Increasingly, leading cities are hiring “data people.” Whether with the title chief data officer, chief innovation officer, performance stat program director, or data scientist, these individuals are looking at government in a new way and using data to increase efficiency. Are these hires worth the investment? Resoundingly, the answer is yes. However, their value is often hard to demonstrate. Improvements in the quality or efficiency of public services, or the integrity and accuracy of service delivery, are not easy to measure. Yet these officials are essential to improving results and to restoring faith in our government.
There are some areas where the value of data experts in government has been well established. For example, at the federal and state levels, data analytics can identify fraud and find savings in tax and benefits programs. But at the city level there are few documented examples that demonstrate the return on investment available to cities that hire staff specifically for data analysis across operational and functional lines. Government employees with data skills can be thought of as “utility infielders,” able to work across departmental silos on a variety of projects, their value recognized internally but too seldom documented for the public. This compilation of examples of the financial benefit of hiring a data analyst in government is intended to establish a starting point for continued dialogue on how public servants with data skills can produce a financial return for their taxpayers. Examples include the use of descriptive data for management purposes, such as in stat programs, as well as the development of predictive data analytics models that allow prioritization of government action.
The city of Louisville, Kentucky adopted a data-driven performance management approach to measuring results five years ago, creating the Office of Performance Improvement and Innovation (OPI). Last year, OPI hired a chief data officer to lead specialized geospatial and predictive modeling efforts. OPI quantifies everything in city government, including their own impact, which they calculate at a five-to-one return for every dollar of cost to the city. Some examples of concrete financial benefit for the city include:
- Collection of fees and fines. The city had only a part-time employee tracking fines and fees owed to the city by businesses and residents. When the staffing was increased to three full-time analysts, the collection of overdue payments to the city went from $300K per year to $2.5 million per year, a seven-fold increase, and an amount that pays for the salaries of the staff 22 times over.
- Reducing unnecessary workers’ compensation claims. Data analysts looked at detailed data for the $2 million in annual city payments for the city’s Department of Public Works workers’ compensation claims. By studying patterns and looking for outliers in the data, they were able to reduce the annual cost to $200K by identifying workers whose compensation claims were either invalid or no longer needed, saving the city $1.8 million annually.
- Better oversight of overtime costs. Overtime payments are needed to pay eligible employees who cover for those out on leave or when exigent circumstances require extended shifts. But in the absence of oversight and management, overtime can become an expected part of compensation. Data analysis identified patterns that showed how overtime was used for routine deployment and became the rule rather than the exception. In one year, the team cut overtime by $3.1 million. Special projects have looked at overtime in corrections, law enforcement, and the fire department, and have included management solutions such as holding managers accountable for overtime budgets and having supervisors sign off on overtime shifts.
Reflecting on the success of OPI in claiming revenue owed to the city by scofflaws, and tightening management of operations to avoid excess costs, Daro Mott, Chief of Performance Improvement, says:
“The use of data for performance management has the power to make government smarter, faster, cheaper, and better. With our data-driven approach, we’re addressing the following basic questions: what do we do, how much, how well, and who is better off? Together with department leadership teams, my team co-creates data-driven solutions to problems for the sake of our citizens and taxpayers.”
South Bend, Indiana is a city of 100,000 that punches above its weight in bringing data to bear on government problems, applying data analytics, geospatial analysis, and common sense in the Office of Innovation. The city tracks the return on investment for all of its projects and estimates a total return of $20 million in the three years since the program began, with a large part of that value coming from data analysis on large city contracts to extract additional value.
The team saved the city $5 million via a series of route optimization and fleet management projects. For example, the city saved $1.4 million by moving from larger trucks to SUVs for certain city operations. The city also avoided significant costs for garbage truck replacement when it applied geospatial analysis to the city’s routing of the trucks — the analysis found the city needed only seven garbage trucks, instead of 10, to do its work. Not only did the city reduce costs, it improved the quality of its waste management by putting radio-frequency identification tags on trash bins, which reduced missed pickups by 30 percent. Santiago Garces, the city’s chief innovation officer, says his job is about helping city staff “spend less time doing things that are tedious and more time adding value for the residents.” He recognizes that his team of analysts is small but that it can have strategic value across the city, saying, “The biggest role analytics has played is prioritizing and figuring out what are the things most important to go after for city government to operate at greatest value to the taxpayer.”
