Recognize the data we create and support as a critical resource that can provide competitive advantage and drive organizational success is how we will effectively provide the right users with the right data at the right time.

• Implement data tagging and metadata management to enhance data discoverability, lineage and governance across cloud vendors.
• Develop an Application Programming Interface registry and enforce security leveraging the DISA Zero Trust Architecture and DISA Data Catalog.
• Implement Data Classification Standards. Partner with the DISA Risk Management Executive Office to classify data based on sensitivity, criticality and regulatory requirements to guide its handling and protection.

We will achieve greater efficiency and scalability by centralizing and standardizing the methods of sharing data assets, eliminating redundancies and ensuring data is easily accessible and interoperable across our organizations.

• Consolidate data sharing requests through a centralized program to streamline the process and ensure compliance with regulations and policies.
• Collaborate with key stakeholders throughout DISA to establish governance and implementation of centralized data warehouses to provide sources of high-quality data and improve accountability of data.
• Improve interoperability by setting agency data standards and information models. The goal is to ensure data generated by one system can be easily understood and used by another without extensive translation or transformation.

Mature data improves the ability to make actionable decisions to accomplish our mission. Decision-makers must be able to rely on trusted and quality data to make critical decisions and manage risk.

• Continue to produce clear policies and standards for data governance to ensure data integrity, security and compliance throughout the entire data lifecycle.
• Conduct data quality and data management assessments, provide plans for enhancements, and monitor to continuously evaluate and improve data accuracy, completeness and consistency.

Using data-driven decision-making empowers us, at all levels, to operate more efficiently by improving resource use, responsiveness, automation and information sharing. 

• Create and implement descriptive, predictive and prescriptive analytics models to support decision-making processes within DISA organizations.
• Provide knowledge management assessments, provide plans for enhancements, and monitor to continuously evaluate and improve decision-making through information sharing.

AI and ML adoption will be achieved by investing in scalable data infrastructure, fostering cross-functional collaboration, and streamlining model development.

• Catalog and advertise AI and ML models for our workforce to leverage.
• Market and increase the use of the DISA Data Lab self-service analytics tools and platform to empower users across the organization to explore data and generate insights.
• Optimize our data infrastructure to support AI and ML models, including cloud storage, data lakes and high-performance computing environments.

The establishment of data standards and information models ensure interoperability and establishes authoritative data sources but also promotes a common vocabulary, crucial for using data across various systems.

• Register authoritative data sources of our information as single, trusted sources of information.
• Conduct data quality assessments through our enterprise and monitor processes to continuously evaluate and improve data accuracy, completeness and consistency.
• Implement interoperable data solutions compatible with various cloud platforms to facilitate data portability and flexibility.

By increasing data literacy across DISA, we empower the workforce to make data-informed decisions and contribute to a more data-driven culture.

• Offer comprehensive training programs to upskill employees on data literacy, analytics tools and best practices.
• Establish data challenges or competitions that encourages our workforce to solve business problems using data analytics and visualization tools.
• Recruit and retain data-centric individuals to increase attention to data literacy.

When everyone in our workforce understands their role in maintaining data integrity, ensuring compliance of data governance and standards, the agency will achieve a culture of responsible and ethical use of data.

• Host data stewardship workshops, brown bags and lunch and learn opportunities to promote data-centric culture.
• Increase data specific job series in our workforce to increase the number of billets focused on data responsibilities.
• Designate and include data stewardship responsibilities as part of individual’s performance elements.

For us to stay ahead of our adversaries and remain competitive, effective knowledge management ensures that valuable information is accessible, reusable and properly stored.

• Continue the digitization effort of increasing data visualization agents across DISA used for information sharing, reporting and decision-making.
• Foster cross-functional collaboration between data experts, knowledge managers and business stakeholders to drive data-driven initiatives.
• Develop clear process and procedures for capturing, categorizing and maintaining knowledge. Implement these procedures into governance practices in our information storage and sharing environments.