DataGov360 Diagnostic
Multiple disconnected data management strategies created by business units to meet regulatory requirements for key assets. These strategies are not linked to enterprise priorities. Executive sponsorship is limited to meeting the mandates.
Business functions across the organization coordinate with each other, the IT department, and the GIS data section to establish priorities, a data management strategy, and goals and objectives to meet regulations and set up data platforms. Executive sponsors support such multiple-stakeholder projects.
Priorities, goals, and objectives from the organization’s vision and mission document, data management program charter, and strategic roadmap are being used to drive investments by executive sponsors. Data assets are tracked against performance goals.
A data management program does not exist at the enterprise level. No charter exists that establishes policies, processes, metrics, requirements, and guidelines for data management across the organization.
A data management program is established, and a charter documents near-term goals and objectives. Auditing, compliance, and architectural technology guidelines have been developed based on factors affecting key data assets.
The charter has been updated to include long-term goals, business values, core data management principles, and program assessment metrics. Auditing, compliance, and architectural technology guidance span all data assets. Policies and standards in the charter are updated as needed.
No enterprise-level strategic roadmap that shows envisioned and ongoing data management programs, projects, or investments exists. Business- and IT-driven decisions and project selection are based on available funding. Benefits and progress are not evaluated.
Key and high-profile data management and integration implementation projects are tracked. Investments in solutions, applications, and technology platforms are based on guidelines in a data management program charter. Implementation progress is measured for the key data assets in the program.
A strategic roadmap categorizes and tracks a variety of data management and integration projects across the organization. The roadmap is built and published at a regular cadence. Projects and solutions that meet the needs and requirements of multiple stakeholders are prioritized based on ROI and are systematically deployed.
Resources exist but are distributed throughout the organization.
Data governance operational body exists (e.g., a data office or data council).
Vision, mission, and resources are performance-aligned and budgeted with executive support.
Data are consumed as they are, and staff can ask questions to individual experts.
A few employees are dedicated to data management functions in some way yet fill data governance roles in other duties as assigned.
Functions and roles are managed by chartered governance.
Roles and responsibilities are not clearly defined within the organization to support the data governance vision.
Managers perceive the value of data and allocate dedicated resources in different business lines—yet with limited cross-cutting roles, enterprise integration toward knowledge is limited.
A dedicated and funded organizational capability manages data within the agency.
Data assets are identified based on lists of datasets available in data systems used for reporting and information management (e.g., Management Information System or MIS). Light metadata are collected about the data assets. For example, data asset ID, owner, type (i.e., primary data type—planning, design, construction, etc.), data asset organization/division, applications, and priority. Data asset inventory is managed in a single tab spreadsheet.
Data assets are inventoried from personal databases and from reports and MIS systems. Data asset profiles will contain information such as priority/importance of asset, asset readiness score, and asset complexity/sophistication score. Data asset portfolio is managed using a multiple-tab spreadsheet with data asset, FEA’s Business Reference Model (BRM), Data Reference Model (DRM), and data application tabs. The spreadsheet is maintained quarterly.
Additional assets are inventoried at Level 3. Unstructured data assets are collected, including document stores, web page collections, directories of surveys, CADs, and unmanaged document stores. Data applications are inventoried that are used to collect and/or deliver/use data. Data assets are mapped to the Logical Data Model (LDM) and business processes, tasks, and activities. Data portfolio is managed in a database and updated quarterly.
The MIS and data applications associated with data creation, workflows, and analysis are identified in the data asset inventory as primary and secondary or Create, Read, Update, and Delete (CRUD) applications.
Data applications and information management systems are inventoried in a separate tab of a multiple-tab spreadsheet, along with light metadata about the application (e.g., vendor/creator, purpose, number of users).
At Level 3, data applications in inventory are mapped to the ARM- and IRM-based taxonomy. Data application information is integrated with the IT portfolio to add/update information and inventory additional data applications.
Data frameworks (taxonomies) are not used to classify the data assets in the portfolio at this level.
Data assets are classified based on BRM- and DRM-based taxonomy. Optionally, data assets may also be classified using PRM. Data portfolio should contain mapping of data assets to the DRM topic/information class and BRM subfunction/service.
Applications Reference Models (ARMs) and Infrastructure Reference Models (IRMs) are used to map data applications in inventory. Data assets are mapped to Performance Reference Model (PRM). BRM and DRM are extended to include information about target processes and LDMs. IRM mapping of data assets is translated into creating technology business management constructs.