In Boston, the Citywide Analytics Team has taken on projects on important topics such as drug overdoses, ambulance deployment, traffic congestion, human trafficking, homelessness, restaurant inspections, the safety of rental housing, and parking enforcement. Chief Data Officer Andrew Therriault, in the position since 2016, recently launched a new project selection and management workflow that makes tracking the return on investment for every project an essential part of the process, and plans to share the results publicly. “The city of Boston has made a major investment in data and analytics over the past three years,” he says, “and it’s already paying off substantially. I’m really proud of what our team has accomplished, and we’re excited to be able to give the public a better view of what we’ve done to make life better for everyone here in Boston.”
A few projects where impact has been quantified already include:
- Saving nearly $1 million a year on energy costs with data. The city’s new energy manager has a dashboard to monitor energy usage in real time, allowing him to track usage and compare to daily and hourly averages. The dashboard was built by city staff, saving the dollars that might have been spent on an outside consultant. And the dashboard is made public so that for several major buildings, any taxpayer can see how their energy dollars are being consumed. The dashboard enables a granular look at the data that has helped save money in two ways. First, it has enabled the energy manager to see usage right down to an individual piece of equipment. This allows identification of outliers in cost that might indicate faulty equipment in need of replacement. Second, and more significant in financial impact, it’s allowed adjustments like reducing consumption slightly during the hours of the day when energy costs peak at high-demand hours. Small changes in energy use that are imperceptible to the public, like lowering fan speeds slightly for a few hours, can result in noticeable cost savings, for example $40,000 a year for implementing this strategy in one library building. Another benefit of the energy consumption dashboard is being able to make real-time adjustments, like lowering energy consumption when the building is closed during a snowstorm. In the first year of implementation, this monitoring system is expected to save the city $700,000 to $900,000.
- $1 million in overtime savings with data analysis. The Boston Fire Department is on track to save $1 million in its first year of implementation of a new overtime and scheduling dashboard created by the Citywide Analytics Team. The dashboard allows the commissioner to view in real time how well each firehouse is doing in staying on budget and in following agreed city scheduling rules. Using this data and new policies and procedures, the fire commissioner can proactively monitor costs and manage efficiently.
- $5 million and 20,000 pounds of carbon emissions saved with data-driven bus routing. The Boston Public Schools held an open competition to develop better school bus routes than the manual ones created traditionally. The task was to develop routes that more efficiently get students to their schools, accounting for traffic, the optimal number of bus stops and students in each bus, special buses that transport special needs students, and graduated start times for various schools. A prize of $15,000 was contributed by private donors, so the cost to the city was zero. The winning solution came from two graduate students and one professor from the MIT Operations Research Center. The algorithm achieves in 30 minutes the task that typically has taken as many as eight people four weeks. Cost savings estimates include transportation savings but not this saved personnel effort. The optimized route uses 50 fewer buses per day at a savings of $5 million per year and a savings of 20,000 pounds of carbon emissions.
The city of San Francisco is a leader in open data and in promoting data use in city government, led by the office of the chief data officer (CDO). While much of the value created has been hard to measure, the city has developed a long-term evaluation plan to assess the impact of its open data program that will measure both the degree to which, among other things, city employees are using the data to make their jobs easier, and the number of external uses of the data.
