Unit 5
How David Eaves teaches Unit 5 (part 1)
Guest Lecture - How Governments are Using Data
What is this page?
This is a detailed breakdown of how David Eaves, a Lecturer at the University College London's Institute for Innovation and Public Purpose (UCL IIPP), teaches the contents of Unit 5 of the open access syllabus developed by Teaching Public Service in the Digital Age. Read how part two of Unit 5 is taught here.
It is part of a series of twenty-five classes that David developed originally for the Harvard Kennedy School's master and executive education programs, where he taught for eight years, and are now taught at UCL's master and applied learning programs.
This page contains a summary of a presentation by Dr Amen Ra Mashariki, former Chief Analytics Officer for the City of New York and the Director of the Mayor's Office of Data Analytics, given to David Eaves' students enrolled in "DPI-662 Digital Government: Technology, Policy, and Public Service Innovation" at the Harvard Kennedy School. This class, combined with a second, enable David to teach across the learning outcomes described in Unit 5.
We believe presenting diverse ways to teach the syllabus will help others adopt and teach the material in various contexts. See here how Konstanz University's Prof Ines Mergel teaches the same unit.
Who is this page for?
This page was developed for university faculty who teach public administrators or master's levels students in public policy and public administration. This material may also be suitable for teaching to upper year undergraduates.
Guest Lecture Overview
In the unit on data - we focus on issues public sector leaders and managers will need about in regards data: Data for decision making, Data as infrastructure and Data is political.
To open this unit we invite in Dr Mashariki talks about his experiences on these three topics and provides insights on how data analytics' teams help make governments more effective.
While Dr. Mashariki covers a range of topics there are three areas that are of particularly interest to students of this course.
Data for Decision Making: Dr Mashariki discusses the competencies and functions of a policy-maker data scientist and the challenges and opportunities associated with this role. This includes tactical challenges such as:
how and when to rely on domain expertise from the policy and operational experts working the issue
how to scope and define problems for which a policy-maker data scientist can be additive
managing the politics of engaging with departments that fear data analytics will expose problems or not lead to relevant insights
These insights will be particularly helpful for policy makers and public administrators that hope to apply data analytics to policy and service delivery challenges.
Data as Infrastructure: Dr Mashariki shares his outline of the civic data "life cycle" - how data is created, how it flows through an organization and some of the management challenges that appear at each stage. This view is critical for senior public administrators who will be accountable to how data is managed and used on their watch. (David will expand on this element of the presentation in Part 2 of the class)
Assigned Readings
The Analytics Playbook for Cities: A Navigational Tool for Understanding Data Analytics in Local Government, Confronting Trade-Offs, and Implementing Effectively (2020), Amen Ra Mashariki and Nicolas Diaz for the Ash Center at the Harvard Kennedy School.
Open data outcomes: U.S. cities between product and process innovation (2018), Mergel, Kleibrink, and Sörvik.
It's the icing, not the cake: key lesson on open data for governments, (2011), David Eaves
Short Videos and Insights
In this presentation, Amen Ra shares insights on how cities use data and what it means to lead a data team in a city government. The main insights and takeaways from this conversation are explained in the videos below.
🙏 I'd like to express gratitude to Dr Amen Ra Mashariki for sharing his experiences from his time at the City of New York. Amen has spoken to my students about his experience on numerous occasions and has inspired several that have gone into data analytics roles in local government.
Answer questions relevant to the city's leadership: Teams that go to the office and brainstorm interesting things that could be done with data tend to be the least successful. In a city analytics team, focus on answering the city's most relevant questions irrespective of what data you have is essential.
Effective analytics teams go on the ground, connecting and engaging with users and experts: The most relevant data won't necessarily be waiting to be manipulated. The best way to use data is to ask experts for their hypothesis on the problems they are trying to solve and then collect or approximate the relevant information. Soft skills and curiosity are some of the most important skills you should look when recruiting team members. When questions come to you, you should be curious to ask follow ups and to find creative ways of answering with existing or non-existing data.
Understand the lifecycle of data in a city: The data lifecycle in a city used to take months, sometimes years to complete. Now with the right practices and methodologies this cycle can complete in days. Understanding the lifecycle of data, how it flows from collection to modelling to analysis and decision making is critical to anyone doing data analytics.
Be problem-driven, not data-driven: Start with the problem, not the solution. Sometimes we are tempted to start deploying models that are not solving the main problem. Step back and try to really understand the root problem.
Data is not always useful: Often those engaged in a problem already have the relevant information about the problem. The art and judgement of an effective data analytics team is to identify when a model will predict or explain an outcome that is already known versus exposing an unknown insight. For example, sometimes a problem is well understood. The challenge is marshaling the political will and/or resources to address. Gathering more evidence about, or incrementally better modeling the problem is often not additive in such circumstances.
Sometimes the best solution is a very simple model: When you have one or two weeks to solve a problem, don't aim for perfection and use complex models. It is often more important to extract quick and useful insights that will provide guidance to the city's leadership and citizens. In this process, many times the insights come from simple models such as a conditional probability formulas (which is computed using simple division).
Focus on building skills in common types of problems: Over his years working in NYC, a pattern to problems emerged. These include targeting, prioritizing the backlog, detecting anomalies, analyzing scenarios and estimating resources. Being aware of these categories help data analytics teams be more focused on how they serve other departments and build up specialized capabilities.
Your team will probably work behind the scenes: It can be hard for governments to acknowledge the work of data teams in public. This is due to the fear of raising concerns with practices that might be wrongly interpreted as privacy violation or impinging on freedoms. It also speaks to the need to increase the capability of public organizations - and their leaders - to be able to talk about this work in a nuanced and sophisticated manner with the public. As a consequence, data analytics teams don't always have public recognition of their work.
⚠️ This can be an excellent point to talk about the ethics of data and the trade offs practitioners have between using data to solve problems that might save lives and the need to balance privacy and security - or the publics concerns about privacy and security.
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Acknowledgements
David Eaves would like to note that this material was made possible by numerous practitioners and other faculty who have generously shared stories, pedagogy and their practices. David is also grateful to the students of DPI 662 at the Harvard Kennedy School for enriching the course and providing consent to have the material and questions shared. Finally, an enormous thank you must be given to Beatriz Vasconcellos, who helped assemble and organize the content on this page.