Have you ever had the frustration of having to provide the same information about yourself to different government services? Have you ever had to make decisions without information about what does and doesn’t work? Data is fundamental to delivering public services, improving systems and processes, and supporting sound decisions – but accessing accurate data is […]
Posted on July 16, 2019 by Yvonne Gallagher
Have you ever had the frustration of having to provide the same information about yourself to different government services? Have you ever had to make decisions without information about what does and doesn’t work? Data is fundamental to delivering public services, improving systems and processes, and supporting sound decisions – but accessing accurate data is far from easy. Drawing from our recent report, Challenges in using data across government, I highlight here some of the difficulties, their implications and ways they can be addressed.
Very real problems arise when data is not adequate – for users of government services, the management of public sector organisations and in the development and delivery of government policies.
Case work level: Data needs to be accurate at individual level to make decisions about benefits entitlements, medical treatment, tax liabilities etc.
For example: Windrush – poor data about individuals’ UK residency status resulted in wrongful removals, detentions and sanctions on public services for people, including the Windrush migrants who came to the UK between 1948 and 1973.
Business management level: Managers need to be able to aggregate data across the organisation if they are to manage the organisation’s performance and make plans about where to allocate resources.
For example: Supporting disabled people to work – a lack of structured data meant the Department for Work & Pensions could not know if policies for supporting disabled people to work were applied consistently by jobcentres and it had limited ability to learn about what worked.
Policy development level: Decision managers need to be able to aggregate data from a wide range of sources to inform policy decisions.
For example: Transforming rehabilitation – Ministry of Justice (MoJ) lacked understanding of what worked in probation trusts and what the costs were before it replaced them with Community Rehabilitation Companies (CRCs) working under contract. After multiple CRCs failed the MoJ decided to terminate CRC contracts 14 months early.
Better decision making, improved efficiency, reduced fraud, collaboration, innovation, more tailored services, greater accountability: there are tremendous benefits to be achieved by better data and better use of it. But having, sharing and using data presents numerous challenges. Over the last 20 years various strategies and reports have highlighted the hurdles to overcome, yet many significant blockers and constraints remain unchanged. Why?
Challenges in using data across government
Challenges in using data across government is intended to help those dealing with data in government who are facing challenges such as the following.
The legacy environment and its issues are continually overlooked. A lot of data was captured 20, even 30, years ago. It is often out of date, inaccurate, and stored inconsistently across different systems that are hard to change – leading to poor quality and lacking integrity. It is not like new retail services where customers make sure they routinely keep their contact addresses and payment details up to date.
Technology alone isn’t a solution for inadequate data: Government has got used to systems requiring armies of people and burdensome processes to deal with data – and the full costs of this aren’t understood. New technologies, such as robotics and artificial intelligence, are seen as solutions. But the best system is only as useful as the accuracy and comprehensiveness of the data it uses; and better technology on top of poor data might just get you to the wrong answer more quickly.
No agreed cross-government standards. There are many views of what standards are, but even basic customer data standards don’t exist. We found more than 20 ways of identifying individuals and businesses across 10 departments and agencies – National Insurance, driver’s licence, unique taxpayer reference, passport number and 16 more. These have no standard format for recording data such as name, address and date of birth, resulting in the same person’s name being held differently, even within the same departments, and often with out-of-date addresses. None of this enables easy data sharing.
Silo working inhibits progress. Although the Digital Economy Act 2017 has made it easier to share data where appropriate, many people in government lack confidence in how to share it legally, especially post-GPDR (General Data Protection Regulation). People are used to working within departmental boundaries, and this is a hurdle to setting up and maintaining cross-departmental initiatives, especially when the benefits might be enjoyed by organisations other than the one investing in the data project.
There are practical steps that organisations, as well as government as a whole, can take to address these challenges.
Our report, including Appendix Four, details three key steps.
Have a clear understanding of what you are trying to achieve
- A clear strategy in place – What is the problem to solve? What blockers have prevented previous attempts from being successful? (Avoid having a technology solution looking for a problem.)
- Leadership and accountability – Understand how organisations use data, which ones will benefit most from change and identify those that will need greater support.
- Funding to make it possible – How can government as a whole get a good return on investment even though in the short term some departments will benefit more than others? E.g. HMRC’s sharing of Real Time Information (RTI) with DWP is an example of better use of data where both departments committed resources.
Have the infrastructure in place to make it work
- High-quality data – Identify key datasets; assess the state of the data and its fitness for purpose; identify what changes are needed, and when.
- Data standards to improve consistency – Our report recommends establishing a cross-government consensus on consistent data fields and standards (at least in all new systems).
- Systems and tools that talk to each other – e.g. Driver and Vehicle Licensing Agency re-uses photographs from passports (with the driver’s consent) for online applications.
Have the conditions in place to make it work
- Safeguarding data and securing public trust – When asking for data, understand the needs and limitations of those who are providing it.
- Legislation to enable change – Use the Digital Economy Act (DEA) codes of practice and review boards to develop familiarity and expertise.
- Skills and appetite for change – Putting in place clear guidance for the front line on sharing data appropriately and safely, will help to build a culture of using and reusing data efficiently and effectively.
Government recognises the value of using data more effectively, and the importance of ensuring security and public trust in how it is used. In early June the Department for Digital, Culture, Media and Sport started its public engagement towards developing a new 2020 Data Strategy. This Strategy offers a good opportunity to improve government’s use of data – but it will only work if the right processes, systems and conditions are put in place.
We welcome comment on this guidance or any other issue raised in this blog-post and invite you to contact us if you have any queries.
About the author: Yvonne Gallagher is NAO’s digital transformation expert, focused on assessing the value for money of the implementation of digital change programmes. Yvonne has over 25 years’ experience in IT, business change, digital services and cyber and information assurance, including as CIO in two government departments and senior roles in private sector organisations, including the Prudential and Network Rail.
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Tagged: Behaviour change Business operations Cross-government Customer service Digital transformation Forecasting Fraud and error Good practice principles Information management Information sharing Innovation IT Performance measurement Public sector reform Regulation & consumer protection Risk management
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