There are many ways the value of the open data program has already been demonstrated, even if few are easy to quantify. The CDO’s team has developed a culture of data usage across city government by creating and connecting a cohort of data coordinators in departments, by hosting quarterly events to celebrate exemplary uses of data, and by creating an annual Data and Innovation Awards program. The data culture is evident in cross-departmental efforts to publish and analyze data such as the Housing Data Hub, which includes data across all city agencies connected to the challenge of affordable housing, and the neighborhood-specific problem-solving portal for the Tenderloin district, which is a joint project of city economic development, planning, and public health employees. The anchor of their data culture-change program is the Data Academy. The Data Academy has taught over 700 city employees how to gather and understand data, how to analyze and present data, and how to explain data insights to policymakers. At the cost of about one FTE, the city saves $1.7 million a year by empowering employees to use their technology and data skills to save time and money on the job.
New York City is well-known for its application of data analytics to operations and management through the Mayor’s Office of Data Analytics (MODA), established by Mayor Michael Bloomberg and continuing today. MODA is developing a formal structure for reporting out cost savings and increased revenue achieved for the city, along with efficiencies that improve service quality and fairness. One example of a project that will have quantifiable benefits is an effort to implement universal school lunch programs in a way that optimizes program reimbursement to the city from the federal government. Two additional cost savings examples completed in New York include:
- Applying a data-driven approach to adopting private-sector best practices in office configuration, in which the real estate was viewed as a city asset and was managed as a portfolio for the first time. In first three years of this effort, the city reduced office space by 400,000 square feet and saved $15 million in annual rent occupancy cost. Additional savings in energy costs totaled $4 million as the footprint shrank.
- Using data analytics, the city improved efficiency 40 percent in finding fraudulent tax returns. David Frankel, who was commissioner of the New York City Department of Finance from 2009 to 2013, led an effort to use data to identify taxpayers who failed to pay accurately. Using data from city, state, and federal tax records, along with other data such as business licenses, the team looked for patterns — identifying similar businesses and their tax payment patterns to identify outliers who might have failed to pay taxes or who paid too little. This increased the ability of the city’s auditors to target cases that were in fact fraudulent, while reducing the burden on compliers.
Cincinnati, Ohio created its Office of Performance and Data Analytics in 2015, an operation that includes performance management, open data, and advanced geospatial analytics. Between new revenue and cost avoided, the team has achieved $6.1 million in value for the city since inception. A few examples:
- Data analysts reviewed overdue fees and fines owed to the city across departments and found that many could be collected with more aggressive follow up, including using collection agencies. Their work garnered $523,840 in additional funds for city coffers.
- By training city staff to do work formerly completed by an outside contractor, the city avoided $350,000 in annual cost to the contractor by internally completing the data entry and data analysis for Cincinnati Police Department data on traffic stops and citations.
In Syracuse, New York, the city’s chief data officer managed a project through the University of Chicago’s Data Science for Social Good program that developed a predictive model to identify the water mains at greatest risk of breakage so the city could prioritize preventive repairs. The model incorporates 12 years of water main data, including data about the age and diameter of the pipes and the geology of the soil and road quality above them. The model allows for strategic deployment of sensors on the water mains, as well as for preventive repair and replacement in coordination with other public works projects. The new model is six times as effective as the status quo process for preventing water main breaks. Implementing this data-driven model of proactive water main replacement will save the city $400,000 this year and $800,000 next year by avoiding emergency repairs, and replacing them with proactive rather than reactive water main repairs.
New Orleans created its Office of Performance and Accountability in 2011 and has long been a leader in applying data insight to achieve better results for the public, both via citywide performance management and by targeted use of predictive analytics. Many of the high-priority analytics projects have focused on saving lives, including efforts to more strategically deploy ambulances, using risk data to get smoke detectors into the most vulnerable homes, and applying behavioral economics to close health disparities.
The city data team has also applied its talents to work that brings in revenue or avoids costs for the city. The city’s blight reduction project addressed 15,000 blighted properties and reduced inspection time from 160 to 80 days, while reducing the backlog of appeals, all by applying data analytics and a stat process to managing the effort. Financial benefits have not been calculated, but include additional fines and fees collected from errant homeowners and significant additional tax revenue as the properties are returned to productive use. As part of that project, the team applied behavioral economics to “nudge” homeowners of blighted properties to address the problems reported to the city. As a result, the increase in homeowner compliance resulted in a net savings of an FTE in the inspections team.
