Listen to article:
By Patrick Elder
Director, ECS Data ＆ AI CoE
and Michael Bechara
Senior Solutions Architect
Improving Data Maturity to Meet the Federal Data Strategy
Government agencies face an ever-expanding variety of data assets and complex data operations. It’s imperative that they navigate these challenges to meet the standards of the Federal Data Strategy (FDS), which calls for the use of high-quality, accessible, and usable data for evidence-based decision-making. To do so, it’s essential to use a data maturity model — complemented by data mesh concepts — to assess where your organization’s data maturity level is. Then, leadership can identify iterative steps that deliver short-term mission value while improving data maturity for the long term.
In previous entries of our series on meeting the FDS, we explored how leveraging data to its full potential requires moving from traditional data architecture to a distributed model, also known as data mesh. (For more background on the goals, requirements, and projected timelines of the FDS, as well as a detailed explanation of data mesh architecture, including the data mesh pyramid, data catalogs, and data products, we invite you to check out parts one and two of this series.) In this article, we will:
- Establish the fundamentals of what any effective data mesh maturity model should look like.
- Provide an example of a maturity model against which an organization could self-evaluate and determine its maturity level.
- Explain why data maturity is key to meeting the standards of the FDS.
How Does Data Mesh Improve Data Maturity and How Do You Implement an Effective Data Mesh Maturity Model?
Government agencies are constantly challenged to meet mission needs with the right data at the right time. Without secure data sharing architecture, analytic effectiveness suffers and data collection efforts are duplicated, reducing capacity and trust. Data mesh offers a robust architecture to distribute data assets into self-service catalogs, enhancing data accessibility, usability, and governance within an organization. Implementing data mesh principles, from basic to advanced levels, improves data maturity and supports the continuous evolution of data practices, operations, and quality.
What does an effective data mesh maturity model look like? It should be comprehensive and evaluate organizational data governance, management, and infrastructure across four key categories:
Data Maturity Criteria
Data as an Asset
Recognizing the importance of data resources in achieving enterprise goals and fostering data literacy throughout the organization. Mature organizations rely on data products for core operational activities and decision-making.
Self-service Data
Emphasizing the availability and discoverability of data products in an enterprise data catalog, enabling mission-speed analysis without barriers.
Data Governance
Standardizing the management and control of data assets to ensure they are published, structured, described, integrated, and secured properly. This includes providing observability and lineage for impact analysis and audit requirements.
AI and Analytics
Applying analytical tools to enhance data visibility, outputs, and conclusions. Mature use of AI and analytics improves performance, reduces costs, and increases quality by leveraging reliable, quality, and trusted data products.
By evaluating these criteria against a data mesh maturity model, you can determine your organization’s maturity level for each category. An effective model not only helps in self-evaluation but also in setting clear goals for enhancing data maturity. For example, if you self-evaluate against ECS’ own data mesh maturity model below, is your organization crawling, walking, or running?
The ECS Data Mesh Maturity Model
Fragmented
Data Use
- Enterprise data is not widely identified.
- Data governance and sharing across organizational units is ad hoc.
- Data products lack sophistication.
- Analytics are basic and limited.
Emerging Data Mesh Principles
- Data products are in wider use but still subject to silos. Data inventory is rolling out.
- Data governance and quality improves but with limited collaboration.
- Early formation of a data catalog to underpin a data mesh.
- Data products are built to provide some business services.
- Analytics expand to exploring predictive tools and models.
Transitioning to a Data Mesh
- Centralization of data inventory across an enterprise.
- Established data catalog for the data mesh enables greater collaboration across the enterprise.
- Data operations from cataloged data are adopted across the enterprise.
- Basic analytics and reporting adopt automation while advanced models expand beyond pilot phases.
Integrated Data Mesh Principles
- Enterprise data inventory has identified all organizational data assets.
- Data catalog has effective metadata to serve wider userbase within the organization.
- Greater levels of self-service for enterprise data, with users empowered to improve data governance and controls.
- Real-time, predictive, and prescriptive analytics are trusted and embraced for business activities.
Strategic Enterprise Data Mesh
- Data mesh contains a high-quality and cataloged inventory of enterprise data assets.
- Users are empowered to leverage enterprise data and use self-service features.
- Governance activities are user-driven with data access, discovery, and quality subject to largely automated processes.
- AI and analytics are embedded across the enterprise’s activities with data mesh underpinning MLOps.
Maturing Your Organization to Meet the Federal Data Strategy
We’ve established the importance of meeting the standards of the FDS, not only to achieve more effective and efficient government operations, but to foster transparency, accountability, and public trust in accordance with the principles of good governance.
Increased data maturity is critical to meeting those standards because it enhances data quality, accessibility, and usability, and supports compliance with data governance requirements. To get there, however, your organization must first understand its data maturity needs. An effective data mesh maturity model helps define those needs, while data mesh architecture helps meet them.
Are you ready to meet the standards of the FDS with data mesh architecture? ECS’ experts are committed to helping your organization improve its data maturity and evolve toward the enterprise model of the future.
