DataGov360 Diagnostic
Delivery solutions are implemented primarily by individual functional units and managed by supply data and IT staff. Solutions serve specific information policy, workflow, and compliance for their functional policy requirements and thus have limited reuse alignment or interoperability outside their MIS and GIS systems. Discoverability is limited to advanced users.
Governance has established a role in delivery management to enhance data integration and meet FAIR principles across the enterprise and for open-data needs. Investments have been made in organizational development plans to manage portfolio use cases, existing data assets, and priorities. Governance collaborates on data readiness and quality assessments to identify high-priority use cases and necessary investments in data platforms. Delivery management leadership and architecture have been assigned to design a roadmap supporting the growing portfolio of requirements.
Governance has charted an enterprise delivery management function to manage the investment, development, and operational cycle of delivery platforms, applications, and services as an enterprise service unit for the organization. Delivery management roles are implemented to support maintaining use cases and to guide and implement delivery platforms, applications, and services.
An initial list of high-priority use cases for data and information is identified, likely through informal methods.
Governance has inventoried and backlogged most data and information use cases, implemented high-priority ones, and identified key use cases for knowledge and wisdom. Through outreach, data literacy programs, and precedence discovery, governance facilitates ideation and prioritization across DOTs. Near-term and ongoing implementation focuses on data and information use cases, with knowledge and wisdom use cases planned for future delivery platforms. Governance has also established a capital planning and investment control process integrated with portfolio management to support delivery investment.
The main difference between Level 2 and 3 is the level of implementation. Governance incorporates ongoing maturity in investment control, balancing investment with mission performance metrics and understanding ROI. Advanced delivery management, including agile practices, supports incremental development and DIKW platform implementation. Processes, software, and infrastructure follow cloud DevOps best practices, and customized application configurations enable solution reuse.
Manual data pipeline and system-to-system integrations
Some data pipelines are integrated via data services with initial support for high-velocity data streams and initial investment in enterprise data platforms to support data integration.
Data pipelines are automated to feed data services and data lakes for future, to-be-determined knowledge use cases.
Information workflows are locked in a closed, siloed MIS or GIS system.
Siloed systems now have data service interfaces and initial investment in microservices to allow nonproprietary interface integrations supporting cross-system data updates and reporting integration.
Data and information services meet FAIR data principles with solutions such as simple sign-on integration and limited to no IT intervention. Projects have been undertaken to govern delivery platforms that have been deployed.
Reactive fire drills in response to events to answer the ask: What is happening?
Data warehouses are used as knowledge solutions with pilots to support future knowledge platform architectures modeling future enterprise data analytics and visualization platforms.
Knowledge platforms have permeated the organization. Staff is trained on FAIR use and use of cloud workbenches for execution on data platforms that implement data science principles.
Data archival, recovery, and storage are determined by IT or external recommendations and are handled ad-hoc without formal policies or processes. Most data systems lack established backup plans, with archives limited to key documents and datasets. Basic recovery processes impact continuity of operations across the agency.
Data administrators have established plans for data storage, archival, and recovery, considering data criticality, value, and use. Service level agreements exist for routine recovery and archival operations. The archival plan meets regulatory and operational requirements, with data archived according to set guidelines and schedules.
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Data stewards have not been identified. No or limited means exist for self-service discovery of data assets. Metadata describing data is not acceptable for discovery or records.
Local repositories have limited documentation for niche functional data asset registries. Some core enterprise data assets are discoverable, but most are restricted to function-specific access, integration, and reuse needs. A simple entry point, like a webpage, lists the various registries and repositories.
Stewards are assigned to all repository workflows with clear roles defined across the organizations. The roles should define who is responsible and accountable and who needs to be consulted or informed. End-user discovery features meeting cited repository principles cut across all architecture types, supporting discovery with managed harvesters and search-signal enhancement and workflow integration with documents and records.
