California Statewide Data Strategy 2026-2027

Executive Summary

Vision

Since the 2024 Data Strategy report, California has made significant progress in maturing its data ecosystem. The State Chief Data Officer’s office is established within the Office of Data and Innovation (ODI) and is required to update the State Data Strategy Report every two years and achieve its goals to advance the state’s data ecosystem.

Previous data strategy reports established strategic goals using the analogy of data roads, traffic rules, and drivers to illustrate the state’s data ecosystem. This data ecosystem thrives when the data roads are well-designed, rules are consistently applied, and travelers can safely and efficiently navigate their journeys. Similarly, in the virtual data world, the goal is to avoid data roads that are ineffective, poorly maintained, or confusing. In this analogy, our virtual data world requires the same level of planning, design, and care as the roads and bridges of the real world.

For the 2026 California Data Strategy, the vision is for California to establish a federated, trusted data highway. This highway will serve as a connected network linking the state’s diverse data silos under unified standards, modern infrastructure, and human-centered governance. The data highway analogy represents a user-centered data ecosystem that will empower departments to move data safely and purposefully, providing effective services, equitable outcomes, and data-informed decisions that enhance Californians’ lives.

Strategic Goals and Objectives

Goal 1. Streamline data access

Objective 1: Modernize Enterprise Data Platforms: Accelerate the adoption of modern, cloud-based data platforms, such as cloud data warehouses, and analytics platforms across state agencies.

Objective 2: Champion Improvements to Statewide Open Data: Collaborate with the California Department of Technology (CDT) to update California’s open data program and improve the discoverability, timeliness, and usefulness of public datasets.

Objective 3: Leverage State Processes to Accelerate Adoption of Data Tools: Collaborate with CDT and the Department of General Services (DGS) to enhance pathways to streamline and accelerate data tool adoption.

Goal 2. Improve data management and governance

Objective 4: Enhance Data Interoperability and Standards: Establish data standards and integration tools to ensure systems can talk to each other. Objectives include standard definitions for demographic data, including race and ethnicity for equity measures, geospatial data, data quality evaluation, and program performance metrics.

Objective 5: Scale the Interagency Data Exchange Agreement: Scale existing use of the Interagency Data Exchange Agreement framework to achieve higher volume and efficiencies of cross-agency data exchange. Explore additional opportunities to improve data sharing processes at the state to improve public services.

Objective 6: Privacy-First Digital Identity, Access, and Data Use: Enhance identity and access management while embedding privacy-by-design, data minimization, and secure analytics environments across state programs. Collaborate with CDT to enable state agencies to adopt consistent statewide standards for digital identity that facilitate secure, role-based access to data and reduce duplication of effort.

Goal 3. Spur data use and ability

Objective 7: Accelerate Data Science and Advanced Analytics Projects: Promote and support the use of advanced analytics in addressing state challenges. ODI’s data services enable and scale this objective, providing substantial value to agencies by driving transformation, culture change, and building capacity.

Objective 8: Responsible AI Standards and Enablement: ODI will establish a comprehensive AI enablement framework designed to build agency capacity. The goal is to empower agencies  to solve critical business problems efficiently, prioritizing the technology ecosystem the state already possesses or is actively moving toward.

Objective 9: Grow and Upskill California’s Data Workforce: Collaborate with the Department of Human Resources (CalHR) to link training, career development, and classification planning into a cohesive data workforce strategy and expand data course offerings to multiple levels of general staff, managers, and executives.

Objective 10: Data Communities of Practice: Establish Data Communities of Practice (CoPs) centered around key data topics to break down silos and encourage peer-to-peer learning. These communities will meet regularly to share case studies, solve common challenges, and develop best practices.

Vision

The California state government is undergoing a statewide transformative shift towards innovation and agility in delivering public services, and data is crucial in driving this culture change. This culture shift in government is happening at a critical time, as artificial intelligence (AI) and digital transformation initiatives unlock new opportunities to improve government services.

Public benefits are foundational to our society’s social safety net, providing essential support to the most vulnerable Californians. This includes food assistance, health insurance, as well as rallying aid to those impacted by natural disasters. Additionally, public benefits play a crucial role in educating the next generation of Californians to be prepared for the demands of the future job market.

Californians need their government to keep pace with the advance of these technologies, delivering fast, accessible, and effective public services that meet their needs. Data is more important than ever in unlocking the benefits, enabling true innovation and creativity in public programs, and reducing the risks of these emerging technologies.

