device, Classified network device, Email, Internet

Decision-makers, Analysts, Other consumers

Ingestion, Analysis, Insight creation, and Consumption

Infrastructure and Tools
BI tools, Data science tools, Staging environments, Analytics environments, Data lake(s), Data warehouse(s)

In scope for analytics / In scope for data management

Data Foundation
Data Governance, Data Quality, Data Security, Data Architecture, Document and Content Mgmt

Other Data Sources
Application data, Sensor data, Partner data, Open data, Standalone databases

More information on the above diagram, including definitions, are found in Annex B.

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