Attachment (1) DRAFT DHA SOO.docx

Defense Health Agency (DHA) Data Governance: Advancing a VAULTIS-Guided Framework for Healthcare and Readiness Statement of Objectives

Solicitation Number:

Version: 4 Date: DD Month YYYY

PART 1

1.0 GENERAL INFORMATION

This statement of objectives is designed to support the transformation the Defense Health Agency's (DHA) data governance and strategy to be aligned with the DoD Data Strategy and its seven goals of making the data visible, accessible, understandable, linked, trusted, interoperable and secure (VAULTIS).

1.1. INTRODUCTION: A Fragmented Foundation for Critical Missions

DHA enables the Army, Navy, and Air Force medical services to provide a medically ready force and ready medical force to Combatant Commands in peacetime and wartime. The DHA supports delivery of integrated, affordable, and high-quality health services to MHS beneficiaries and is responsible for driving greater integration of clinical and business processes across the MHS. Other organizations that may be supported include HA, the Services, Active Duty, Reserve, National Guard, the DoD, and other agencies. The scope of work will include support to the DHA mission to improve health and build readiness – making extraordinary experiences ordinary and exceptional outcomes routine – anytime, anywhere, always.

DHA Priorities include:

DHA operates at the nexus of national security and public health, providing vital medical support to service members, their families, and beneficiaries worldwide. At the heart of this immense responsibility lies data – the lifeblood of decision-making, patient care, logistics, and operational readiness. However, the DHA currently faces a critical challenge: a multitude of disparate data systems and devices that operate in silos, unable to communicate effectively. This fragmentation creates significant hurdles:

· Operational Inefficiencies: Multiple systems provide necessary data for the same operation, leading to redundancy, delays, and a lack of a unified operational picture.

· Vulnerability to Cyber Threats: Antiquated systems are inherently more susceptible to sophisticated cyberattacks, jeopardizing sensitive patient and operational data.

· Impeded Decision-Making: The complexity and lack of integrated data make it exceedingly difficult to implement best practices for both administrative and critical medical decisions, directly impacting patient outcomes and resource allocation.

· Barriers to Innovation: The inability of systems to communicate prevents the seamless integration of cutting-edge technologies like Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and Optical Character Recognition (OCR), leaving DHA unable to fully leverage advancements that could revolutionize healthcare delivery and administrative improvement. This ultimately comes at a detriment to both the taxpayer and the patient population the agency serves.

1.2 BACKGROUND: The Strategic Imperative of Data Governance for the Defense Health Agency

As a Combat Support Agency, the DHA leads the Military Health System (MHS) to support our nation by improving healthcare and building readiness at the crossroads of efficiency gains and innovation in a digital health battlespace. In the complex and mission-critical environment of the DHA, data is not merely information; it is the lifeblood of every operation, from patient care to global military readiness. The sheer volume, diversity, and sensitivity of the data managed by DHA – encompassing everything from clinical records and logistics to financial figures and operational intelligence – necessitate an unwavering commitment to its precise and responsible management. Recognizing the immense scale and sensitivity of its operations, DHA understands that the effective stewardship of this vast data ecosystem is paramount. Without a robust framework, the integrity, security, and utility of data can be compromised, directly impacting mission success.

At its core, data governance for DHA means establishing and enforcing a comprehensive system of policies, processes, standards, roles, and responsibilities that align with the DoD VAULTIS goals. This framework ensures that DHA's data is managed as a strategic asset, beginning with its fundamental attributes:

· Data Quality: This ensures that all clinical records, logistical data, financial figures, and operational intelligence are consistently accurate, reliable, and fit for purpose. For DHA, this directly contributes to patient safety, treatment efficacy, and optimal resource allocation, ensuring that decisions are made based on trustworthy information.

· Data Security and Privacy: Given the highly sensitive nature of Patient Health Information (PHI), Personally Identifiable Information (PII), and classified operational data, a robust governance structure protects against cyber threats, unauthorized access, and misuse. This is paramount for ensuring compliance with HIPAA, federal regulations, and national security directives, safeguarding the trust of service members and their families.

· Data Accessibility and Usability: Effective governance also focuses on making the right data available to the right personnel at the right time, in a format that enables rapid and informed decision-making. This is crucial across disparate systems, whether in high-stakes clinical environments or dynamic operational settings, facilitating expedited and effective action.

· Data Interoperability and Standardization: A key objective is to create common data models, definitions, and formats. This facilitates seamless communication and information exchange not only between different DHA systems but also with internal and external agencies such as the Department of Defense (DoD) and the Department of Veterans Affairs (VA), thereby eliminating data silos and fostering a unified view of critical information.

· Data Lifecycle Management: Governing data throughout its entire lifecycle is essential. This defines clear processes for data creation, storage, usage, archiving, and secure disposal, ensuring ongoing compliance, operational effectiveness, and data integrity over time, while mitigating risks associated with outdated or unmanaged data.

