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DOD Data Strategy: Capitalizing on Strategic and Tactical Opportunities

In 2020, the Department of Defense (DOD) established a new data strategy that focuses on making the department data-centric, with the ability to use data at speed and scale. The DOD Data Strategy sees data management as a resource that can create and maintain a battlefield advantage. It also emphasizes the need to treat data management with the same priority given to weapon systems. Because it’s so well designed for transforming an organization into a data-centric enterprise, many businesses are following this data strategy framework. 

What Is the DOD Data Strategy?

The DOD Data Strategy is designed to “unleash data to advance the National Defense Strategy.” DOD Chief Information Officer Dana Deasy describes the value of data this way: “Data is the ammunition in the Digital Modernization Strategy and is increasingly central to warfighter advantage on and off the battlefield.” With this strategy, DOD leaders aim to treat data as a weapon system and “manage, secure, and use data for operational effect.”

While the DOD Data Strategy includes a vision, focus areas, guiding principles, capabilities, and goals, this article focuses specifically on the guiding principles and goals, which will benefit any organization, whether in the private or public sector.

8 Guiding Principles 

The foundational principles that guide the DOD Data Strategy help create a context for the components of the strategy. The department outlined the principles in a media release:

  • Data Is a Strategic Asset: DOD data is a high-interest commodity and must be leveraged in a way that brings both immediate and lasting military advantage.

  • Collective Data Stewardship: DOD must assign data stewards, data custodians, and a set of functional data managers to achieve accountability throughout the entire data lifecycle.

  • Data Ethics: DOD must put ethics at the forefront of all thought and actions as they relate to how data is collected, used, and stored.

  • Data Collection: DOD must enable electronic collection of data at the point of creation and maintain the pedigree of that data at all times.

  • Enterprise-wide Data Access and Availability: DOD data must be made available for use by all authorized individuals and non-person entities through appropriate mechanisms.

  • Data for Artificial Intelligence Training: Data sets for AI training and algorithmic models will increasingly become the DOD’s most valuable digital assets and we must create a framework for managing them across the data lifecycle that provides protected visibility and responsible brokerage.

  • Data Fit for Purpose: DOD must carefully consider any ethical concerns in data collection, sharing, use, and rapid data integration as well as minimization of any sources of unintended bias.

  • Design for Compliance: DOD must implement IT solutions that provide an opportunity to fully automate the information management lifecycle, properly secure data, and maintain end-to-end records management.

7 Goals for the Federal Data Strategy

The DOD Data Strategy’s goals are vital for any data management program. Let’s dive into each one.

1. Make Data Visible

According to Seagate’s 2020 “Rethink Data” report, only 32% of data available to enterprises is put to work. The remaining 68% is unleveraged. One of the main reasons for this problem is that users can’t locate the data sets that are relevant to their needs. Because valuable data comes from so many different sources, it’s often siloed in various locations. To make data visible, data owners must make their data visible to authorized users by making it easily discoverable across the enterprise. Ideally, data will be made available in a single source of truth such as a secure cloud data warehouse with appropriate metadata standards.

2. Make Data Accessible

Authorized users also must be able to retrieve the data when they need it. APIs should connect the data platform to tools that allow users to retrieve, share, manage, and analyze data. Additionally, the process for authorizing or denying users should be efficient so data can be put to use quickly, without time-consuming roadblocks.

3. Make Data Understandable

Uses must be able to recognize the content, context, and applicability of data. Without context, data interpretation and analysis could be flawed. The ability to truly understand data dramatically reduces risk and enables teams to react and respond for the best outcome. From a practical standpoint, this means presenting data in a standardized way that preserves semantic meaning and having processes to create, align, implement, and manage business vocabularies. 

4. Make Data Linked

Any analytics program will require integrating various data sets. Deriving accurate insights depends on including all data relevant to a query and understanding the relationships between data sets. For this reason, data must be linked so relationships and dependencies can be seen. 

5. Make Data Trustworthy 

Data quality is crucial for trustworthy decision-making. This is especially critical when it comes to decisions of extreme consequence. Implementing data quality management techniques and strategic data governance in a consistent manner will help ensure data quality. 

6. Make Data Interoperable

The DOD Data Strategy states that “properly exchanging data between systems and maintaining semantic understanding are critical for successful decision-making and joint military operations.” When data is being generated in a variety of formats from many different sources, it’s vital to have a data platform capable of ingesting unstructured and semi-structured data from these various systems.

7. Make Data Secure

Needless to say, DOD data must be protected while at rest, in motion, and in use. Protecting data at the network level is no longer enough. Identity management, compliance with robust security standards, and data loss prevention technologies are all vital components of a data security strategy. 

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