Introduction
Technological advances have made it cheaper and easier than ever to collect, process, and use data. This data helps individuals, businesses, and governments make better decisions, and data-driven innovation is a critical pathway for boosting social and economic prosperity. But, in a world in which economic opportunities, government services, and health-care outcomes are intricately linked to data, how individuals and communities are reflected in datasets and how they can use datasets about themselves significantly impact their ability to fully participate in the data economy. Divides are emerging between the data haves and the data have-nots, and these data divides can greatly impact individuals and communities. While many in academia, civil society, and the public sector have considered the impacts of the digital divide, such as disparities in access to broadband, mobile devices, or computers, few have explored the data divide or considered steps to address it.
The data divide refers to the social and economic inequalities that may result from a lack of collection or use of data about individuals or communities. Data divides can manifest in various ways. People in certain places may face greater environmental risks because an insufficient number of sensors gather data about their environmental conditions. Likewise, patients may receive inadequate medical treatments because their demographic is underrepresented in clinical trial data. Other times, some students receive suboptimal educational opportunities because school districts lack the systems to track and measure links between educational programs and outcomes. These data divides can emerge for different reasons, including a lack of resources, political pressure, or legal and regulatory issues.
As the Center for Data Innovation first wrote in its 2014 report, The Rise of Data Poverty in America, some Americans will be born in hospitals leveraging health informatics, attend schools powered by learning analytics, and live and work in “smart” communities that use data to maximize their economic, social, and environmental prosperity. But others won’t, and the scarcity of data about themselves and their communities will mean that they will not benefit from the advantages of an increasingly data-driven world. These imbalances in data collection and use lead to data divides, and policymakers should prioritize addressing these data inequalities.
This report offers an overview of the data divide in the United States and recommends actions that policymakers should take to ensure the fair and equitable representation and use of data for all Americans. First, the report defines the data divide and contextualizes it within the conversation of the more-familiar digital divide. It identifies several areas where data divides persist, ranging from demographic and geographic data gaps to instances of inequitable data systems. Finally, it offers nine recommendations for how policymakers can address the data divide:
- Improve federal data quality by developing targeted outreach programs for underrepresented communities.
- Enhance data quality for non-government data.
- Ensure comparable data collection and monitoring methodologies among the government and civil society.
- Support increased utilization and incorporation of crowdsourced and private-sector data into official datasets.
- Improve documentation and quality of prominent AI datasets to reduce the number of situations with biased results.
- Provide funding from core federal agencies to close both the digital divide and the data divide.
- Direct federal agencies to update or establish data strategies to ensure data collection is integrated into diverse communities.
- Amend the Federal Data Strategy (FDS) to identify data divides and direct agency action.
- Establish a bipartisan federal commission to study the data divide.
Read the report. (PDF)