Since the 2024 data strategy report, California has made significant progress in maturing its data ecosystem. However, agencies continue to encounter challenges in addressing data quality issues, developing cross-agency longitudinal data systems, and establishing strategic alignment and support for data projects across all levels of leadership and expertise.

For the 2026 California Data Strategy, the vision is for California to establish a federated, trusted data highway. This highway will serve as a connected network linking the state’s diverse data silos under unified standards, modern infrastructure, and human-centered governance.

The data highway analogy represents a user-centered data ecosystem that will empower agencies to move data safely and purposefully, providing effective services, equitable outcomes, and data-informed decisions that enhance Californians’ lives.

This vision of an interconnected data highway for California state government opens up new possibilities for streamlining the digital experience for Californians to easily access state programs through “digital front doors” without navigating complex back-end bureaucracy siloes. This vision enables rapid, results-driven experimentation with AI and other digital tools to improve the cost effectiveness and performance of government services upon a solid foundation of high quality data interoperable across state agencies. This highway safeguards state data through a network of local data stewards that can leverage secure privacy-by-design data architectures to promote digital identity tools without compromising the security and privacy of Californians.

Public servants must demonstrate creativity, courage, and care to meet this critical moment and responsibly bring California state government into the AI era. This is already happening. State leaders are actively promoting data-driven approaches and building capacity, skills, and mindset to embrace a more innovative, efficient, and engaged government. The ultimate goal is to establish a streamlined, coordinated California state government that continuously learns and improves, using data as a strategic asset in serving its 40 million residents.

2026 Strategic Goals

The State Chief Data Officer and their office is established within the Office of Data and Innovation (ODI), with the responsibility to update the State Data Strategy report every two years and deliver on its goals to advance the state’s data ecosystem. California’s 2026 Data Strategy is built upon the foundational work described in ODI’s two prior data strategy reports and reflects the realities of today’s data and AI environment.

This report also reflects an evolution of the original analogy of “data roads, rules, and travelers.” The original metaphor illustrated the essentials of good data practice to agencies: infrastructure, governance, and people.

As California navigates a new era of interconnected systems, generative AI (GenAI), and federated governance structures, the state’s needs are evolving into a “data highway.”

The data highway serves as an interconnected network that connects the many roads built by state agencies and local partners into a cohesive, governed ecosystem. This ecosystem is guided by shared standards and is maintained through common utilities.

This highway embodies modern data mesh principles. Each agency remains the steward of its own data (“local roads”), while statewide standards, agreements, and shared services serve as the entry points and safeguards that allow the safe and efficient transfer of information.

Goal 1: Streamline Data Access and Infrastructure: Connecting the Roads

California continues improving its data roads, including platforms, Application Programming Interfaces (APIs), and pipelines that make information accessible when and where it is needed.

In 2026, these roads come together through a data highway with shared data utilities such as identity and consent services, metadata registries, data quality tools, and AI evaluation frameworks. These utilities will enable departments to work effectively in partnership across a federated, interoperable network. This approach ensures that local innovation can thrive while statewide infrastructure and standards safeguard secure and efficient data flow.

Goal 2: Strengthen Governance — Establish the Rules and Traffic Control

Clear and well-defined governance structures and rules are essential for the data highway’s success. California will refine its statewide data and AI governance to establish traffic control systems that ensure the safe, efficient, and equitable operation of the data highway.

These policies and agreements will establish the statewide “highway code,” including data standards, privacy protections, quality expectations, and AI-specific guidelines, such as model evaluation, provenance, and audit logging. These governance controls foster innovation and promote accountability, enabling trusted collaboration across a diverse government ecosystem.

Goal 3: Empower Data Users — Support the Travelers

The state will continue investing in data literacy, AI enablement, leadership engagement, and communities of practice to strengthen the workforce responsible for operating and maintaining these systems.

Californians remain the primary users of the data highway and need clear destinations. The 2026 Data Strategy focuses on helping every agency to define, measure, and achieve meaningful outcomes, including improved service delivery, operational efficiency, equitable impact, and policy insight.

California will expand training and guidance to help programs use data to frame measurable goals, track results, and communicate success in ways that resonate with residents. At the same time, digital assistants, AI tools that assist with research, analysis, and service design, will support these travelers in navigating their journeys. The strategy emphasizes ethical use, human oversight, and equitable access, ensuring that every department can progress safely toward its desired outcomes.

A Federated, Future-Ready Data Highway for California

Together, the roads, rules, and travelers are now part of a larger data highway. They are a statewide network that connects data domains while respecting local autonomy. This reflects how California’s data ecosystem has matured:

• From isolated roads to connected networks;
• From a lack of standards to clear governance for data and AI;
• From individual travelers to human-governed AI partnerships driving measurable outcomes.

