Data strategies are important for ensuring that efforts by data teams and stakeholders have a consistent approach and purpose. And the Department of Defense’s (DoD) data strategy is a published example of the sort of guidance needed to achieve consistent quality out of data efforts in a high-stakes enterprise environment. It’s also a signal that the DoD is taking data seriously and claiming it as a strategic asset. This is particularly meaningful to the learning technology community, as the DoD is served directly by the Advanced Distributed Learning (ADL) Initiative, the group that published both the SCORM and xAPI standards. And data efforts at the DoD, guided by this strategy, could have real impact on the future of how learning data is collected and used. In this blog post, we’ll discuss their data strategy and how it applies to learning and development.
What is the DoD’s data strategy? What is its purpose/goal?
In October 2020, the DoD published a new data strategy, and stated vision is to be “a data-centric organization that uses data at speed and scale for operational advantage and increased efficiency.”
In reading the full strategy, a key concept that is repeatedly referenced is “operationalizing” data. This echoes the journey of many industry participants who are moving from using data as a rearview mirror to integrating analytics directly into daily operations. This is enabled primarily by having data available collected and processed as quickly as possible, hence the DoD’s emphasis on the “speed and scale” of data.
In practical terms, the purpose of the DoD’s data strategy is fundamentally about improving the availability and quality of data for decision makers at every level of military service. This is proposed through establishing an enterprise data management practice “to ensure that trusted, critical data is widely available to or accessible by mission commanders, warfighters, decision-makers, and mission partners in a realtime, usable, secure, and linked manner.”
This core purpose is articulated further in the various goals that are outlined in the strategy—which address the visibility, accessibility, coherence, trustworthiness, interoperability, and security of data. All of these goals are ultimately in service of using data for decision making in every applicable scenario.
Beyond this, the strategy expands on the capabilities, guiding principles, and key initiatives to move toward accomplishing those goals. It discusses how the DoD’s existing practices around interoperability, standardization, and data governance should be used to full advantage. It also covers:
- principles for collecting the right data in the right way,
- how data should be considered a strategic asset, and
- the ethics involved in the collection and access of data.
Finally, it lays out the key, tangible areas of focus to drive efforts—including collaboration among major DoD parties, supporting decision making of senior leaders, and establishing new analytics on practical concerns such as budget, procurement, inventory, logistics, and personnel.
How can I use DoD’s data strategy to inspire my own plan?
The DoD document serves as a great model to use in designing your own data strategy. It is fairly complete in its scope, covers broad data concerns at an appropriately high level, and lays out the guiding principles and areas of focus to help practitioners move forward. Each section provides a valuable prompt:
- What is your organization’s vision for leveraging data?
- What are the guiding principles for ensuring consistency and quality in the work?
- What existing capabilities can you use to your advantage?
- What are the primary goals for the collection and use of the data?
- What are the key areas of focus for your initial efforts?
Having answers to these questions will put you ahead of the competition, by providing a coherent set of signposts in the ongoing rush to collect and use more data throughout the organization.
Want to learn more about data governance?
We’ve compiled everything you need to know about xAPI Governance into this handy guide—which covers tools, technology, and best practices for cleaning and maintaining good data.
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