Using Data to Strengthen Communities

Data has improved the lives of many people around the world. More efficient work flows, better traffic and weather forecasting, enhanced educational methods, and improved medical care are all ways that large data sets, and the careful analysis of them, have made many people’s lives better.

However, the benefits of these gains are not equitably distributed, either around the world or in the United States. There are groups of people who do not have improved medical care or easier credit access or enjoy better educational methods as a result of advances in data collection and usage.

At the heart of this problem in the United States is, perhaps not surprisingly, a different but related data problem. Every year, the US Census conducts the American Community Survey to collect data on a representative sample of Americans. In any given year, the Census sends out between 3 and 3.5 million surveys, and usually ends up with a little over 2 million final interviews to make their findings. The Federal Government then heavily relies on the answers of those 2 million to make funding decisions for over $400 billion in federal spending in communities, including transportation, education, and economic development; all of which rely heavily on good data for effective implementation.

Even though responding to the ACS is a legal obligation, many do not answer. In so doing, they run the risk of skewing the data that the government uses to make its community spending decisions, including federal spending for disproportionate-share hospitals (DSH) that treat a large number of indigent people, federal grants to state transportation departments to fix roads, and federal spending for free and reduced lunches for students. This means that the biggest tool for addressing some of the data gaps—collecting data through schools, transportation, and hospitals—can be misled by a lack of data.

As meta and recursive as this problem is, there are ways to help make the survey more inclusive. In “The Rise of Data Poverty in America,” written by Daniel Castro of the Center for Data Innovation, outlines four recommendations for addressing the gaps in data:

  1. Continue government data collection programs that focus on hard-to reach and underrepresented communities. Focusing on those communities will directly improve the money available to help their communities.
  2. Ensure that funding programs aimed at closing the digital divide consider the impact on data poverty. Making sure that all individuals have access to broadband internet will make it easier to get more data and improve the veracity of larger data sets.
  3. Ensure that digital literacy programs help individuals understand data-producing technologies, such as social media and the Internet of Things. As networks grow and the smart use of data becomes more prevalent in the home and for individuals, educational programs need to incorporate lessons in these topics for children and adults.
  4. Encourage civic leaders in low-income neighborhoods understand the benefits of data and know how to integrate technology solutions into grant proposals. As leaders of low-income communities strive to improve them, their ability to use and execute data will need to increase as governments and companies increase their dependence on data.

Reaching out to individuals and families in all communities and making sure that they understand the need of good data and the benefits that good data collection and management offer are important steps to paving the way for improving the physical and economic health of many of America’s communities.

Better data can do more than just strengthen companies, it can strengthen communities.