The state’s vision for 2026–2028 – which is a federated, trustworthy, and intelligent data ecosystem that enables information to move safely and efficiently – empowers people to responsibly use technology, and upholds California’s commitments to equity, privacy, and public trust.

What We’ve Heard

This section outlines the current landscape and the rationale behind the selected priorities, based on stakeholder feedback and real-world lessons.

What We’ve Heard Across the Data Ecosystem: Over the past two years, ODI and agency partners have engaged hundreds of stakeholders through its initiatives in data accelerator services, standards development, and innovation training programs. ODI has listened and learned from practitioners and leaders at all levels regarding data requirements and challenges.

Common themes emerged:

Data Silos and Access Friction

The state is making significant process connecting critical statewide datasets. Initiatives like the Cradle-to-Career (C2C) data system are successfully integrating billions of data points across education, social services, and workforce entities, providing an unprecedented holistic view of student pathways. Additionally, the Homeless Data Integration System (HDIS) has created the state’s first centralized repository of data on homelessness from 44 regions. Despite improvements, agencies continue to report challenges in data discovery and accessibility across departments. Staff may only see a fragment of the puzzle, unable to effectively combine data to get the full picture of a given problem. Breaking down these data silos through better infrastructure and interagency agreements remains a top priority. As state data systems evolve their analytics capabilities and achieve better integration, new data opportunities will generate sophisticated data insights to improve programs and strengthen public transparency initiatives through the state’s open data program.

Legacy Systems and Outdated Tools

Agencies are actively moving away from older, rigid systems by adopting modern cloud-based solutions. For example, ODI partnered with CalHR to increase analysis capabilities for the Examination & Certification Online System (ECOS) creating a scalable, cloud-based data pipeline for self-service workforce analytics. However, many agencies are constrained by aging Information Technology (IT) systems that are not built for modern data analysis or secure data sharing. Stakeholders have voiced the need for modern cloud-based platforms, data warehouses, and API-driven systems that can manage increasing data volumes and integrate across programs. Additionally, teams lack user-friendly analytics tools and some analysts still export data to spreadsheets due to a lack of better options. Upgrading these capabilities is crucial to improving efficiency.

Complexity of Data Sharing and Privacy Compliance

Agencies continue to collaborate on data sharing projects, facilitated by the Interagency Data Exchange Agreement framework, established by ODI in 2021. While agencies actively use this framework to facilitate smoother inter-agency data sharing practices across the state, effective implementation at scale requires streamlined guidance and wider coverage of departments across the executive branch. Departments want clarity on whether data can be shared with another department and, if so, the appropriate procedures for doing so, without months of legal negotiations. Additionally, it is crucial that privacy and security not be afterthoughts. Any sharing solution must incorporate confidentiality, data minimization, and security measures up front, rather than relying on ad-hoc safeguards later.

Data Workforce Gaps

The state has made tangible investments in data leadership and training, including establishing Chief Data Officer (CDO) roles in major departments to guide strategy, and launching CalAcademy in January 2024 as a centralized program to enhance data literacy and analytics skills for all state employees. Despite these advancements, agency leaders and Chief Information Officers (CIOs) consistently highlight data skill gaps as a barrier, encompassing not just technical data science skills but also core data literacy among staff and managers. Programs sometimes find it challenging to interpret the data they have, let alone to deploy advanced analytics. Recruitment is another workforce issue. Hiring and retaining qualified data professionals in government is difficult due to misaligned job classifications for data roles and intense competition with the private sector. Many departments are actively seeking support in upskilling existing staff and modernizing hiring practices to attract and bring in new talent.

Data Thought Leadership and Standards

Departments are aligning on shared data standards, such as demographic data collection and reporting practices across the state, notably through working groups like the California Health and Human Services Agency (CalHHS) data standards community. However, there is a growing demand for data thought leadership that can foster consistency in data standards, advocate for necessary data policy, and encourage executive support through clear mandates from state leadership to prioritize data initiatives. This call for policy and standards accompanies a need for practical resources like playbooks, templates, and one-on-one expertise. Essentially, “tell us why something is important, and show us how to do it.” ODI’s critical role as the state’s experts in setting data standards promotes consistent best practices and resources and helps prevent departments from reinventing the wheel. ODI also provides expert services, ranging from helping departments pilot AI services to advising on platform modernization and enterprise data architecture initiatives.

