Challenges in Data-Driven Policymaking: Using Smart City Data to Support Local Retail Policies

Abstract

Abstract. In Flanders (Belgium), there is a growing awareness among city administrations and governments to turn their cities into smart cities by transforming their policy decisions into evidence-based and data-driven policymaking. Nonetheless, many Flemish cities still face several challenges related to the integration of data-driven policy evaluation research at different stages of their policy making processes. For that reason, the ‘Smart Retail Dashboard’-project was set up, which aims to develop a dashboard for helping Flemish cities in transforming their retail policies into data-driven policies. This paper reports on the initial findings of this project. The most important finding is the varying degrees of maturity across cities regarding their smart retail policies. This is illustrated by the current state of data-driven policy making in the retail sector in Flanders. Subsequently we provide an overview of the current challenges reported by mature and less mature cities, which range from the cost and the usability of data to the analysis and interpretation of such data. We discuss how a Smart Retail Dashboard could provide answers to these challenges. This discussion helps city administrations to transform their policymaking processes into data-driven processes and dashboard developers to optimise their development processes.

Setting Requirements for a Dashboard to Inform Portuguese Decision-Makers About Environment Health in an Urban Setting

“…The lack of standardization hampers a potential comparison of datasets from different sources and may lead to unreliable data. The comparability of periodical and geographical data is highly important, as benchmarking could be required for decisionmaking (9).…”

“…Common challenges related with health data have been data standardization; database linkage, integration and sharing; selection of key data for decision-making; and finding a balance between evidence and research data to be included within decision tools (5,6). Challenges related to translating data into better decision-making concern the creation of decision aiding tools in which evidence and data are understandable and inform about the extent to which specific policy goals are attained (7,8), and tailoring such tools to the specific settings (9). This incorporation of data within decision aiding tools requires engaging organizations and policymakers in the early stages of design of the tools (10), which can then contribute to a shared awareness about data concerns and also to motivate organizations to improve data consistency and accuracy (11,12).…”