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Why Learning Data Analysis Is Essential for Public Administrators

Written by: Professor Anirudh Ruhil
Associate Professor of Leadership and Public Affairs at Ohio University’s Voinovich School of Leadership and Public Affairs
Email: ruhil@ohio.edu

Private-sector companies understand the power of big data. Insights gained from effective data analysis can be a game changer for a business, and private companies, large and small, are working hard to take big data to the next level. According to NewVantage Partners’ 2017 Big Data Executive Survey, almost 40 percent of Fortune 1000 executives asked said their organizations have invested more than $100 million in big data initiatives within the past five years. A few of them—6.5 percent—have invested over $1 billion.

It’s a different story in the public sector. Some government agencies and public administrators have acknowledged that understanding big data could yield important insights or better work processes, but on the whole, the public sector lacks a sufficient number of people with expertise in the complicated, specialized, burgeoning world of data analysis. This is a shame. If the public sector had more specialists who were trained in the same kind of data analysis techniques that innovators within Google and Facebook use, it wouldn’t be surprising if policymakers and public administrators were able to pursue more efficient solutions to our nation’s most pressing social issues.

That’s a big statement, and, of course, it’s easier said than done. Part of the problem is that the issues to solve—e.g., failing schools, rural health crises, and infant mortality rates that are higher among some groups than others—are, in themselves, so seemingly impenetrable and complex. Even more intimidating is that the proposed solution is such a humbling undertaking, to say the least. The mountain of big data currently being produced is so substantial that even efforts to quantify the data set fall short. Consider this: From the start of recorded history up until 2003, we as a species created roughly 5 billion gigabytes of data. Beginning in 2011, the same amount was generated every two days. Now, much of that data is being gathered by the rapidly expanding internet of things (IoT). Imagine a near future in which street lights are wired to “know” when their lights are about to burn out, bridges fitted with wires send alerts when road conditions are icy, and smart traffic systems instantaneously reroute traffic to ease congestion. Other IoT technology is wearable, which means it can collect data on whole communities’ health and exercise habits. It’s a lot of data, and it’s extremely useful if properly analyzed by public administrators. What are the best ways to solve the problems we face? The answers could be hiding within these mountains of data.

An important place to start is with an acknowledgement that various types of mindsets need to coexist in the public sector. Policymakers have honed essential skills in writing and shaping policy. An entirely different set of skills is required to analyze the results of policy. This latter mindset necessitates a belief in the beauty of math and an excitement rooted in the faith that math and statistics, if wielded correctly, can be tools to solve enormous problems.

Once that mindset is acknowledged and more experts in statistical analysis are brought into the public relations realm, how do public administrators deal with the procedural challenges wrapped up in gathering and analyzing data efficiently? Every problem will require its own process, but in general, the steps align with the ones I take when I dive into an issue and try to see it in a fresh way. For example, I have recently begun researching the question of why African American infants have a higher mortality rate than other groups. In seeking to answer this question, I start with a fairly accessible and unglamorous tool: Google.

Using Google, I search for studies on infant mortality, and through the reports that I find, I determine how others are approaching the question. After this survey of peer research, I devise research plans replicating the framework of the methodology in the top studies. The next step would be to adapt the best methodology to the available data and the problems to solve. After the crucial analysis stage, it’s important to consider what can really change effectively, in terms of changing systems to lead to the solution. And, of course, it’s important that public administrators and government bodies then dedicate themselves to the communication and education efforts that will help to sway the public and get it on board.

If taken seriously within the public sector, data analytics could help us rise above the ideological debates that divide society. Truly trusting data means starting with a blank canvas, free of biases about what the analysis will find, and then letting the data dictate where the problems are and where solutions should focus. Once a policy is in place, public administrators must exhibit a continued commitment to data analysis, which means that they must continually question and analyze the given policy’s effectiveness. If we do that, and we keep gathering data, we’ll always be able to return in a few years and receive an answer. As the right questions keep pointing us toward the right answers, we can slowly push our way into a better world.

About the Author

Anirudh Ruhil is an Associate Professor of Leadership and Public Affairs at Ohio University’s Voinovich School of Leadership and Public Affairs. Along with Holly Craycraft, he oversees the Honors Tutorial College/Voinovich Scholars Program, a rewarding applied research program enrolling some of Ohio University’s most promising undergraduate students interested in leadership and public affairs. Ani also teaches quantitative research methods, policy analysis, and (occasionally) program evaluation for the Voinovich School’s Master of Public Administration (MPA/OEMPA) academic programs, and serves as a quantitative research methodologist/data analyst for the School.

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