· Data Stewardship and Ownership: Central to this framework is the clear assignment of roles and responsibilities for data assets. This ensures accountability for data quality, security, and policy adherence across the organization, fostering a culture of data responsibility.

· Regulatory Compliance and Auditability: The governance model ensures strict adherence to all relevant federal laws, DoD directives, and healthcare regulations. It also mandates the maintenance of transparent audit trails to demonstrate compliance and build confidence in DHA's data handling practices.

· Supportive of Innovation: A forward-looking data governance strategy is designed to enable future capabilities. By structuring and preparing data appropriately, it facilitates the effective application of advanced analytics, artificial intelligence, and machine learning to derive critical insights, predict outcomes, and automate processes, driving continuous improvement and innovation for the DHA.

Establishing a comprehensive data governance framework is a strategic imperative that is foundational to the DHA's ability to deliver world-class healthcare and maintain military readiness in an increasingly data-driven world. This foundational effort will enable the DHA to leverage its data assets fully, ensuring they consistently contribute to mission success and taxpayer value.

.2.1. Why Data Governance is Critical to the Defense Health Agency's Mission

For an organization as globally distributed and mission critical as the DHA, the effective management of its vast and sensitive data is not merely an operational preference; it is an absolute necessity. Data governance is the fundamental enabler, allowing DHA the ability to deliver on its mission. Without a robust and comprehensive data governance framework, the very pillars of the DHA's effectiveness would be compromised.

The requisite for strong data governance stems directly from its profound impact across key organizational functions:

· Improved Patient Care and Outcomes: Effective data governance ensures that patient data is accurate, integrated, and readily available. This translates directly into better diagnoses, more effective treatments, and truly personalized healthcare, ultimately improving patient outcomes and saving lives.

· Enhanced Operational Readiness: Beyond clinical care, DHA plays a pivotal role in military readiness and global humanitarian efforts. Real-time, reliable data on personnel deployment, equipment availability, and medical supply chains is vital. Data governance ensures this critical information is accessible and trustworthy, directly supporting agile decision-making in dynamic operational environments.

· Strategic Decision-Making: For DHA leadership, a unified common operating picture is indispensable. Data governance consolidates disparate information into a clear, comprehensive view, empowering informed strategic decisions on resource allocation, policy changes, and long-term planning, ensuring the agency's effectiveness and foresight.

· Risk Mitigation: In an era of escalating cyber threats, protecting sensitive data is paramount. Data governance actively reduces vulnerabilities to cyberattacks, prevents costly data breaches, and ensures stringent adherence to legal and regulatory compliance frameworks. This proactive approach safeguards not only the agency's reputation but also the privacy of those it serves.

· Cost Efficiency and Resource Optimization: Beyond direct security, data governance drives fiscal responsibility. By eliminating redundant data, optimizing processes across the enterprise, and enabling greater automation, it optimizes resource utilization and generates significant cost advantages, ultimately delivering better value for taxpayer dollars.

· Enabling Future Technologies: Looking ahead, data governance is the bedrock for technological advancement. It meticulously prepares and structures the foundational data infrastructure, making it ready to leverage the transformative power of AI, machine learning, and other emerging technologies for predictive analytics, advanced research, and intelligent automation. This ensures DHA remains at the forefront of medical innovation.

· Building Trust: Finally, and perhaps most importantly, robust data governance builds and sustains trust. It assures the public and military personnel that their sensitive health and personal data is handled responsibly, ethically, and with the utmost care, fostering confidence in DHA's unwavering commitment to their well-being.

In essence, data governance in DHA is about transforming a complex, fragmented data landscape into a coherent, trusted, and agile information ecosystem. This transformation directly supports the agency's dual mission of providing exceptional healthcare and ensuring robust support for military operations, now and into the future.

1.3 OBJECTIVES: Data Management and Governance

To overcome these challenges and fulfill its mission more effectively, the DHA recognizes the necessity to transform its data strategy and model. The DHA's vision is to establish a comprehensive and unified data management and governance framework that focuses on implementing and executing the VAULTIS goals. This program will evolve the agency's disparate data systems into a cohesive, secure, and intelligent ecosystem that enhances operational performance, decision-making, and supports the adoption of advanced technologies, all while ensuring paramount data quality, privacy, and compliance.

1.4 SCOPE: Establishing a Modern Data Management and Governance Framework

DHA is initiating a critical framework to transform its data infrastructure. This data management and governance structure aims to evolve the current disparate data systems into a unified, secure, intelligent, and compliant ecosystem without centralizing data into a single platform. The objective is to establish and implement a comprehensive framework that supports enhanced operational effectiveness, decision-making, and the adoption of advanced technologies, while upholding high standards of data quality, privacy, and regulatory adherence.