The small suburban town of Wellesley, Massachusetts has saved $132,000 in energy costs using data analytics. By managing and reporting regularly on energy use for each town building and benchmarking the results, the town reduced energy use by 9 percent over a three-year period, even with 37 percent more extreme weather days. Installing exterior LED lights in town buildings is achieving 15 percent more light and a 10 percent overall reduction in electricity energy costs.
The city of San Diego, with its open data portal, predictive analytics, geospatial analysis, and performance management program, has achieved significant savings, just a few years into its innovation efforts. Many projects have clear value in efficiency and will have future cost savings. For example, innovation teams using Lean Six Sigma methodology streamlined the 911 call answering and dispatch process to offload non-emergency calls, allowing true emergency calls to be answered faster. Another project resulted in a 42-percent improvement in the time spent hand-sorting materials that library patrons request through careful analysis of motion diagrams and process maps and the elimination of non-value-add steps in the process. One project with a significant projected savings is the “smart city” initiative. This effort includes sensors on streetlights that allow automatic dimming and brightening of the lights, estimated to save $2.4 million annually on the city’s energy costs.
The city of Chicago has been a leader in open data and in the use of predictive analytics to improve city operations. Development of sophisticated predictive analytics models have resulted in major quality of life improvements for city residents, including increased effectiveness of restaurant inspections and improved accuracy of estimates of unsafe swimming conditions at city beaches. The city’s rodent abatement program is now 20-percent more efficient based on the predictive model used to identify problems before they occur.
The open data program has been successful in not only increasing transparency and data sharing across agencies, it has also resulted in tangible benefits. For example, the city’s Department of Public Health noticed a 40-percent decrease in Freedom of Information Act requests after the launch of the city’s open data portal. This has allowed that department to deploy its resources on higher value-add activities rather than photocopying documents and mailing them to requestors.
The city of Atlanta has an innovation team in the Mayor’s Office populated with data analysts focused on solving high-priority problems for the city. When faced with a $1 billion infrastructure backlog, the group focused on finding financial efficiencies. Their data analysis skills produced a high financial return including:
- $4 million was saved through employee health-care plan consolidation and optimization and by adopting some private-sector best practices in health-care management.
- $3.6 million in additional annual revenue was achieved from implementation of customer service and technology improvements related to the collection of fees and fines for a variety of departments. This effort included a benchmarking study comparing the city to comparable jurisdictions, and a review of best practices in customer service.
- $1 million reduction of workers’ compensation claims due to safety improvement programs. For example, one effort used event-recorder camera technology in city vehicles to proactively reduce accidents.
In Baltimore, the implementation of a budgeting for outcomes strategy used data in focusing on results for the city’s top priorities, carefully examining data and evidence on what was working and what wasn’t. Data analysts worked in cross-departmental “results teams” to identify efficiencies and opportunities for cross-agency collaboration to achieve the best outcomes for the public. Efficiencies included both cost savings and increased revenue. For example:
- Looking at data for code enforcement, one results team increased revenue by $500,000 when they invited competitive proposals for code enforcement as the Housing Department was able to more efficiently perform the burglar alarm registration program than the Police Department, which had been running the program.
- By examining payment data, one results team identified duplication of effort and recommended that the city’s transportation department piggyback on an existing recreation and parks department mowing contract to reduce the cost of median strip mowing by $1.5 million.
- Results teams spanning the fire and health Departments decided to assign nurses to frequent 911 callers to prevent repeat calls. This innovation reduced 911 call volume for these so-called “frequent fliers” by 50 percent, which improves response time and saves money.
While these examples are by no means exhaustive, they demonstrate that hiring a data analyst in city government, and assigning that person to find economies, can pay for their salary once over, or in some cases 22 times over. Each dollar value in quantifiable savings represents immeasurable added value in restored faith in the efficiency and integrity of government. As the field continues to grow, we hope to catalog both substantive successes of data-driven government, as well as the concrete returns for taxpayer value.