Community of Practices

Communities of practice are active within and across agencies, especially in high-impact areas like open data, Geographic Information Systems (GIS), and AI. There are many data success stories across the state. However, these lessons often remain buried within departmental siloes. Codifying these achievements into reusable playbooks, implementing train-the-trainer models, and fostering communities of practice accelerates and replicates impact, compounding benefits across programs and regions. In summary, this tactical approach involves listening to the needs of state agencies, learning from what is successfully working, and investing in critical enablers such as technology, policy, and people to yield the greatest improvements in data capability. The following sections detail the strategic objectives and initiatives developed in response to these priorities, each with clear owners, milestones, and metrics to ensure accountability from planning to execution.

2026 Strategic Objectives

To translate ODI’s vision into action, California will focus on tactical goals that build capacity through skills, tools, and processes and that scale efficiencies through shared best practices and solutions. The Strategy’s goals are implemented through strategic objectives for 2026–2028. Each objective corresponds to a major focus area often bridging the “roads, rules, travelers” categories and encompassing specific initiatives.

Goal 1: Streamline Data Access and Infrastructure

Provide California with the modern “data roads” needed to move and use data efficiently across the enterprise. This objective focuses on upgrading technology platforms and establishing the technical standards that facilitate data integration, accessibility, and quality at scale.

Why it matters: A modern, well-governed data infrastructure is the foundation for all other data efforts. Without reliable “data roads,” agencies will remain limited in their ability to share and analyze data effectively, regardless of their willingness or skill level. By investing in platforms, standards, and quality, California is paving the roads that every program can use, helping each agency avoid having to build its own technology platform.

Objective 1: Modernize Enterprise Data Platforms

Accelerate the adoption of modern cloud-based data platforms, such as cloud data warehouses and analytics platforms across state agencies. By transitioning from siloed legacy databases to scalable platforms, departments can easily access and share data as well as perform advanced analytics.

• Scaling the Modern Data Stack Accelerator program within ODI to identify high-impact use cases in collaboration with agencies.
• Promoting modern data architecture systems across state government, aligned with the following principles:
• Modular – reusable components that are interoperable with other data tools.
• Scalable – grows easily with data and users.
• Collaborative – shared infrastructure promotes secure, transparent, and repeatable data work.
• Governed – Built-in monitoring, quality checks, and role-based security.
• Providing expertise and platforms allows ODI to partner on projects and provide data and analytics engineering expertise or secure computing environments for sensitive data analyses.

Milestone: Each year, ODI will provide training and deployment support to five engagements with departments to help them transition to a modern data stack infrastructure.

Progress Tracking Metrics:

• Number of departments adopting modern data stack architectures.
• Reduction in time to deploy new data solutions.
• Staff time saved or cost savings to business for Return on Investment (ROI).

Objective 2: Champion Improvements to Statewide Open Data

Collaborate with CDT to update California’s open data program and improve the discoverability, timeliness, and usefulness of public datasets. CDT, as the product owner of the statewide open data platform, is evolving the open data portal and delivering on a roadmap to continuously improve open data. ODI will continue to collaborate with and champion CDT’s work on open data. Policy updates and development of standards will be updated in accordance with ODI’s authority established in legislation and in partnership with CDT and key stakeholders.

Milestone: Update data.ca.gov by 2028 with new features.

Progress Tracking Metrics:

• Portal usage: Increase in dataset downloads, page views, and use cases.
• Dataset currency: Key datasets updated on schedule.

Objective 3: Leverage State Processes to Accelerate Adoption of Data Tools

ODI will collaborate with CDT and DGS to enhance pathways to streamline and accelerate data tool adoption. This includes:

• Leveraging enterprise-wide procurement vehicles, such as the software licensing program (SLP) to facilitate the procurement of modern tools for departments.
• Adopting reference architectures for the modern data stack, enabling departments to easily adopt proven patterns.
• Establish a streamlined approval process for modern data tools, consistent with CDT’s governance frameworks.

To avoid duplication, ODI and CDT will collaborate to provide enterprise licenses or shared services for popular analytics software, such as data visualization tools like Tableau/PowerBI, geospatial analysis tools, or statistical packages.

Milestone: By 2027, negotiate SLP agreements for key analytics tools. Add a modern data stack reference architecture to the California Enterprise Architecture Framework.

Progress Tracking Metrics:

• Number of agencies using centrally procured analytics tools and provide statistics of those tools, including reports, and dashboards created.