The scope of this comprehensive framework encompasses a series of interconnected activities:

· The data infrastructure will focus on data interoperability. This involves designing, developing, and implementing a decentralized data model capable of ingesting and unifying data from DHA's extensive array of existing data sources by enabling seamless communication and data exchange across the enterprise.

· Building on this integrated foundation, the program will Enhance Decision Support by providing and implementing capabilities for a unified common operating picture. This includes the delivery of customized decision aids and data collection tools specifically tailored for DHA's critical acquisition, administration, logistics, budgeting, and procurement organizations.

· A paramount focus will be on cybersecurity, data privacy, and risk management. This framework will involve implementing measures to strengthen DHA's data security posture, ensuring rigorous data privacy and compliance with all relevant regulations (e.g., HIPAA, PII, PHI). It will also integrate advanced risk identification and modeling capabilities.

· The data framework is designed to enable advanced technology adoption by preparing DHA's data infrastructure to effectively support and leverage cutting-edge technologies such as Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and Optical Character Recognition (OCR).

· To ensure the integrity and utility of data, the program will establish comprehensive data quality and metadata management. This involves defining and enforcing high standards for data accuracy, completeness, consistency, and timeliness. Robust metadata management practices, including the creation of a comprehensive data catalog, data dictionaries, and data lineage tracking, will be implemented, alongside enhanced asset and device cataloging and reporting.

Crucially, the program will formalize data stewardship and lifecycle management. This involves clearly defining roles, responsibilities, and accountability for data ownership and stewardship across DHA. It will also implement comprehensive policies and processes governing the entire data lifecycle, from creation and storage to retention, archival, and secure disposal. To ensure consistent application and oversight, execution of the framework should help establish robust data governance policies and performance monitoring. This involves developing, implementing, and enforcing comprehensive data governance policies, standards, and procedures, along with establishing clear metrics, reporting mechanisms, and auditing processes. Finally, the successful execution and continuity of this program will be supported by meticulous program management and operational continuity. This includes providing comprehensive program and project management, ensuring effective, accurate, timely, and compliant execution of all activities in accordance with established methodologies and contractual requirements.

While comprehensive, this SOO’s scope is specifically defined. It does not encompass the development of new, independent operational systems whose primary function is not data management or integration into the specified data governance framework. Similarly, it does not cover the procurement of hardware or infrastructure not explicitly required for the direct implementation of the data platform as defined by DHA.

PART 2

2.0 DEFINITIONS, ACRONYMS, AND APPLICABLE PUBLICATIONS/INSTRUCTIONS

PART 3

3.0 GOVERNMENT FURNISHED PROPERTY, EQUIPMENT, AND SERVICES

PART 4

4.0 CONTRACTOR FURNISHED ITEMS AND SERVICES

PART 5

5.0 SPECIFIC TASKS

5.1. STRATEGIC OBJECTIVES: Building the Future of DHA Data

This SOO will focus on achieving the following specific objectives, designed to deliver a robust, integrated data environment that supports DHA's strategic goals:

Core System Establishment & Data Cataloging

Given DHA's vast and diverse data landscape, encompassing critical clinical, operational, and administrative information often residing in disparate systems, establishment of core system and data catalog is essential to manage DHA’s data assets effectively. By systematically identifying, documenting, and classifying every data element and asset, the DHA can overcome data silos, enable efficient data discovery, and ensure a shared understanding of its sensitive healthcare data. This foundational cataloging should serve as a prerequisite for robust data governance, allowing the DHA to assign ownership, enforce policies, meet stringent regulatory mandates like HIPAA and FISMA, and ultimately transform raw data into a strategic, trusted asset for informed decision-making and improved patient outcomes.

Objective 1: Establish and Implement a Comprehensive and Centralized Data Inventory Establish a comprehensive, accurate, and dynamic data inventory management system (DIMS) that enhances data governance, improves data discoverability, ensures regulatory compliance, and supports the strategic utilization of data assets across the DHA enterprise.

Identify, discover, and document all relevant data assets (databases, data lakes, applications, systems, files, etc.) across the DHA enterprise, including those managed by components, programs, and operational units.

Develop a standardization data asset identification and onboarding process using the initial population of the DIMS with an industry standard defined percentage (e.g., 80% within in 12 months, 95% within 18 months) of the known DHA data sources and systems.

4. The DIMS should be developed with the capability to continuously discover and update new or modified data assets.

5. Provide the necessary tools and functionalities within the DIMS to support DHA's data governance framework, enabling effective data stewardship and accountability.

6. Establish a measurable system to assign and track data owners, data stewards, and subject matter experts for each data asset and data element.

7. Ensure the ability to link data assets and elements to relevant DHA policies, standards, and regulations is included in the workflow capabilities for data governance processes (e.g., data request approvals, policy updates, issue resolution).

