The digital revolution, ubiquity of the internet, and rise of Big Data have given government an unprecedented capability to produce, collect, utilize, and disseminate a vast array of information and data.
These trends have ushered in a new era of data-powered government innovation and citizen services based on the undeniable value in making government data widely available – to citizens, activists, companies, academics, and entrepreneurs.
This is often referred to as the “open government” era, which thrives on government transparency, public accountability, and citizen-centered services.
Consequently, the last 20 years have seen a transformation of public policies – legislative, regulatory, and administrative – grounded in the philosophy that access to and dissemination of government data is a public right and that any constraints on access hinder transparency and accountability.
While there is broad recognition of the need to maximize access to government data, the types of government data are increasingly diverse and complex.
For instance, there are many cases where the government collects or licenses private sector data, often combining this data with other data produced by the government.
These data sets are often referred to as “hybrid data” or “privately curated data” – data licensed to or collected by the government that comprises both public and private sources.
Access to and use of hybrid data is increasingly critical for government to transform data into actionable information.
Examples of curated, or hybrid, data sets include the integration of traffic-app data with US Department of Transportation information, the incorporation of private geographic mapping software into local government flood tracking, the federal award infrastructure’s use of the Dun & Bradstreet D-U-N-S® Number to administer and oversee a $1.2 trillion federal grant market, and peer-reviewed scientific and technical literature that is based on government-funded academic research but published in the private sector.
Subjecting this full range of information to unfettered “openness” requirements risks the availability and quality of these valuable data-driven resources.
Such requirements will ultimately harm the public interest when the inevitable “tragedy of the commons” scenario compromises the quality of the data set, as private-sector actors begin avoiding these government partnerships for fear losing control of their data.
Unfortunately, some current open data policies invite unintended consequences – specifically, well-intentioned but overly broad open data mandates that nullify intellectual property rights by extending to data produced in the private sector and collected by, or licensed to, the government.
In these cases, the pursuit of maximum data-driven transparency often conflicts with other important public-interest goals, such as rewarding data driven innovation, safeguarding individual privacy, protecting intellectual property, encouraging private-sector innovation, and promoting the government’s access to data-driven tools that enable smarter decision-making.
Therefore, policies and requirements for openness of government data must contend with these unique challenges and take care to avoid unintended consequences.
To be sure, resolving these tensions is not easy, as it requires the nuanced balancing of competing public interests (e.g., effective and accessible government versus open government), but it is possible – and urgent.