Goal 2: Improve Data Management and Governance

Establish a comprehensive “rules of the road” framework for data management that ensures data is handled ethically, consistently, and securely across state government. This objective encompasses the policies, standards, and governance bodies that will guide data use, including privacy protection, security, data sharing agreements, and ensuring equity in data practices.

Why it matters: Data governance and policy are the foundation for sustainability and trustworthiness in enterprise systems. Without clear rules, agencies may either not share data out of fear of doing something wrong or losing control of their data. Neither scenario is ideal. Robust governance frameworks ensure consistent, lawful data use, fostering trust among agencies and the public.

Furthermore, equitable data practices help mitigate biases to vulnerable communities and reduce disparities. The result will be a trusted state data environment that fosters innovation and flourishes because all participants understand the rules and have confidence in the system.

Objective 4: Enhance Data Interoperability and Standards

Establish data standards and integration tools to ensure systems can talk to each other. Initial objectives include standard definitions for demographic data, including race and ethnicity for equity measures, geospatial data, and program performance metrics.

Priority standards and guidelines include:

• Demographic Data Standards: Collaborate with state data and program leaders to implement standardized practices for collecting, reporting, and using demographic data. This may involve adopting uniform race and ethnicity categories across all departments, as required by recent legislation such as AB 91 and AB 1726. Ensure datasets used for decision-making include relevant demographic breakdowns to analyze equity impacts.
• Data Inventory Guidelines: Establish standard procedures for agencies to maintain an updated inventory of their critical datasets and document key metadata.
• Data Quality and Lifecycle Management Standards: Provide guidelines on data documentation, retention, archival, and disposal in accordance with records management regulations, ensuring responsible management of data assets from creation to retirement.

Milestone: By 2027, release a toolkit to operationalize ODI’s data standards. By 2028, all agencies shall have an action plan to implement the demographic data standards in their data collection systems.

Progress Tracking Metrics:

• Number of agencies adopting the standards.
• Number of successful data exchange use cases across systems.

Objective 5: Scale the Interagency Data Exchange Agreement

The Interagency Data Exchange Agreement (IDEA)provides a legal framework for data sharing among signatory state entities. By 2024, many departments signed on, but department usage can improve with additional support. ODI’s strategy is to:

• Develop a comprehensive governance and support model to manage the agreement, track data sharing exchanges under the agreement, and address amendments or issues.
• Monitor key metrics of the agreement’s effectiveness, as committed by ODI in 2024. Metrics include:
  • The number of data sharing engagements executed.
  • The average time from request to data provision.
  • The variety of program areas utilizing it.
• Expand or adjust IDEA’s provisions as needed to address new scenarios. This includes discovery work to enable other kinds of data sharing capabilities for research purposes.
• Promote success stories to document and publicize cases where IDEA has enabled important data collaborations to build confidence and encourage increased usage. This important task requires support from communications and external and public affairs leads within agencies and departments.

Milestone: By 2027, double the number of annual data sharing initiatives executed under IDEA from the 2024 baseline of 15 executed agreements.

• Reduce the average turnaround for approving a standard data sharing request to less than 90 days, assuming legal prerequisites are met.
• Ensure 100% of Cabinet-level agencies and departments are signatories to the agreement by 2028.

Progress Tracking Metrics:

• Number of data exchange agreements.
• Number of IDEA signatories covered by the framework.
• Average turnaround time to develop a data exchange agreement.
• Customer satisfaction from departments using the agreement.

Objective 6: Privacy-First Digital Identity, Access, and Data Use

As California connects more systems and increases data sharing, ODI must ensure that identity, access, and privacy protections evolve at the same time. ODI will enhance identity and access management while embedding privacy-by-design, data minimization, and secure analytics environments across state programs. ODI will collaborate with CDT to enable state departments to adopt consistent, statewide standards for digital identity that facilitate secure, role-based access to data and reduce duplication of effort. These efforts will also help ensure compliance with applicable privacy laws.

• Digital identity and access federation: Collaborate with CDT to enable departments to use a shared digital identity approach, simplifying access to services, identity verification, and data sharing across programs.
• Streamlined Privacy Impact Assessments (PIAs): Enable department use of PIAs for new projects or systems involving personal or sensitive data so departments can easily identify risks and embed mitigations early in design.
• Data minimization and retention: Encourage agencies to collect only the data needed to accomplish specific tasks, reduce unnecessary collection of personally identifiable information, and follow records retention schedules and privacy regulations.
• Secure data enclaves for high-risk data: Promote secure data enclaves or sandboxes for analytics involving sensitive data. These secure data environments allow analysts to operate within tightly controlled environments with strong access controls and logging capabilities.

