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Government Large Data Program next generation solutions require high data integration to achieve its goals.

There are current major investments in current standard IT Operations in support (Development, Operations, and Maintenance) for major data management, workflow, warehousing functions across the organization. They need to improve upon the solutions and data maturity. Also, strategic alignment, governance, operations, platform, and architecture to achieve this goal. To address this, many are looking to stand-up Chief Data Office to help in Data Strategy, Planning, and Architecture. There are few toolkits or frameworks on how to organize services. Here is one example for Agencies to consider.

Organizations requires data professional services support to help accelerate Strategy, Planning and Architecture efforts. They need this while aligning these activities to other ongoing major investments.

The services need for focus on reducing risk of early phases efforts (strategic planning, vendor prototypes, efficiencies studies). Then they move to operations. This results in a lower risk and faster mean time to big ideas.  These tasks to support program planning will provide the flexibility and quick availability of expertise. It would do so without the initial expense and commitment of sustaining the staff long-term. Also, it would help Organizations improve needs identification.

Example Chief Data Office (CDO) Toolkit of Services

There are many frameworks to organize around. This example is mostly built off CMMI Institute – Data Management Maturity framework combined with other changes and lessons learned we have observed.

Management Strategy

  • Requirements Gathering – Support Inventory backlog through workshops, backlogs, interviews of standard, taxonomic, technology and data requirement issues for future Program FY planning efforts to populate those relationship requirements and needed standards
  • Concept of Operations – Establish the model for how data management will operate at transitional phases across all 5 maturity model dimensions (Strategy, Quality, Operations, Platform& Architecture, Governance). This should include as well, how the organizations will work together across State Agencies and Partners (Federal, Local, etc.)
  • Product Planning – Explore further process for scaling including establish end-stakeholder requirements elicitation and validation, standards and governance, prioritization needs with Governance. This can establish stakeholder priorities, Application Priorities (i.e. Emergency, Transportation, EcoDevo, etc.), Provide User Story Guidance. Establish project planning to support pilots, investigate business case parameters to help establish both budget and program performance functions.
  • Program Transition Roadmap Planning – Plans could be developed to establish vision, strategy, methods, tools, and approaches to creating and managing to large-integration project transition plans over not only time phases, but also when projects hit the integration “chasm” which risks and issues can be registered where sponsorship engagement will need to be increased or decreased.

Data Governance

  • Baseline Data Inventory Development – Research, INVENTORY and evaluate and test the Data quality for initial set of associated priority business objectives; Identify GAPS and integrity issues in data sources, Also, develop mitigation approaches for issues and test.
  • Organization & Governance Development – Integrating into existing desired models and groups, establish data governance management, project governance, roles & responsibilities and transition approaches to mature organization. This could identify initial key stakeholder community for PILOT involvement. Also, initiate ownership and advocacy for Activity. Furthermore, PROJECT STRUCTURE with objectives. Finally, develop basic communication plan for stakeholders.
  • Modeling & Standards Development – Establish Business Glossary, Taxonomies, Data models, and Service Models, and metadata management practices

Data Quality

  • Information, Data, and Requirements Analysis – Cross-agency, Segment, Solution, Product-line/theme, System, etc. Also, requirements gather and analyze to improve product and service requirements, i.e. from Intelligence community
  • Transition & Maturity Quality Model Development – Using best practices, establish a baseline inventory, apply scoring, and establish performance or other methods to establish transition that aligns to projects. This will help establish data quality strategy, data profiling, data quality assessment practices, and data cleansing guidance.

Data Operation

  • Operations & Funding – Funding strategies would need to be considered and possibly beyond Agency or CIO resources or integrating with other efforts at other State Agencies
  • Data Integration Prototype Support with Vendors Technologies – Help Vendors with navigating the data, data requirements, data cleansing, and assuring the tests are based on Agency data, not uncleansed, demo data. Also, help establish 2-3 User Stories with real data, Open APIs, and shows core.

Platform & Architecture

  • Technology Planning – Explore planning both before and with governance for executing Technology RFIs, prototypes, vendor demos, alternative analysis, hackathons, etc. to help increase technology platform needs.
  • System Analysis – Conduct and analyze and recommend tactical production, integration, discovery and use improvements across CIO and Agency major systems. Also, approach would need to INVENTORY existing / owned systems and technologies that are required. Furthermore, it would support ETL/ analysis of data to business objectives and INVENTORY the As-Is suite of analytical tools. Also, use constraints within the organization.
  • Testbed support – Support the Agency efforts to be early explorers of new solutions, technologies, data, and methods. Also, support review of the architecture deliverables, framework, milestones, and dependencies. Furthermore, efforts could establish a testbed to PILOT data usability (visualization, reports, interoperability)
  • Baseline Architecture – Work with Agency and CIO architecture efforts to establish the architecture baseline integrating the performance, process, data, application, technology, management, and security dimensions.
  • Architecture Transformation efforts – Establish a program transformation method, train teams, and lead blueprint efforts to establish architectures for big data, large data integration, real-time integration, performance dashboards, asset conditions, or other core data/functional capabilities.
  • Architecture Principles Development & Training – Establish architecture guidance for data to increase effective and efficient data integration and allow for improved data use, distribution, storage, movement, integration, and movement of data into the knowledge application dimension.

Performance Management

  • Reporting & Tracking – Establish mechanisms for data status and metrics reporting across the 5 maturity model dimensions (Strategy, Quality, Operations, Platform& Architecture, Governance)
  • Quality Surveillance – Establish methods to allow for improved self or automated monitoring and reporting of key data quality metrics.
  • Establish Workforce Training – Establish data management training across all 5 dimensions, and establish ways to measure effectivity of training based on program, division outcomes. This would include formal trainings, brownbags, coaching, and providing advisory on industry, change, best practices, and thought leadership.

Example Tasks Activities may include full data and technology lifecycle analysis and planning  deliverables such as :

  • Requirement draft for Vendor RFPs
  • Prototype execution Support
  • Requirements analysis
  • Prototypes Alternative analysis
  • SETA Operational Efficiency Improvements
  • Agile Program PMO Support
  • Architecture Analysis – Solution, Data, Process, IT, Enterprise.
  • Collaborative Communications Facilitation
  • Data Architecture for Data Platforms – Geospatial, Asset, Open, Big, IoT, MIS Data Service Integration

 A CDO Scope ends typically before Operations & Maintenance, Enterprise Development beyond prototyping or transition, Strategic Planning outside of data or geospatial purview.

-Hope that helps in your CDO office build-out.