The Case for Data-Driven Decision Making in Government
ALBANY, N.Y. (Feb. 1, 2022) – From local municipalities to national governments, the opportunity to leverage data in decision-making processes has never been more widespread. Yet, despite abundant technology resources, hurdles remain for government officials to bring data into their policymaking in a structured way.
A new research paper by the Center for Technology in Government at the University at Albany (CTG UAlbany) takes a closer look at how Data-Driven Decision-Making (DDDM) and Evidence-Based Policy Making (EBPM) can be used as a tool to improve government.
In “Towards Data-Driven Decision-Making in Government: Identifying Opportunities and Challenges for Data Use and Analytics,” researchers Yongjin Choi, J. Ramon Gil-Garcia, G. Brian Burke, Jim Costello, Derek Werthmuller and Orguz Aranay found that neither quality data nor advanced analytic techniques alone guarantee effective DDDM — organizational resources and capabilities are also needed.
"While an increasing number of public agencies have embraced DDDM or EBPM in recent years, there are few studies demonstrating how challenging they are to implement. They encompass more than the utilization of data,” said Choi, a doctoral candidate in the Department of Public Administration and Policy at the Rockefeller College of Public Affairs and Policy. “This case study demonstrates that transitioning toward DDDM and EBPM requires significant investments and steady organizational support as well as high quality data and analytic techniques."
Through a case study involving a state agency tasked with overseeing water quality in New York State, the researchers found that the introduction of data analytics helped produce evidence that could be leveraged for broad planning and designing of environmental interventions. However, the data instead simply became another source of information involved in routine decision-making.
Part of this has to do with the time-consuming and costly nature of the data collection. As water quality scientists and consultants gather data based on manual fieldwork to meet federal requirements, they are often limited in terms of samples by time frames and geographic scope and statistical analyses for identifying drivers of water impairment.
Further, when presenting data to stakeholders, the original datasets are often insufficient to convey findings to top policymakers or the general public, who may not have the scientific expertise to evaluate the information. This then requires the additional step of crafting digestible evidence and storylines which can be more easily understood by nonscientific community members.
One way around these hurdles is to have organizational buy-in at each level, whereby from the data collection to delivery, the agency is leveraging all available resources to improve analytic capabilities and increase the amount of evidence for decision-making.
CTG UAlbany found nine determinants in the process of innovation that could dramatically expand an organization’s ability to leverage DDDM. These determinants focus on data and technology, as well organizational and institutional designs that could either help or harm an agency’s ability to improve DDDM:
- Data: data quality and coverage, compatibility and interoperability, and external data
- Technology: information systems and software and analytical techniques
- Organizational: cooperation and culture
- Institutional: privacy and confidentiality, and public procurement
Taken as a whole, these determinants each present opportunities and challenges within the process of transitioning toward effective DDDM in government.
“The organization we examined has been implementing DDDM previously to some extent in that its decisions have been heavily relying on data use and data analysis, but the existing tools and techniques could not provide enough evidence in light of new complicated environmental issues,” said Gil-Garcia, director of the Center for Technology in Government. “This gap has become a motivation for the organization to revisit and attempt to improve its analytic capabilities to the extent of being able to produce more appropriate evidence for decision-making.”
The paper was presented as part of the Proceedings of the 54th Hawaii International Conference on System Sciences.