B. Objective 2: Conduct a Comprehensive Analysis of Alternatives (AoA) for DIMS Software Solutions (DHA Enterprise Data Catalog)

1. Evaluate and recommend the optimal Commercial Off-The-Shelf (COTS), Government Off-The-Shelf (GOTS), or open-source software solution(s) for establishing the core DHA Data Inventory and Catalog, considering technical feasibility, cost-effectiveness, scalability, security, and alignment with DHA's enterprise architecture.

2. Deliver a detailed AoA report within 3-6 months of contract award, presenting a comparative analysis of at least four viable DIMS solutions.

3. The AoA report should provide a clear recommendation for the chosen solution(s) with justification based on predefined selection criteria [e.g., metadata management capabilities, integration potential, security posture, Total Cost of Ownership (TCO)].

C. Objective 3: Implement Robust Metadata Management, Including Data Element Definitions

1. Capture, categorize, and manage comprehensive metadata for all inventoried data assets, including detailed data element definitions, covering technical, business, operational, and security metadata.

2. Establish a standardization of metadata schema that aligns with DHA requirements and relevant industry/government standards (e.g., DoD Data Strategy, NIST).

3. Publish comprehensive data element definitions for all inventoried data assets, including but not limited to data type, format, allowable values, business rules, data lineage, source systems, transformations, and usage context.

4. Leverage automated metadata extraction capabilities from various data sources where feasible.

5. Provide the capability to capture and link key metadata elements such as data classification (e.g., PII, PHI, CUI, classified), data owner/steward, data quality metrics, retention policies, and security controls.

6. Leverage Machine Learning (ML) capabilities to automate and enhance the creation and maintenance of data dictionaries for identified strategic data assets, and to ensure these strategic data elements are fully operationalized and accessible for DHA's data initiatives.

7. Implement ML-driven capabilities within the DIMS to infer, suggest, and refine data element definitions and relationships for designated strategic data assets.

8. Demonstrate accuracy and efficiency improvements in data dictionary creation and maintenance through ML over manual methods

9. Operationalize strategic data elements into DHA's key data consumption points (e.g., analytical platforms, reporting tools) as defined by DHA.

10. Develop a process for continuous improvement and retraining of ML models based on user feedback and new data.

II. Proliferation and Maturation of the DHA Enterprise Data Mesh Prototype

DHA has developed a prototype data mesh architecture to modernize its enterprise data infrastructure by enabling domain-driven data ownership, federated governance, and secure data interoperability. This RDT&E-funded initiative supports the continuation of prototype maturation activities by validating people, process, and technology enablers through controlled experimentation, digital testbeds, and sandbox environments. The effort directly supports the DoD Data Strategy, the DHA CDAO roadmap, and AI/ML readiness across clinical, operational, and research domains.

D. Objective 4: Evaluate Federated Governance and Workforce Readiness

1. Conduct pilot experiments with federated data stewardship roles across selected data domains and data systems.

2. Assess the clarity, effectiveness, and responsiveness of assigned data ownership and accountability structures.

3. Pilot training programs and evaluate skill development for data mesh concepts and data product management.

4. Evaluate the feasibility, adoption, and performance of federated data governance across selected domains.

5. Test role clarity, policy execution, and data steward escalation paths.

6. Demonstrate metadata lineage, stewardship effectiveness, and policy audit traceability.

E. Objective 5: Metadata Harvesting and Discovery Services

1. Test automated metadata harvesting across source systems, data products, and APIs.

2. Validate completeness, accuracy, and update frequency of harvested metadata.

3. Evaluate metadata tagging consistency across domains and systems.

4. Assess semantic search, catalog population, and lineage graph generation capabilities.

F. Objective 6: Test and Refine Data Product Lifecycle and Governance Processes

1. Define and exercise prototype workflows for data product creation, publication, discovery, change control, and deprecation.

2. Validate the use of data contracts as enforceable, machine-readable agreements between producers and consumers.

3. Validate policy-to-platform traceability through tabletop exercises and digital enforcement of metadata-first, data-sharing, and access control policies.

4. Evaluate time-to-readiness, repeatability, and data quality enforcement.

5. Simulate end-to-end workflows for data product creation, versioning, SLA enforcement, and deprecation.

6. Measure compliance with data quality (DQ) rules, contract-based access controls, and semantic tagging protocols.

7. Track observability and governance responsiveness to contract violations.

G. Objective 7: Conduct Platform Infrastructure Performance and Scalability Testing

1. Simulate multi-domain data access and concurrent query activity in controlled test environments to evaluate mesh services.

2. Collect metrics on latency, failure recovery, throughput, and policy enforcement scalability under realistic loads.

3. Identify bottlenecks and propose refinements to support future enterprise scaling.

4. Execute performance testing across ingestion pipelines, federated query engines, and metadata services.

5. Simulate multi-domain traffic, failover conditions, and auto-scaling in environments.

6. Monitor and document system latency, throughput, uptime, and observability alerts.

H. Objective 8: Develop and Test Reusable Mesh Components and Templates

1. Build and validate standardized, reusable components (e.g., ingestion pipelines, transformation contracts, lineage tracking modules).

