In a RECENT BLOG POST Sam Edelstein reflects on his time as the Chief Data Officer of the City of Syracuse:

When I took the chief data officer job with the city, I thought I’d get to grab all the data and build predictive models which in turn would save millions of dollars, make people’s lives easier, and prevent issues like infrastructure failures. While I’ve gotten to do this a couple of times, on a day to day basis, the main goal is generally to figure out how to count stuff correctly.

His experience is symbolic of a lot of data science work in the public and nonprofit sectors. Every now and then there is an opportunity to create a cool predictive system that generates considerable impact. But most days data science professionals spend their time making incremental (but important!) improvements by building better databases, finding new ways to measure things, digitizing old files, “figuring out how to count stuff correctly”, identifying trends, and nurturing communities of practice.

Edelstein’s experience is not unique. For example, an MIT case study on Data-Driven City Management [1] notes:

Many organizations have been slow in compiling, classifying, and organizing the data sitting in siloes and dark corners. It’s “a boring, boring job,” says Ger Baron, Amsterdam’s first-ever Chief Technology Officer. “But very useful!” The Netherlands’ capital has 12,000 different datasets, and even they can’t tell him everything about the city. For example, no one knows exactly how many bridges span Amsterdam’s famous canals, because the city’s individual districts have not centralized their infrastructure data.

The challenge is not unique to the public sector [2]:

Organizations across the business spectrum are awakening to the transformative power of data and analytics. They are also coming to grips with the daunting difficulty of the task that lies before them. It’s tough enough for many organizations to catalog and categorize the data at their disposal and devise the rules and processes for using it. It’s even tougher to translate that data into tangible value.

For some useful case studies on data science applied to the city management context, see:

  1. Fitzgerald, M. (2016) “Data-Driven City Management,” MIT Sloan Management Review, May 2016.
  2. Kiron, D. (2017). Lessons from becoming a data-driven organization. MIT Sloan Management Review, 58(2).
  3. Quaintance, Z. (2017). “Summit on Data-Smart Government Day Two Shows How Fast the Discipline Has Grown” Gov Tech: Nov 9, 2017.
  4. Hatry, H., and Davies, E. (2011). “A Guide to Using Data‐Driven Performance Reviews.” IBM Center for the Business of Government.