Report Using Data Management to Support Long-Term Fiscal Sustainability

Introduction

In the age of artificial intelligence and machine learning, data is abundant. The availability of data can help state governments make informed decisions on how services are delivered and where to invest funds. The desire to use data is growing. Legislators in many states are using evidence-based, results-driven methods and tools like those created by NCSL and The Pew Charitable Trusts to inform policy. Data and analysis are also critical components of the budget process. With accessible, transparent and reliable data, state leaders are better equipped to understand fiscal risks and outlooks. If welded effectively, data can be a strategic asset to state budgeting practices and long-term fiscal sustainability.

Data sharing between the legislature, executive and local governments is often driven by statute reporting time frames during the annual or biennial budget cycle. What is presented to state leaders during the budget process has significant implications for funding objectives and outcomes. However, data systems detailing revenue and expenditures related to services like education, infrastructure, property taxes and health care, are usually managed and maintained separately. Siloed data systems make tracking and assessing budgetary objectives difficult for policymakers, especially as the volume of data collecting and storage increases.

The influence of data in policymaking also comes with the responsibility of managing it. The next evolution of data-driven budgeting is data management, such as data governance and integrated data systems. Once considered a function of information technology offices, data governance has evolved into a strategic and collaborative management tool across the public sector. Data governance formalizes the use of integrated data through shared standards and data policies.

Creating an integrated data system from multiple sources of state data is an opportunity for policymakers to better understand the state's overall fiscal health. Connections between datasets enable better depth and breadth of analysis of ongoing and future projected deficits, revenue shortfalls and structural imbalances. Further, an agreed-upon integrated data system levels the playing field and eliminates questions of integrity. Integrated datasets provide valuable information while honoring the differing perspectives of policymakers. The goal is to remove any unreasonable assumptions that can be settled through credible data and improve dialogue among legislators, agencies and other stakeholders.

The Budget Working Group recommends that policymakers consider the following information as they develop data management systems for long-term fiscal sustainability practices.

Data Governance and Integrated Data Systems

In the public sector, integrated data systems and data governance are gaining traction. According to the Government Accountability Office, data governance is a framework of organizational structures and supports intended to help agencies improve the quality and availability of data. The system connects components of the full integrated data system to align objectives, define roles, policies and resources. It also ensures data is properly handled and protected. Public sector data governance gravitates to a federated data system model. In a federated data system, individual sources agree to share their data, while maintaining control and privacy, with an established integrated data system. Data governance anchors organizational data management processes and establishes a formal body to make decisions about integrated data systems.

Integrated data systems store data, linking records from multiple public agencies to give a broader view of performance. They can capture large universal datasets, not just a pulled sample. Systems are usually overseen by a specific entity- the legislature, an agency or an external partner, such as a university. Access to shareable data through integrated data systems makes it easier for stakeholders to analyze, coordinate and allocate resources.

Many states already use statewide longitudinal data systems for social services, like education. The Education Commission of the States cites at least 40 states that connect data between education and workforce systems. Thirty states have formal privacy policies or guidelines for state longitudinal data systems. For example, Rhode Island's Longitudinal Data System (RILDS), a federated model, connects early childhood, K-12, higher education and workforce data. It serves as the "state's repository for linked, interagency, longitudinal data." RILDS has three governance bodies that coordinate data between five agencies.

While public sector data governance and integrated data systems are supporting service-specific programmatic evaluation and cost-benefit analysis, their use is less prevalent in statewide fiscal analysis.

Leveraging Data Management for Fiscal Sustainability

Even in non-crisis times, states manage short-term fiscal shifts to maintain a balanced budget. Lately, revenues, inflationary, and employment indicators are less predictable, leaving states vulnerable to future downturns or deficits. Accurate revenue and expenditure data helps lawmakers decipher the performance of their policies as it relates to the macroeconomic climate.

State lawmakers are looking for better ways to use data to achieve policy priorities and prepare for emerging risks. Data management is a proactive approach to expediting and easing the burden of the budgetary cycle. For example, integrating school enrollment, expenditure and revenue data allows legislators to assess performance trends continually, rather than once annually during agency budget requests and hearings. And, when legislative questions arise, comparisons and analyses can be quickly aggregated instead of waiting to retrieve information. Overall, this type of automation can help lawmakers avoid short-term pitfalls when budget demands are high. Budget cuts or hasty spending decisions that might be detrimental to service delivery and sustainability can be better anticipated.

