Data Management

Six Pillars of Digital Transformation

Governs how data is collected, integrated, secured, and used to drive insights and decisions across the enterprise.

  • Treats data as a strategic enterprise asset rather than a secondary "byproduct."
  • Breaks down silos to enable interoperability and cross-mission visibility.
  • Essential for Data Architects, CDOs, and Business Intelligence Leaders.
Back to Framework Explore ODXA
Mission outcomes delivered through integrated digital capabilities Mission Solutions & Capabilities Architectural integration aligns tradeoffs, design decisions, and cross-pillar dependencies Architectural Integration Tradeoffs • Alignment • Design Decisions Cloud, DevOps, emerging compute, and decentralized platforms enabling portable execution everywhere Ubiquitous Computing Edge Computing enables data processing and decision-making closer to where data is generated to support low-latency, resilient, and mission-critical operations. Edge Computing Artificial Intelligence enables systems to learn, reason, and assist decision-making through data-driven models embedded across digital and operational workflows. Artificial Intelligence Cybersecurity protects systems, data, and missions through Zero Trust principles, resilience, and continuous risk management across all domains. Cyber Security Data Management governs how data is collected, integrated, secured, and used to drive insights and decisions across the enterprise. Data Management Advanced Communications provides secure, resilient connectivity enabling data, systems, and people to operate as an integrated whole. Advanced Comms Strategic Domain Organizational Domain Process Domain Digital Domain Physical Domain

Core Capability

Treating data as a strategic enterprise asset by ensuring its quality, accessibility, interoperability, and governance throughout its entire lifecycle.

Definition

Short Definition: Data Management governs how data is collected, integrated, secured, and used to drive insights and decisions across the enterprise.

Long Definition: The Data Management pillar focuses on treating data as a strategic asset by ensuring quality, accessibility, interoperability, and governance throughout its lifecycle. Effective data management enables analytics, AI, and operational visibility while maintaining trust and compliance. Within ODXA, data strategy aligns data use with mission outcomes, organizational models define stewardship, processes manage data lifecycle and quality, digital platforms deliver storage and analytics capabilities, and physical systems generate and transport data.

This Pillar Is

  • Strategic Stewardship: Defining clear ownership and value for every data stream.
  • Interoperability: Ensuring data can flow between different systems and pillars without manual translation.
  • Lifecycle Governance: Managing data from creation to archival/deletion.

This Pillar Is Not

  • Just a Database: It's about the governance and use of the content, not just the storage technology.
  • Data Hoarding: Collecting vast amounts of "dark data" without a clear mission outcome.
  • Manual Cleanup: Successful data management automates quality rather than relying on one-off fixes.
“Data Management provides the Governance Plane that turns distributed data into high-velocity Data Products without requiring centralization.”
AI MODELS Inference DASHBOARDS Exec Views REPORTS Compliance DIGITAL APIs System Interop GOVERNANCE CONTROL PLANE Stewardship • Quality • Policy • Metadata • Interoperability The "Universal Connector" for Enterprise Data CLOUD DATA Multi-cloud Repos EDGE DATA Tactical Sensors ON-PREM DATA Legacy Systems DISTRIBUTED DATA MESH • MANY-TO-MANY VALUE FLOW

In the ODXA framework, Data Management provides the Governance Control Plane that connects distributed sources to distributed consumers. This architecture proves that data value is derived from its refinement and access, not its location.

GEAR Integration & Architect's Map

Data Management provides the Governance Control Plane that turns distributed data into high-velocity Data Products within the GEAR system.

FORGE Methodology in Data Management

Architects use FORGE to move data from passive, siloed repositories to a strategic enterprise asset.

Stage Architect's Focus Key Artifacts
Find Identify data silos, shadow data, and unmapped classification requirements. Data Catalog, Source Inventory
Observe Analyze data movement, quality friction points, and manual handoffs. Data Lineage Map, DQ Report
Reconcile Unify disparate taxonomies and schemas into a shared architectural language. Master Data Schema
Ground Root data strategy in existing storage fabrics and federated data meshes. Data Mesh Governance Spec
Enhance Augment capability via automated pipelines, real-time analytics, and Data Products. Data Product API Spec

Data Dimensions Map

How the Four Dimensions are aligned to ensure data quality and accessibility.

Dimension Data Play Example Check
People Data literacy programs and decentralized stewardship roles. Do data owners understand their quality obligations?
Process DataOps automation and repeatable quality validation loops. Is data quality checked automatically at ingestion?
Policy Retention rules, data sovereignty, and privacy (GDPR/CCPA). Does our data residency comply with jurisdictional law?
Technology Data Fabrics, API-driven meshes, and metadata tagging tools. Can our platform support real-time schema validation?

Data-Domain Intersection

Architect's checklist for aligning Data Management across O-DXA domains.

Domain Data Requirement Verification Point
Strategic Align data collection with Mission Outcomes and value cases. Verify data retention policy aligns with mission needs.
Organizational Define data stewards and owners across business units. Check for cross-departmental data sharing culture.
Process Automate data flow from source to insight (DataOps). Verify DQ validation is embedded in workflows.
Digital Deploy unified data fabrics and API-driven products. Check for metadata tagging and cataloging automation.
Physical Optimize data placement across cloud, on-prem, and edge. Verify hardware integrity for critical data storage.

System-of-Systems Context

Enabling AI

Acts as the "fuel" for AI—providing the clean, high-velocity data required for training foundation models and grounding RAG architectures.

Enabling Edge Computing

Defines how much data is processed at the edge vs. the core, managing "Data Gravity" to optimize latency and bandwidth.

Dependency on Cybersecurity

Relies on persistent encryption and Classification-based Access Control to ensure data is only visible to verified identities.

Dependency on Ubiquitous Computing

Requires a scalable compute fabric to handle "Massive Data Processing" tasks like ETL, indexing, and complex querying.

When to Start Here

Prioritize Data Management if your leaders are getting "Conflicting Answers" to basic questions, or if your AI projects are stalled because data is trapped in disconnected legacy silos.

Frequently Asked Questions

Is Data Governance the same as Data Management?

Governance is the *Strategic* and *Organizational* part of the pillar (the rules and ownership). Management includes the *Process* and *Digital* execution of those rules (the pipelines and tools).

What is a "Data Product"?

A data product is a high-quality, ready-to-use dataset that is treated like a software product—it has an owner, a clear purpose, and an API for consumption.

Does Data Management require a Data Lake?

Not necessarily. Modern architectures favor **Data Meshes** or **Data Fabrics**, which allow data to remain where it's most effective (Edge, Cloud, or On-Prem) while providing a unified virtual view.

Learn More

The Six Pillars

Framework Overview

The ODXA Domains

Learn ODXA Structure

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Next Steps on Your Transformation Journey

Use the Six Pillars as a common language between business leaders, architects, and operators. From here you can dive into pillar pages, listen to interviews, or explore ODXA in depth.

The Six Pillars

Explore the foundational technical capabilities that enable digital transformation, from AI to advanced communications.

Execution Pillars Overview

The ODXA Domains

Navigate the structural layers of the enterprise to align strategy, people, processes, and technology.

Map Domain Structure

Transformation Dimensions

Understand how to balance the critical dimensions of People, Process, Policy, and Technology in every initiative.

Understand Dimensions

FORGE Methodology

Apply our active methodology to Find, Observe, Reconcile, Ground, and Enhance your transformation efforts.

Apply the Practice

Continue Your Journey

Browse all DTA episodes organized by aspect to see architectural guidance in practice.

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