Milestones:

• By 2028, implement a pilot of cross-department data access credentials aligned with CDT’s digital identity standards for at least one major data-sharing domain.
• Release data minimization and privacy guidelines and toolkit, and facilitate departments in conducting PIAs for new projects or systems involving sensitive data.

Progress Tracking Metrics:

• Reduction in the number of separate credentials required for interagency data workflows.
• Number of PIAs completed for new or significantly modified systems handling sensitive data.

Goal 3: Spur Data use and Ability

This goal enables and scales “analytics and AI” at the state, providing data services, tools, frameworks, and governance for responsible data use in process improvement, enhanced services, and efficiencies within state departments. This approach promotes traditional business intelligence, cutting-edge data science, and AI adoption while preserving data privacy and security protections.

Why it matters: Data analytics and AI are force multipliers for government. They can reveal insights that may be overlooked by humans and can significantly increase efficiency through automating routine tasks and optimizing resource allocation.

However, without a concerted effort, most agencies may encounter challenges in adopting these technologies due to skill and risk barriers. That is why a statewide initiative is needed to foster innovation while establishing robust safeguards. Executed effectively, this will improve services by making them more personalized, proactive, and effective, and cost savings through efficiencies and fraud and error reduction, as well as informed policymaking by using predictive insights and evaluations.

Most importantly, by training staff to apply strong ethical guidelines and practice human oversight over algorithms, California will establish a responsible path for public sector AI, maximizing the benefits while mitigating risks such as bias or misuse.

Objective 7: Accelerate Data Science and Advanced Analytics Projects

Promote and support the use of advanced analytics, including predictive modeling, machine learning, simulation, and similar tools, in addressing state challenges. This objective is enabled and scaled through ODI’s data services, which provide substantial value to departments by driving transformation, culture change, and building capacity. This involves:

• Scaling ODI’s  Data Science Accelerator to identify high-impact use cases for data science in collaboration with agencies. For example, using predictive analytics to identify fraud, machine learning to allocate inspection resources, and natural language processing to improve government services.
• Providing technical and data expertise through ODI partnerships with departments on projects to:
• Provide data scientist expertise on how to define, scope, and build robust data science solutions designed for specific business requirements.
• Engage with academia, technology practitioners, and community organizations to connect state departments with new best practices, translate data science research into operations, or surface user insights. This could involve convening roundtables, writing white papers, or meeting with stakeholders to highlight high-impact opportunities for improving state programs.
• Evolving the Analytics Accelerator as a resource designed to help departments transition from manual, high-effort reporting to streamlined automated reporting. This holistic service, which includes structured training, scoping sessions, and project-based learning, will expand its training options to include:
• Cohort and train-the-trainer models: Structured, team-based learning for small, medium, and large groups.
• Self-service training: Allows for flexible access and self-paced learning, maximizing scalability across the state workforce.
• Encouraging an “experiment and evaluate” mindset. ODI will promote approaches like A/B testing and pilot evaluations in program delivery, so agencies use data to learn what works before scaling policies.

Milestone: Each year, initiate at least five new advanced analytics pilot projects in collaboration with agencies.

Progress Tracking Metrics:

• List the number of pilot projects completed and estimated benefits achieved in terms of dollars saved, efficiencies gained, and outcomes improved.

Objective 8: Responsible AI Standards and Enablement

In response to the rapid advancement of AI, including GenAI, the state will continue to proactively establish safeguards in testing and leveraging these technologies for public good. ODI’s approach to AI is twofold: foster innovation while ensuring accountability.

ODI will establish a comprehensive AI enablement framework designed to build departmental capacity. This builds on the importance of high quality data, rigorous outcomes evaluation, and iterative product improvement in successfully testing and adopting AI in state programs. The goal is to empower departments to solve critical business problems efficiently, prioritizing the technology ecosystem the state already possesses or is actively moving toward.

Strategic Enablement: Using a product approach

ODI will provide comprehensive resources and hands-on advisory services to help departments navigate the complexity of AI adoption. This support structure focuses on four key pillars:

• Selecting the right tool: Not every problem requires GenAI. ODI will provide decision frameworks to help teams determine when to use GenAI versus traditional analytics, standard machine learning, or simple automation.
• Maximizing infrastructure: ODI will educate and train departments on unlocking AI and ML features within their current licensed platforms (e.g., Microsoft Azure, AWS, Snowflake). This ‘existing-tools-first’ approach minimizes costs and accelerates deployment by removing procurement hurdles.
• Defining and measuring success: To prevent unfocused experimentation that does not deliver sustainable value, ODI will publish guidelines on developing project success metrics to help department teams:
  • Define success by connecting the specific project to a broader, critical mission goal.
  • Identify concrete, measurable metrics that can be consistently tracked to demonstrate the impact (e.g., time saved, dollars conserved, improved service outcomes).
  • Assess readiness for tracking these metrics.
• Iterative product delivery: ODI will embed a product-thinking mindset into state projects and support departments in developing initial proof of concepts (POCs) and minimum viable products (MVPs) using the success metrics defined above to validate value before scaling iteratively.

