The purpose of the workshop was to build on the recent research effort on Big Data and Urban Governance sponsored by the Inter-American Development Bank’s (IDB’s) Housing and Urban Development Division. This work, conducted during the summer of 2016, resulted in the creation of a big data maturity model for urban governance. The workshop provided an opportunity for external review of this work by peer researchers, identified in the course of our literature review, who are developing and using urban data maturity models in other geographic and institutional contexts. The workshop also provided an opportunity to tap this group for advice and insight on IDB’s future efforts in this area, including the design of a data innovation lab in Santo Domingo, Dominican Republic, as well as further guidance on how to refine and improve the IDB data maturity model.
The workshop produced several shared insights around the develop- ment, use, and impact of urban big data maturity models for cities:
- Maturity models are a valuable tool for engaging city leaders in discussions about data innovation, and producing self-assessments that support planning specific strategies and actions.
- Data science is not magic, and it is very important that maturity models make this clear to city officials to calibrate their fears and expectations.
- Substantial impact can be achieved even at low levels of data maturity. Many pressing problems are solvable using existing tools and techniques and do not require big data approaches. Problem-focused and simple solution approaches shouldn’t be undervalued with governments.
- Data maturity progress depends less on data scientists in government and more on public sector entrepreneurs to get things done.
- Data maturity needs to be assessed across all units of urban government, and partners. Data innovation efforts are team projects. This is implied but not explicit in some maturity models.
- Data shared between departments (not necessarily open data) can have the largest impact. The importance of sharing internally/across departments shouldn’t be overshadowed by an open-data mandate.
- More documentation about data-sharing projects is needed so that patterns can be identified around successful strategies, tension points, and factors of failure.
A handful of open questions on data maturity models remained:
- Is strong mayoral leadership required to make a maturity model useful as a planning tool?
- Are generalized maturity models as effective for building long-term engagement with partners as problem-specific approaches?
- How can maturity models reflect ability to create incentives, per- missions, institutionalization, and other political economy concerns?
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