2. Test portability and extensibility of mesh components across multiple data domains.

3. Document component design patterns for future reuse and technology transition.

I. Objective 9: Assess Mesh Integration with AI/ML Testbeds and Data Science Workflows

1. Establish secure pipelines to deliver curated and tagged mesh data to approved AI/ML experimentation environments.

2. Evaluate mesh support for data discovery, explainability, drift detection, and closed-loop feedback in model training and testing workflows.

3. Demonstrate compliance with AI ethical use and DoD Responsible AI guidelines during test execution.

4. Deliver curated, governed data to approved AI/ML sandbox environments.

5. Validate feature delivery pipelines, data lineage, and explainability support.

6. Align test activities to DoD Responsible AI principles.

H. Objective 10: Evaluate Self-Service and End User Experience

1. Test semantic discovery, NLP-enabled search, and federated dashboards.

2. Conduct user acceptance testing (UAT) with stakeholders at MTFs and HQ elements.

3. Evaluate persona-specific workflows, developer SDK usability, and onboarding friction.

III. Additional Objectives

1. Achieve Data Integration and Interoperability:

· To enable seamless communication and data exchange across DHA's multitude of existing, non-communicating data platforms and devices.

· To establish a decentralized data platform (e.g. data virtualization layer) capable of ingesting, unifying, and analyzing vast amounts of data from diverse APIs, data sources, and data formats.

2. Enhance Decision Support and Operational Efficiency:

· To provide DHA personnel with a simplified, secure, and near-real-time common operating picture that integrates critical data for improved administrative and medical decision-making.

· To streamline workflows, improve processes, and facilitate automation through integrated data, leading to significant resource savings and reduced operational complexity.

· To deliver customized decision aids and data collection capabilities for acquisition, administration, logistics, budgeting, and procurement organizations, supporting the Agency’s innovation and data governance strategies as identified by stakeholders.

3. Strengthen Cybersecurity, Data Privacy, and Risk Management:

· To mitigate cybersecurity vulnerabilities inherent in DHA's systems.

· To ensure rigorous data privacy and compliance with all relevant regulations, including but not limited to HIPAA, PII, and PHI guidelines, across all data handling processes.

· To improve situational awareness, identify patterns, and model risks through advanced data analysis and embedded artificial intelligence.

4. Enable Advanced Technology Adoption:

· To unlock the potential for implementing cutting-edge technologies, including AI/ML algorithms, Natural Language Processing (NLP), and Optical Character Recognition (OCR), to enhance data utilization, analysis, and innovation within DHA.

· To ensure the DHA's data infrastructure is adaptable and scalable to support future technological advancements and evolving operational requirements.

5. Establish Comprehensive Data Quality and Metadata Management:

· To define, measure, and enforce high standards for data quality (accuracy, completeness, consistency, timeliness) across all DHA data assets.

· To implement robust metadata management practices, including the creation of a comprehensive data catalog, data dictionaries, and data lineage tracking, to improve data discovery, understanding, and trust.

· To develop a robust system for comprehensive asset/device cataloging, alerts, and reporting, ensuring accurate and up-to-date visibility of DHA's technological landscape.

6. Define Data Stewardship and Lifecycle Management:

· To establish clear roles, responsibilities, and accountability for data ownership, stewardship, and usage across DHA.

· To define and implement policies and processes for the entire data lifecycle, including data creation, storage, retention, archival, and secure disposal, in alignment with regulatory requirements and operational needs.

7. Establish Data Governance Policies, Standards, and Performance Monitoring:

· To develop, implement, and enforce comprehensive data governance policies, standards, and procedures across DHA to ensure consistent and effective data management practices.

· To establish metrics, reporting mechanisms, and auditing processes to continuously monitor the effectiveness of data governance initiatives and ensure ongoing compliance and value realization.

8. Ensure Effective Program Management and Operational Continuity:

· To maintain comprehensive program and project management, ensuring efficient, accurate, timely, and compliant execution of all program activities in accordance with established methodologies and contractual requirements.

· To facilitate a seamless and efficient incoming transition, defining clear plans for coordination, service review, team orientation, and achieving Full Operational Capability (FOC) within prescribed timelines.

· To ensure a smooth and organized outgoing transition, providing comprehensive plans for coordination, service transfer, documentation handover, asset disposition, and personnel out-processing within prescribed timelines.

· To maintain transparent communication and accountability through regular reporting, including monthly updates on program status, progress, and challenges.