Responsible AI: Enabling readiness and safe adoption

To enable safe adoption, ODI will develop a toolkit focused on AI readiness, ethical considerations, and operational due diligence. This resource will be designed to help departments identify and mitigate risks early in the process and will help address core questions from practitioners, such as: Have we considered impacts on different groups? Is there potential bias in how this data was collected? Is there transparency in how the data is used?

• Bias and Impact Assessments: Evaluate models for potential disparate impacts across demographic groups and document findings. If a model is used to inform decisions about people, such as benefits eligibility or hiring, departments should conduct an algorithmic impact assessment.
• Data Assessments: Evaluate the data used by an AI tool to ensure effectiveness and fairness. This may include evaluating data sources for accuracy and completeness and testing model performance.
  • Transparency Measures: Wherever feasible, use AI models that are explainable and inform the public when AI is being used (e.g., a notice on a website if a chatbot is AI-driven).
  • Privacy and Security: Ensure AI deployments comply with privacy laws. Do not expose sensitive data to public AI services without appropriate safeguards. Also, protect against cybersecurity risks, such as an AI system being manipulated via prompt injection.
  • Human-in-the-Loop: AI should augment, not replace, human judgment. ODI will set guidelines requiring human review of AI outputs, especially in high-stakes use cases.

Milestones:

• By 2027, establish an ‘AI Accelerator’ service to support department pilots.
• By 2027, enable at least five GenAI pilot projects and document the results that show efficiency improvement and project outcomes and learnings.
• By 2028, publish an AI toolkit focused on enabling readiness and safe adoption.

Progress Tracking Metrics:

• Number of AI use cases piloted.
• Number of AI projects that undergo bias and impact assessment.

Objective 9: Grow and Upskill California’s Data Workforce

The launch of CalAcademy in January 2024 provided a centralized program to train state employees in data literacy and analytics. Simultaneously, California must evolve its data-related roles and career paths to attract, grow, and retain top talent. By 2028, ODI will link training, career development, and classification planning into a cohesive data workforce strategy.

The strategy includes:

• Scaling CalAcademy training across competency levels. Expand data course offerings to multiple levels of general staff, managers, and executives. This includes advanced courses on modern + cloud-based tools, data science, visualization, AI, and related topics, to ensure all participants from front-line staff to executives have relevant learning paths.
• Embedding data skills into standard professional development. Partner with CalHR and departmental training units to integrate CalAcademy data literacy courses into existing professional development and leadership programs, such as incorporating data-driven decision-making modules for new supervisors and managers.
• Aligning training with data career pathways. Define and communicate data career ladders, including data analyst to data scientist to data leader and map CalAcademy offerings to each step, enabling staff to identify clear pathways for advancement and the skills required at each level.
• Partnering with CalHR on future data workforce needs. Collaborate with CalHR to conduct research and discovery on modern data, AI, and privacy roles, such as data scientist, data engineer, AI/ML specialist, Chief Data Officer and Chief Privacy Officer. This effort will build on CalHR’s existing workforce development frameworks on data and GenAI skillsets to understand the level of effort, feasibility, and strategy needed to eventually implement updated or new job classifications.

Milestones:

• Train at least 300 employees via CalAcademy in 2026, scaling to a cumulative 3,000 by 2028.
• Launch at least three new advanced CalAcademy courses by 2027.
• By the end of 2027, complete a CalHR partnership research and discovery phase on data-related classifications and deliver a recommended roadmap for potential future implementation and piloting of updated data career pathways.

Progress Tracking Metrics:

• Number of course enrollments and completions across competency levels.
• Post-training evaluation scores and participant feedback, to ensure training quality and relevance.
• Number of departments incorporating CalAcademy into formal professional development or leadership programs.
• Participation in identified data career pathways, including staff progressing from foundational to advanced courses.
• Completion of CalHR research and discovery deliverables, including findings, options analysis, and proposed roadmap to inform future modernization of data job classifications.

