Unit 5

Data Part I - Uses & Opportunities

Syllabus > Unit 5

Overview of this Unit

The purpose of Unit 5 is to help statistically literate students to identify opportunities to use data to solve real world government problems, and to anticipate the practical challenges they will face when doing so. It does not involve teaching granular data science skills which are beyond the minimum digital era competencies that apply to all public service leaders. We provide links to full depth courses on those skills, below.

This material, developed by 'Teaching Public Service in the Digital Age', has been prepared to help university faculty to add digital era skills to the teaching of Masters in Public Policy and Masters in Public Administration programs. All these materials are based on our eight Digital Era Competencies - this unit corresponds mostly closely to Competency 7.

This unit is one of eight units that make up a full semester course. The units have also been designed to be used by educators independently, without students taking the rest of the course. This unit can be taught in either one or two classes.


Learning Outcome 1

By the end of Unit 5 students will be able to identify common archetypal purposes for which governments make use of data. For example:

  • As a tool to inform policy design and to set strategies

  • To monitor compliance with laws and programmes

  • To understand citizen needs and behaviours

  • To monitor performance

  • To identify and prevent new problems

  • To directly power a public service (for example biometric passport gates)

  • To streamline internal administrative processes (for example usingAI)

  • To enable policy and programme evaluation

  • To meet the needs of parties outside of government (for more see Unit 7)

Learning Outcome 2

By the end of Unit 5 students will be able to explain the key challenges that affect government's ability to use data successfully. This includes:

  • Key decision-makers sometimes lack familiarity with how data is stored, processed and used in the digital era, including how AI systems access data

  • Key decision-makers often lack knowledge of how to integrate data technologies into their administrative processes

  • Governments hire or contract individuals with no data skills, or with the wrong skills to process and use data, including both inappropriate technical skills, and individuals with a lack of understanding of government operations and public policy challenges.

  • Government data is often stored in silos, and in non-standardised ways, making its use for many purposes difficult or impossible.

  • Attempts to join up or share data are stymied by legal or cultural factors - including both legitimate safeguards and unjustified hoarding.

  • Policies and services (for example AI chatbots) are sometimes commissioned on the erroneous belief that data quality and availability is better than it actually is.

  • The volumes of actively and passively generated data are rapidly expanding and overwhelming governments' ability to process this data.

  • A failure to understand how data driven decision-making, including the use of new AI systems, will interact with the messy world of people and politics.

Learning Outcome 3

By the end of Unit 5 students will be able to identify various types of data that modern governments collect and use. This includes:

  • Administrative data (i.e., government data collected during during routine government operations, e.g. service/program delivery" )

  • Actively collected data (i.e., survey data, tax forms, censuses)

  • Passively created data (i.e., traffic monitoring, CCTV, weather sensors)

  • Structured data

  • Unstructured data

Summary of Key Arguments in this Unit

Argument 1 - Governments have been using data to make decisions for a long time, but the current moment marks a difficult transition.

There is nothing new about governments collecting and using data. However there is a transition underway that is challenging for governments because of a) the sheer volume of data and b) the new requirements for digital data skills that are costly and difficult to procure and integrate. The current availability of AI systems such as ChatGPT creates new challenges as well as opportunities for data analysis.

These new digital era data skills are not developed as part of the standard statistical and economic training undertaken by the average public servant.

Argument 2 - The value of data as an asset to governments is growing.

When data usage by governments was simple, the additional value it gave public servants beyond 'governing by best guess' was modest. But as data on everything becomes more granular and more voluminous, and as the tools and systems that data flows through become more powerful, the opportunities to generate public value are greater than ever, including through new AI tools

Argument 3 - Government decisions are increasingly being made by and through automated systems that are based on data.

This can save costs and labour expense, but introduces the risk that decisions will be made that are harmful or discriminatory because the systems have not been carefully built, tested or monitored. Learning to do this is a new skill for most governments, and introduces new challenges for accountability. This is particularly important in the age of AI, in which automated systems are rapidly being adopted and used by public servants, sometimes without appropriate  reflection.

Argument 4 - Government is a producer of data that has value to businesses, citizens and civil society.

As we will talk about more in Unit 8, government data can create great value for entities other than itself.

Argument 5 - Geospatial technologies allow governments to make better quality choices.

However many governments struggle with accessing and deploying the skills and technologies required to make use of this new wave of tools.

Detailed Class Breakdowns

In this section we offer examples of different ways of teaching this unit.

Option A - Full Class Breakdown by David Eaves, Harvard Kennedy School - Includes Video

David teaches Unit 5 across one 90 minute class, plus a 90 minute guest lecture.

David teaches Unit 5

90 minute guest lecture by Dr Amen Ra Mashariki

Option B - Full Class Breakdown by Ines Mergel, University of Konstanz

Ines teaches Unit 5 in a single 90 minute class. Here is the detailed breakdown of that class.

Ines teaches Unit 5

Materials to Inspire Your Class Design

We recommend you read or watch the following before you design your own approach to teaching 'Unit 5'.

Read The Data Life Cycle, by Jeannette M. Wing

Watch 'What really is data science?' by Jonathan Ma

Check out the Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering (2024) by Qinghua Lu, Liming Zhu, Xiwei Xu, John Whittle, Didar Zowghi, Aurelie Jacquet

Suggested Pre-Reading for Students

Ten Great Ways Data Can Make Government Better (2017) by Janes Wiseman & Stephen Goldsmith.

Seeing like a State (1999 edition) [Part 1, Chapter 2, pp. 53-84], by James Scott

Using data to improve your service: an introduction (2016) Government Digital Service

The Parable of Google Flu: Traps in Big Data Analysis (2014) David Lazer et al.

Video: What is Machine Learning (apolitical)

Why we need inclusive data governance in the age of AI (2024) by Jeni Tennison

Digital public infrastructure: orientation matters (2024) by Soujanya Sridharan, Vinay Narayan and Jack Hardinges

Deeper Background Reading for You

💡 Case studies on problematic and unsuccessful uses of data will be published in the next unit

Readings on how the public sector should use data

The Value of Data (2019), Diane Coyle

The Path to Becoming a Data-Driven Public Sector, (2019), OECD

Case studies about successful uses of data in government

New York City is saving lives by predicting where fires will break out (2017), apolitical

Matching data to support troubled families (2018), the Local Government Association.

Parking open dataset used to find a signage failure (2016), Max Galka

'Clear My Record' by Code for America.

Readings on how to build and manage a team that has strong data skills

Managing a Data Science Team (2018), Angela Bassa

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

Historial and theoretical perspectives on data in governments

Ideological Inheritances in the Data Revolution (2016), by Hamish Robertson & Joanne Travaglia

How Data Does Political Things (2015), Blog Post by Jeffrey Alan Johnson for the LSE Impact Blog

Deep dives to develop data science skills (goes beyond the scope of this unit)

Full MOOC: Data Science Specialization (Johns Hopkins University)

Open Access Book: Big Data and Social Science (2016), Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter and Julia Lane

Full Syllabus: Data Science Project Scoping - a guide for social good organizations by University of Chicago and GobLab UAI

Governing in the age of Artificial intelligence

Strategically constructed narratives on artificial intelligence: What stories are told in governmental artificial intelligence policies? (2024), Ali A Guendez, Tobias Mettier

Real Time: Leveraging the Data Explosion for Better Policy (2024), Danielle Goldfarb

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