5.2 EXPECTED OUTCOME: A Resilient, Intelligent DHA for the Future

The successful execution of this comprehensive data management and governance framework will fundamentally transform the DHA, yielding profound and lasting benefits that directly support its critical mission. This strategic endeavor is designed to cultivate an agile, secure, and data-driven organization, capable of meeting the evolving demands of healthcare delivery and military operations. The tangible outcomes of this program should include but are not limited to:

· Improved Patient Outcomes: At the core of DHA's mandate is the well-being of its beneficiaries. This framework will significantly enhance patient outcomes by providing integrated, high-quality clinical data that empowers healthcare providers with unprecedented decision support, leading to more precise diagnoses and effective treatments.

· Superior Operational Readiness: Beyond clinical care, DHA's global responsibilities demand agile and responsive operations. The program will ensure superior operational readiness through the provision of real-time, accurate intelligence, which is indispensable for supporting complex military operations and critical global health initiatives.

· Significant Resource Optimization: Fiscal responsibility is paramount. By systematically eliminating data redundancies, streamlining processes across the enterprise, and enabling greater automation, the framework will lead to significant resource optimization, ensuring taxpayer dollars are utilized with maximum efficiency.

· Enhanced Data Protection and Assurance: Through modernized infrastructure and robust privacy controls, the program will significantly elevate DHA's data security posture, fostering greater confidence in the safeguarding of sensitive information and ensuring unwavering regulatory adherence.

· Transformative Analytical Capabilities: This program will catalyze the rapid integration of advanced technologies like AI/ML, leading to revolutionary predictive analytics, deeper insights, and the development of intelligent automation across DHA's operations, fundamentally enhancing its future decision-making and service delivery.

· Heightened Trust and Compliance: Finally, and critically, the program will foster boosted trust and ensure unwavering compliance. By guaranteeing that sensitive data is handled with the utmost care, transparency, and strict adherence to all regulations, it reinforces the confidence of service members, their families, and the American public in DHA's commitment to data stewardship.

Ultimately, this data management and governance framework represents a pivotal strategic investment in the DHA's future. It is designed to transform our data landscape into one that is not only resilient and intelligent but also continuously optimized to better serve service members, their families, and the nation for decades to come.

5.3 CONTRACT DELIVERABLES

Data Inventory Management System (DIMS) (Objective 1) The contractor shall provide the necessary tools and functionalities within the DIMS to support DHA's data governance framework, enabling effective data stewardship and accountability.

The contractor shall establish a measurable system to assign and track data owners, data stewards, and subject matter experts for each data asset and data element.

The contractor should ensure the DIMS can link data assets and elements to relevant DHA policies, standards, and regulations is included in the workflow capabilities for data governance processes (e.g., data request approvals, policy updates, issue resolution).

Comprehensive and Searchable Data Dictionary (Objective 1) The contractor shall provide core Data Dictionary Deliverables. This is list not comprehensive and can be tailored to best meet government’s intent:

1. Comprehensive Data Catalog with Search Functionality

· Searchable metadata repository that supports the requirement to implement robust metadata management practices, including the creation of a comprehensive data catalog, data dictionaries, and data lineage tracking

· Advanced search capabilities enabling users to find data elements by name, definition, source system, data type, or business context

· Cross-reference functionality linking related data elements across different systems

2. Standardized Data Element Definitions

· Complete data element catalog covering all data assets with definitions that ensure data accuracy, completeness, consistency, and timeliness

· Business and technical definitions for each data element

· Data type specifications, formats, and allowable values

· Business rules and validation criteria for each element

3. Data Lineage Documentation

· End-to-end data lineage tracking showing data flow from source systems through transformations

· Source system identification supporting the goal of ingesting and unifying data from DHA's extensive array of existing data sources, APIs, and formats

· Transformation documentation including ETL/ELT processes referenced in the sources

· Data dependency mapping showing relationships between data elements

4. Interactive Dashboard and Visualization Tools

· Data dictionary dashboard that delivers information using data visualization techniques to include the use of dashboards so that the data is accessible and understandable

· User-friendly interface supporting data discovery and understanding

5. Data Governance Integration Components

· Data stewardship assignments linking each data element to responsible data owners

· Policy compliance tracking supporting comprehensive data governance policies, standards, and procedures

· Data quality metrics integration showing accuracy, completeness, consistency, and timeliness measures

· Audit trail capabilities for tracking changes and ensuring accountability

6. Technical Integration Deliverables

· API documentation enabling integration with existing DHA systems

· Data exchange specifications supporting interoperability requirements

· Security and access control documentation ensuring compliance with cybersecurity requirements

· Configuration management documentation for ongoing maintenance

7. User Documentation and Training Materials

· Dashboard User Guide

· Data dictionary user manual with search functionality instructions

· Training materials for data stewards and end users

· Best practices documentation for data dictionary maintenance

8. Reporting and Monitoring Components

· Usage analytics and reporting showing dictionary utilization

· Data quality monitoring reports integrated with the dictionary

· Performance metrics demonstrating dictionary effectiveness

· Regular status reporting supporting ongoing governance initiatives

9. Transition and Sustainability Deliverables

· Implementation plan for dictionary deployment

· Maintenance procedures for ongoing dictionary updates

· Integration roadmap with existing DHA systems and processes

· Documentation handover supporting the requirement for comprehensive plans for coordination, service transfer, documentation handover These deliverables should directly support the strategic imperative of establishing a comprehensive data governance framework that enables DHA to leverage its data assets fully, ensuring they consistently contribute to mission success and taxpayer value.

