Author: Nora Stechschulte
Publish Date: July 6th, 2020
Recent political history and the COVID-19 crisis have created further tension and distrust between the government and Americans around the use of personal data. Public polling shows that 84 percent of Americans say they have very little or no control over the data the government collects about them, 78 percent say they understand very little or nothing about what the government does with their data, and 66 percent think that the risks of data collection by the government outweigh the benefits. Given this uncertainty; federal, state, and local governments face an uphill battle convincing Americans that using their personal data to inform policymaking benefits the public.
Despite public opinion, the federal government has made substantial progress in the use of data, and so far, these efforts have been successful. For example, the Chief Data Officer (CDO) of the Health and Human Services Office of the Inspector General, Carol Brzymialkiewicz, and her team of data analysts uncovered $1 billion in fraud in 2016. In the same year, the United States Postal Service used analytics to eliminate waste and abuse of funds, saving taxpayers $920 million. More recently, the Foundations for Evidence-Based Policymaking Act of 2018, combined with an Executive Order “Maintaining American leadership in Artificial Intelligence” and improved implementation of the “Information Quality Act,” have led to massive improvements in federal government data-sharing and the use of data in policymaking. At the local level, open data policies allow the general public to access information like teacher performance reviews, local government spending, and health code violations of frequented restaurants. The federal government’s platform for open data sharing, data.gov, has existed for ten years, but only five percent of Americans believe the federal government is very effective at data sharing. Despite these data and analytical wins that have directly benefitted the American people, the public is still apprehensive about letting the government use their personal data to make policy decisions.
Though the federal government may need to continue maturing its data and analytics capabilities, it is evident that the public is largely unaware of steps that have already been taken to improve transparency and access to government data. The federal government needs to publicize the progress made and demonstrate the usefulness of the data collected to support policymaking efforts in order to build public support for the use of data. By doing a better job of telling the “success story,” CDOs and data offices within the federal government can gain the public support they need to increase their budgets and have a continued impact on policymaking.
To demonstrate their success, federal government CDOs need to deliver highly visible value to its customers, the American public. Some examples of ways to demonstrate the value and security of government use of data include:
Send out mailers that illustrate the findings of their data analysis and clearly demonstrate the utility of data use
Create user friendly platforms where constituents can easily access data like teacher performance reviews
Add infographics to highly trafficked sites that explain how the government collects and uses data. For example, the CDC can create an infographic for use on its website or in news conferences that describes how CDC has collected and analyzed personal data in a secure and useful way in order to impact policy and minimize the effects of COVID-19
Though these projects will take time and effort, they will demonstrate the success and personal utility of government use of data and gain the public support needed to proceed with large scale data projects. Merely granting access to citizens is not enough: the government must deliver value directly to their constituents instead of letting citizens try to create value on their own.
Recently, implementation of federal data strategies has taken a backseat to our current COVID-19 crisis. All the deadlines for the Federal 2020 Action Plan have been pushed back and resources have been redirected to focus on “mission-critical” policies to mitigate the effects of the virus and stop the spread. However, if the government’s successful use of data was more widely understood, it is likely that data would be driving COVID-19 policymaking. Pushing back the implementation of the 2020 Action Plan puts the government at risk of missing out on groundbreaking solutions and garnering the public trust and support is necessary to overcoming this pandemic and future crises.
A lack of public acceptance has driven decision-makers back to the historical and more familiar method of intuition-based decision-making. The federal government should focus now on data-driven policymaking and use data-driven insights to solve this crisis and take our country to a “new normal” method of government decision-making, based on data. All levels of government must be better at sharing its success and promoting a data culture not only within itself, but across the country. The intuition-based decision-making path will not work to combat the virus because COVID-19 is unlike anything we have previously seen.
Though data-driven policies have the ability to drastically improve the efficiency of government at every level, the potential of these policies will never be fully realized without the support and understanding of the American people. The federal government has made substantial progress in the collection and use of data, yet it has failed to adequately communicate this to its constituents. Governments must actively educate their populace on the collection and use of data, as well as the potential benefits of data-driven policies. Governments must make the data they collect usable and applicable for their constituents. Without an active effort to gain the trust of the American people, government efforts to utilize data for policymaking will never meet its full potential and the 2020 action plan will be nothing more than another missed talking point.