Under the direction of the Modernization and Risk Analysis (MARA) Committee, Montana is building an integrated data system and model to project future costs and revenues. MARA's 2040 model is structured around data modules that reflect significant revenue and cost drivers, like property taxes, infrastructure and education. Each module is built around an existing dataset. MARA works with the dataset owner to integrate the data into the MARA Data Project. Examples include collecting state accounting data through the State Financial Services Divisions, school accounting data from trustee reports, or local government accounting data from financial reports to the Office of Public Instruction. Non-partisan staff of the Legislative Fiscal Division use a cloud-based business intelligence system to create interactive visualizations of the forecast modules. The tools are used to create analysis of emerging economic, demographic, and financial trends.

There is also potential to use data management systems to improve existing performance and outcome tools, like budget stress testing and long-term budget assessments. Much of the leg work to build these tools is underway. At least 30 states and the District of Columbia produced either long-term budget assessments or long-term projections between 2018-2023. Expanding the data available to create these tools can make it easier to adjust, add, or change outcomes from more useful and timely datasets. Like Montana, other legislative bodies are working to elevate mid- to long-term projections and find ways to maximize existing data. For example, New Mexico, a state that uses both budget stress testing and long-term budget assessment tools, is committed to formalizing and improving long-term forecasts. In 2022 and 2023, the state's Legislative Finance Committee produced a structural budget analysis over the next 25 years. Staff are also working with criminal justice partners to ensure data integration efforts become reliable data tools for lawmakers.

Considerations

The Budget Working Group encourages states to consider the following strategies when developing data management systems.

Finding a Legislative Champion

A legislative champion who is motivated to embrace the use of data management tools in state fiscal health decisions can advocate for the legislature's desired outcome. Data governance and integration require close collaboration and coordination between the legislature and intergovernmental stakeholders. Building trust among legislative colleagues, state partners and other stakeholders is critical to gain access to targeted data and resources.

Define Data Governance and Body

Establishing a data governance body should be a priority for any state creating an integrated data system. All partners must come to a clear consensus of the definition of data governance and how it will be structured. The body will identify the desired data assets, create rules, regulations and reporting structures needed to formally manage the system. Agency and stakeholder needs, and any data challenges should be transparent in the formalization process. A misalignment in expectations can reduce trust and effectiveness. Stakeholders have their own culture related to data infrastructure, maturity, and capacity. A strong data governance framework can help states confront any organizational challenges with integrated datasets.

A legislative committee, working group or task force can assess if sufficient resources and buy-in are available to support an integrated data infrastructure.

Establishing Roles

Data management requires clear roles. There may be a data administrator to implement priorities set by a statewide board or a data steward responsible for oversight. However, the most important role to designate is the data owner. The data owner is responsible for managing the various datasets and integrated structure. In a federated data governance model, the owner assigns responsibilities across partner organizations and is accountable for the overall data strategy and governance policies. Responsibilities should be formally directed in statute, policy or a memorandum of understanding.

Data Quality, Access, and Security

Quality: All data should be accurate, complete, and unbiased. High-quality data leads to a credible system and sets a foundation for better decision-making. Incomplete data can cause gaps in information leading to unintended budget consequences. Additionally, maintaining data quality can reduce redundancies.

Access: The governing body should outline who has access to the data. The model of data sharing largely impacts access, whether the system owner collects all data or access remains with the initial source. Lastly, the governance body should define the data users. For example, identifying if the integrated data system will be open to the public, or if state partners retain access.

Security: Data partners may be concerned or uncertain about how data sharing impacts privacy laws. It is important the governing body establishes security policies and ensures partners are using methods to protect data, privacy laws and regulations.

Identifying Resources and Costs

Once a data governance body is established, the next step is implementing the data system. State leaders need to consider any costs, staffing resources and time needed to create and actualize the system. Pulling from existing datasets is a cost-saving measure, however there may other be startup administrative costs. The body may need to invest in a cloud-based visualization platform to house and display data or ramp up staffing capacity to perform data analysis and long-term projections. Further, creating a scalable system, one that can grow and accommodate other data sources as it matures, provides flexibility when implementing. In addition to standard legislative duties, building a data management system will require a time investment for all parties involved. A timeline provides a schedule of milestones and deadlines.