Comprehensive Analysis of Alternatives (AoA) for DIMA Software Solutions Report (Objective 2) The contractor should ensure that AoA report provides a clear recommendation for the chosen solution(s) with justification based on predefined selection criteria [e.g., metadata management capabilities, integration potential, security posture, Total Cost of Ownership (TCO)]. At minimum, the AoA should cover:

· Technical Assessment and Integration Analysis

· Data Visualization and Analytics Components

· Security and Compliance Assessment

· Performance and Mission Impact Analysis

· Implementation and Integration Considerations

· Cost-Benefit Analysis

· Risk Assessment Matrix

· Alternative Comparison Matrix

· Recommendation with Justification

· Implementation Timeline Analysis

DHA Enterprise Data Catalog (Objective 2) The contractor shall provide core Enterprise Data Catalog Deliverables. This is list not comprehensive and can be tailored to best meet government’s intent:

1. Comprehensive Data Asset Inventory

· Complete catalog of all DHA data assets including databases, data lakes, applications, systems, and files across the enterprise

· Asset metadata repository capturing technical, business, operational, and security metadata for each data asset

· Data asset classification system identifying PII, PHI, CUI, and classified data elements

· Integration with existing systems including MHS GENESIS, DHA and VA platforms as referenced in the sources

2. Data Discovery and Search Platform

· Enterprise-wide searchable interface enabling users to discover data assets across the organization

· Advanced search capabilities supporting semantic search, filtering by data type, classification, and business domain

· Data lineage visualization showing data flow from source systems through transformations

· Cross-reference functionality linking related data elements across different systems and domains

3. Data Dictionaries and Definitions

· Standardized data element definitions ensuring accuracy, completeness, consistency, timeliness as specified in the sources

· Business and technical definitions for all cataloged data elements

· Data type specifications, formats, and allowable values for each element

· Business rules documentation governing data creation, usage, and validation

4. Data Stewardship and Governance Integration

· Data ownership assignments establishing clear roles, responsibilities, and accountability for data ownership, stewardship, and usage across DHA

· Data steward contact information and escalation paths for each data asset

· Policy compliance tracking supporting comprehensive data governance policies, standards, and procedures

· Audit trail capabilities for tracking changes and ensuring accountability

5. Data Quality Management Components

· Data quality metrics dashboard measuring accuracy, completeness, consistency, timeliness across all DHA data assets

· Data quality scoring system with automated quality assessments where possible

· Data quality issue tracking and resolution workflows

· Quality improvement recommendations based on catalog analysis

6. Data Lifecycle Management Documentation

· Lifecycle stage tracking covering data creation, storage, retention, archival, and secure disposal

· Retention policy documentation aligned with regulatory requirements and operational needs

· Data archival and disposal schedules with automated alerts and notifications

· Compliance tracking for regulatory requirements including HIPAA and DoD directives

7. Technical Integration and API Services

· RESTful API services enabling integration with existing DHA systems and applications

· Data catalog SDK for developers to integrate catalog functionality into applications

· Integration with ETL/ELT processes as referenced in the Data Integration AOI

· Cloud services integration supporting AWS GovCloud environment requirements

8. Analytics and Reporting Capabilities

· Usage analytics showing which data assets are most/least utilized

· Data relationship mapping identifying dependencies and connections between data assets

· Impact analysis reports showing downstream effects of data changes

· Compliance reporting demonstrating adherence to governance policies

9. Security and Access Control Framework

· Role-based access controls ensuring appropriate access to sensitive data information

· Security classification display clearly marking PII, PHI, and classified data

· Audit logging of all catalog access and modifications

· Integration with cybersecurity requirements ensuring compliance with stringent security standards

10. User Experience and Training Deliverables

· Intuitive user interface supporting up to 100,000 users as referenced in Data Applications AOI

· Section 508-compliant design ensuring accessibility for all users

· User training materials and documentation for catalog utilization

· Help desk support and user guides for catalog navigation and functionality

11. Monitoring and Performance Management

· Catalog performance metrics measuring system availability, response times, and user satisfaction

· Data catalog health monitoring with automated alerts for system issues

· Regular status reporting supporting metrics, reporting mechanisms, and auditing processes

· Continuous improvement recommendations based on usage patterns and feedback

12. Integration with Strategic Initiatives

· AI/ML readiness assessment preparing data for advanced analytics and machine learning

· Support for emerging technologies ensuring the catalog is adaptable and scalable to support future technological advancements

· Innovation enablement facilitating the adoption of cutting-edge technologies across DHA

Robust Metadata Management Process (Objectives 3) The contractor should create robust metadata management framework and standards document that will deliver, connect, and curate data to facilitate informed decision-making across a diverse data ecosystem in support of MHS while ensuring robust metadata management practices across the DHA enterprise.