Objective 10: Data Communities of Practice

Building on an ODI pilot developed in 2024, ODI will establish Data Communities of Practice (CoPs) centered around key data topics to break down silos and encourage peer-to-peer learning. These communities will meet regularly, virtually and in-person, to share case studies, solve common challenges, and develop best practices.

The Data CoP will serve as a group for data-focused professionals in California state government and have two primary objectives:

1. Informing Strategy: Identifying pain points, priorities, and recommendations from state data teams across sub-specialties, including data science, data governance, and data services, in order to inform state policies and strategies.
2. Scaling and Dissemination: Disseminating and sharing ODI’s own data guidelines and products, as well as uplifting and scaling successful data products and best practices across agencies more broadly.

The structure and engagement of the Data CoP will be organized around quarterly community-wide meetings and include specialized data tracks led by practitioners to address common challenges and develop practical resources.

ODI will coordinate and provide platforms, such as an online forum or knowledge base, but the communities’ strategic direction will be led by practitioners from various departments, fostering a grassroots network. Asynchronous engagement, resource sharing, and questions and answers will be supported via an online community platform, ensuring continual connection and a comprehensive resource repository for all members.

Milestone: Establish charters and leadership for a Data CoP by early 2026; host quarterly meetings; publish an annual summary of insights and recommendations.

Progress Tracking Metrics:

• Participation rates will include the number of agencies and individuals involved.
• Outcomes including collaborative projects initiated or solutions, such as playbooks, white papers or frequently asked questions, produced by these communities.

Summary of Changes from 2024 Data Strategy

Conclusion

California’s 2026 Data Strategy outlines an ambitious but achievable roadmap to transform data governance, management, and use over the next two years. By focusing on infrastructure, process, and people, ODI aims to turn the promise of data into tangible improvements in government operations and resident outcomes.

This strategy is a call to action for state executives, program leaders, and IT data professionals – success requires collaboration and commitment at all levels. ODI, in close collaboration with state agencies and other partners, can serve as a center of excellence for these efforts, but broad engagement and culture change are crucial.

The benefits of executing this strategy are significant. Californians will experience better services that are more timely, targeted, and effective as agencies use data to tailor interventions and improve processes. Modern tools will enhance state employees’ work, streamline data sharing, and automate report generation from dashboards.

Additionally, by enabling strong privacy and ethical safeguards, the state will enhance public trust in government innovation with data while upholding core values.

By embracing this strategy and collaborating to implement it, California will continue to lead as an innovative, data-informed state. The journey from 2020’s Strategy to now has established a strong foundation; 2026–2028 will be the period where ODI truly capitalizes on scaling that foundation to deliver tangible results for Californians.

ODI looks forward to partnering with every department and stakeholders to bring this strategy to life. The road ahead is clear, now is the time to travel on this journey together, using data as our guide and safeguard to an enhanced California.

Appendices

A. Documents reviewed

A non-exhaustive list of documents reviewed to inform the Strategy.

• Blueprint for Delivering Results in State Government. Results for America. Available from https://blueprint.results4america.org/.
• California Department of Technology Envision 2026 Technology Strategy Plan. Available from https://www.envision2026.cdt.ca.gov/.
• California Health and Human Services Agency IT and Data Strategic Plan. Available from https: //www.osi.ca.gov/CalHHS%20IT%20and%20Data%20Strat%20Plan-March-2024-Web508-v4.pdf.
• Connecticut State Data Plan: 2025-2026. Available from https://portal.ct.gov/datapolicy/state-data-plan.
• Data Quality – Vital to Optimizing GenAI: A survey of State Chief Information Officers and Chief Data Officers – NASCIO. Available from https://www.nascio.org/resource-center/resources/data-quality-vital-to-optimizing-genai-a-survey-of-state-chief-information-officers-and-chief-data-officers/.
• Federal Data Strategy. Available from https://strategy.data.gov/.
• New Jersey IT Business & Technology Strategic Plan 2025-2026. Available from https://www.nj.gov/it/docs/StrategicPlan_25-26.pdf.
• State Chief Data Officer Survey – Beeck Center. Available from https://beeckcenter.georgetown.edu/report/state-chief-data-officer-survey/
• The Evolving Role of the State Chief Data Officer: A Framework for Today. Beeck Center. Available from https://beeckcenter.georgetown.edu/report/the-evolving-role-of-the-state-chief
  -data-officer-a-framework-for-today/.
• Washington State Enterprise Data Strategy 2025-2027. Available from https://watech.wa.gov/strategy/enterprise-data-strategy.