*Please note, this report does not provide a comprehensive step-by-step framework for creating data governance and integrated data systems. The federal government created a Data Governance Playbook that can serve as a useful guide. Systems will also be dependent on state cultures, resources, and existing structures.

Conclusion

The NCSL Budget Working Group found that the rapidly evolving nature of data modernization, changing budget constraints and growing interest in efforts to promote fiscal resiliency calls for the development of data management tools. Data management tools can systematically alter the way state budgets are built, maintained and assessed. As states build these tools, they can consider a variety of strategies to develop an integrated data system and effective data governance framework.

Acknowledgments

NCSL Fiscal Affairs program

• Emily Maher, project manager.
• Leo Garcia, policy specialist.
• Erica MacKellar, program principal.
• Mandy Rafool, director.
• Laurel Stiegler, senior program coordinator.

NCSL Budget Working Group Members

• Kirk Fulford, Legislative Services Agency, Ala.
• Sen. John Kavanagh, Ariz.
• Rep. Michael Carbone, Ariz.
• Sen. Chris Hansen, Colo.
• Eric Kurtz, Joint Budget Committee, Colo.
• Sen. Trey Paradee, Del.
• Rep. Matt Hatchett, Ga.
• Sen. Donovan Dela Cruz, Hawaii
• Nandana Kalupahana, House Committee on Finance, Hawaii
• Sen. C. Scott Grow, Idaho
• Sen. Carl Bjerke, Idaho
• Sen. Elgie Sims, Ill.
• Sen. Michael Nemes, Ky.
• Rep. Jason Petrie, Ky.
• Rep. Ann Matlack, Maine
• Sen. Joanne Comerford, Mass.
• Sen. Cindy Friedman, Mass.
• Sen. Guy Guzzone, Md.
• Sen. Tyler McCaughn, Miss.
• Rep. Angela Cockerham, Miss.
• Sen. Carl Glimm, Mont.
• Rep. Llew Jones, Mont.
• Rep. Jerry Schillinger, Mont.
• Amy Carlson, Legislative Fiscal Division, Mont.
• Sen. Christy Armendariz, Neb.
• Sen. Marilyn Dondero Loop, Nev.
• Rep. Daniele Monroe-Moreno, Nev.
• Rep. Donny Lambeth, N.C.
• Sen. Ralph Hise, N.C.
• Sen. Bill Tallman, N.M.
• Charles Sallee, Legislative Finance Committee, N.M.
• Rep. Patricia Lundstrom, N.M.
• Jack Giesea, Office of Senate Majority Leader Nicole Cannizzaro, Nev.
• Rep. Gerrid Kendrix, Okla.
• Sen. Chuck Hall, Okla.
• Anthony Sammons, Senate Fiscal Staff, Okla.
• Rep. David Gomberg, Ore.
• Sen. Mark Meek, Ore.
• Rep. Regina Young, Pa.
• Matthew Knittel, Independent Fiscal Office, Pa.
• Sen. Louis DiPalma, R.I.
• Katie Turner, House Committee on Ways and Means, S.C.
• Sen. David Johnson, S.D.
• Rep. John Mills, S.D.
• Krista Carsner, Fiscal Review Committee, Tenn.
• Sen. Donna Frett-Gregory, U.S.V.I.
• Sen. Scott Sandall, Utah
• Jonathan Ball, Office of the Legislative Fiscal Analyst, Utah
• Sen. George Barker, Va.
• Tyler Williams, Senate Finance and Appropriations Committee, Va.
• Emily Byrne, Legislative Joint Fiscal Office, Vt.
• Sen. Andrew Perchlik, Vt.
• Sen. Jane Kitchel, Vt.
• Rep. Diane Lanpher, Vt.
• Rep. Robin Scheu, Vt.
• Sen. June Robinson, Wash.
• Rep. Timm Ormsby, Wash.

The Budget Working Group was made possible through the generous support of The Pew Charitable Trusts. NCSL extends deep appreciation to the members of the working group for dedicating their time and energy to this project and for their thoughtful consideration of how data management can support fiscal sustainability in their states.