The Data Science Campus: data science for public good

The Data Science Campus vision is data science for public good. Our goals are to explore new data sources, perform cutting-edge research with new-generation tools and technology, and build data science capability across government. This will enable the UK to grasp the transformational opportunities offered by data science, inform our understanding of the UK, and support better decision-making.

Innovation Summary

Innovation Overview

Background Worldwide, the economy and society are changing rapidly. Research and statistics need to be able to continuously adapt in order to remain relevant, and to support decision-making for government, the private sector and citizens. To do this, statistics institutions need to move away from traditional survey and analytical approaches, and embrace the innovative exploration of new data sources and new techniques. To address this need, the Office for National Statistics (ONS) officially launched the Data Science Campus (the Campus) in March 2017.

Its role is to:

• lead innovative research projects, exploring new developments in data science techniques, such as neural networks, text mining, and image analysis, and data sources, for example social media, real-time data from administrative and management systems, satellite images, the Internet of Things

• build data science capability across government, through the creation of career pathways, developing training and trainers, collaboration and mentoring

• support the data science community across Government

• be recognized as the Government hub for data science, and as a world leader in data science for government.

This response focuses on an overview of the Campus’ innovation in research and working practices. Research projects The Campus project process is designed to foster innovation, and as such, is experimenting with a novel and unique way of working for ONS. Campus projects run for a maximum of 6 months, and must have a significant element of new data science. This could be the exploration of a new type of data, or the application of innovative machine learning techniques, or both. Work on the development of current ONS statistical publications, using existing data sources is explicitly excluded from Campus projects, as is involvement in the implementation of new statistics into regular production. This is to allow the Campus data scientists the freedom and flexibility to explore cutting edge ideas, outside the constraints of the statistical production timetable. It also allows for failure. The freedom to fail – in the sense of exploratory research producing a result which is not useful– is key to success. Without this freedom, research will be limited to safe options, and opportunities may be missed. Projects are run using an Agile approach, which allows them to be flexible to research outcomes, and, if necessary, to fail fast.

To lead these projects, the Campus has specifically recruited data scientists with excellent skills in data science, something which is rare – although growing – across the rest of ONS and government. Projects are run as close collaborations with a range of partners: policy-makers, to understand the big questions; other national statistical institutions, to share knowledge and experience; subject matter experts; and academic data scientists, to draw from their skills. Some examples of Campus projects are described below. They demonstrate how the Campus is improving and supplementing existing statistical outputs, and also addressing policy questions. Traditional techniques and data sources would have been inadequate to address these issues.

Projects so far include:

• a new indicator for the Natural Capital Account, using Google Street View images to map the urban forest, important in preventing flash floods, reducing air and noise pollution, and supporting native ecosystems

• a superfast GDP indicator, which takes the temperature of the UK’s economy faster than the official statistics, to inform economic policy in a timely way

• a tool for managing and exploring the data for monitoring the UK’s Sustainable Development Goals, in which there has been international interest

• analysis of ship transponder data to understand the pressure on UK ports in advance of the UK’s exit from the EU, working in collaboration with the Centre for Big Data Studies, Statistics Netherlands

• automated classification of the financial sector into sub-sectors by type of activity, to support the development of more granular financial statistics to inform financial policy, a priority for the Bank of England.

Other longer-term goals include: exploring the potential of visible, infra-red and LIDAR satellite images to improve or supplement our understanding of the UK; exploring the use of Blockchain in understanding supply chains and provenance; and understanding error and uncertainty in administrative data and big data.

Innovation Description

Innovation Development

Innovation Reflections

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Year: 2016
Level of government: National/Federal government


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