1. Comprehensive framework defining metadata standards, schemas, and governance processes:

· Metadata schema specifications ensuring data accuracy, completeness, consistency, and timeliness

· Data classification standards for PII, PHI, CUI, and classified information

· Metadata capture and validation procedures

· Integration specifications for complex systems, such as but not limited to, MHS GENESIS, DHA and VA platforms

2. Comprehensive Data Catalog Implementation: Fully functional, searchable data catalog supporting enterprise data discovery:

· Deployed data catalog platform with search and discovery capabilities

· Business and technical metadata for all cataloged data assets

· Data lineage tracking implementation showing end-to-end data flow

· Integration with ETL or extract, load, transform (ELT) processes

3. Data Dictionary and Asset Catalog: Complete data dictionaries and enhanced asset cataloging system:

· Standardized data element definitions across all DHA systems

· Data dictionaries covering technical and business definitions

· Enhanced asset and device cataloging and reporting capabilities

· Cross-reference mapping between related data elements

4. Metadata Governance Process Documentation: Formal processes and procedures for metadata stewardship and lifecycle management:

· Process flowcharts, business process memoranda, and standard operating procedures (SOPs)

· Data stewardship roles and responsibilities matrix

· Metadata quality control and validation procedures

· Change management processes for metadata updates

5. Automated Metadata Management System: Technology solution supporting automated metadata capture and management:

· Automated metadata harvesting from source systems

· Automated quality assessment and validation capabilities

· Dashboard and reporting interfaces for metadata management

6. Data Lineage and Dependency Mapping: Comprehensive data lineage tracking and impact analysis capabilities:

· End-to-end data lineage documentation and visualization

· Data dependency mapping across enterprise systems

· Impact analysis capabilities for change management

· Integration with implementing automated monitoring systems

7. Metadata Quality Metrics and Monitoring System: Performance monitoring and quality assessment framework for metadata management:

· Management indicators/quality metrics for metadata accuracy and completeness

· Automated monitoring and alerting for metadata quality issues

· Regular quality assessment reports and trending analysis

· Compliance tracking for regulatory and policy requirements

8. Training and User Documentation Package: Comprehensive training materials and user guides for metadata management processes:

· User training materials for data stewards and catalog users

· System administration guides for metadata management platform

· Best practices documentation for metadata creation and maintenance

· Story boards, software backlogs, and other documentation supporting the establishment and execution of the Agency's innovation strategy

9. Integration and Interoperability Specifications: Technical specifications for metadata system integration across the enterprise:

· API specifications for metadata system integration

· Configuration management, change control, documentation for metadata systems

· Interoperability standards for cross-domain metadata sharing

· Security and access control specifications ensuring compliance with stringent cybersecurity requirements

10. Business Process Automation Implementation: Automated workflows supporting metadata management processes:

· Automated process flows based upon findings from the business process reengineering analysis

· Workflow automation for metadata approval and publication processes

· Integration with Scaled Agile Framework execution methodologies

· Requirements management approach incorporating agile methods to allow for rapid speed to capability while maintaining traceability

11. Performance and Compliance Reporting: Regular reporting on metadata management system performance and regulatory compliance:

· Monthly metadata quality and completeness reports

· Compliance reporting for HIPAA, DoD, and other regulatory requirements

· System performance metrics and usage analytics

· Continuous improvement recommendations based on performance analysis

12. Strategic Implementation Roadmap: Long-term strategy for metadata management evolution and enhancement:

· Construct that defines how strategy will be executed through processes, organizational structure, decision governance, culture, and supporting/required IT infrastructure

· Technology roadmap ensuring the system is adaptable and scalable to support future technological advancements and evolving operational requirements

· Integration planning with emerging AI/ML capabilities

· Sustainability and maintenance planning for long-term operations

The contractor shall document the scope of the pilot, including participating organizations, governance roles assigned (e.g., domain stewards, custodians, policy reviewers), and specific data scenarios executed. The report shall include a walkthrough of each governance workflow tested—such as data product submission, metadata tagging, access policy application, and escalation of policy disputes.

The Governance Pilot Report shall provide a detailed account of system behavior, user interactions, automation support, and any deviations from expected governance outcomes. It shall include feedback gathered from stakeholders and system logs, and identify issues related to process clarity, policy enforcement gaps, tooling limitations, or scalability concerns.

The report shall conclude with an analysis of the pilot’s outcomes, key findings, and recommendations for improving governance practices within a federated architecture. This includes updates to the Federated Governance Playbook, refinements to decision-making roles and escalation procedures, and insights for broader implementation across DHA domains.

This deliverable will inform the feasibility and…