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NAO guide for senior government leaders flags barriers to raised data use

A National Audit Office guide for government chiefs on improving data use points to difficulties in achieving data sharing benefits and laments variability in cross-government data quality

Brian McKenna


Published: 05 Aug 2022 16: 34

The National Audit Office (NAO) recently published helpful information for senior government leaders on improving the usage of data, which points to endemic difficulties in achieving advantages from data sharing.

Variable data quality is cited in the guide as a hindrance to effective data use: Data collected by one section of government might not be of sufficient quality to be utilized by way of a different section of government for another purpose. [The] Governments Data Quality Framework supplies a more structured method of improving the standard of data held by departments.

The set of barriers to raised data used in government cited by the spending watchdog is considerable. Standards are hard to implement because, based on the NAO: The structure of government is heavily siloed and departments have a higher amount of autonomy. Legacy systems ensure it is difficult to introduce standards into this environment and government has struggled to create substantial progress in the last 20 roughly years.

Data analytics can be depicted as inadequate to the scale of the issue: Data analytics and tools work very well with good-quality data, although effort must engineer the info as it pertains from disparate sources. But you can find situations where in fact the accuracy and integrity of the info can make analytics difficult to use, specifically for personal data.

The creation of cross-governmental datasets for multiple users is nearly a non-starter, based on the watchdog: Merging personal data which will not easily match is difficult. Further questions arise around ownership, maintenance, funding, privacy, and the risks due to data aggregation.

The guide cites two types of organisation that may become beacons for government leaders. One may be the Silicon Valley tech giants, another may be the financial services industry, that was forced to the path of good data government following the financial crash of 2008, due to the systemic bad practices of the sector itself.

It states: Organisations that understand and also have succeeded in overcoming the info challenge belong to 1 of 2 broad categories.

Firstly, you can find those which were created and built for data exploitation from the outset and don’t carry the baggage of legacy systems and means of working. For example Google, Amazon and Netflix. Because of this they’re naturally in a position to exploit their data assets and may readily benefit from business intelligence, advanced analytics and artificial intelligence.

Secondly, you can find organisations with legacy systems which were forced to handle the info challenge in reaction to external events. For instance, following financial collapse of 2008, the financial services sector was at the mercy of additional regulatory obligations.

The report outlines a means forward that includes four elements: embedding data standards, going for a structured approach, addressing legacy issues and enabling data sharing.

The Committee of Public Accounts has urged [the] Cabinet Office to recognize and prioritise the very best 10 data standards of great benefit to government, it notes.

The NAO welcomes the establishing of a CDO Council in 2021, the creation of the info Standards Authority in 2020, and the creation of a Data Architecture Design Authority, referred to as a fresh body to examine, approve and monitor adoption of data architecture principles and frameworks.

With regards to resolving the legacy issue, the guide backs up the Committee of Public Accounts recommendation that the Cabinet Office and the Department for Digital, Culture, Media and Sport should identify the primary ageing IT systems that, if fixed, allows government to utilize data better; and make sure that whenever departments replace or modify these systems it really is finished with full consideration of the way the systems will support better usage of data in government.

The guides recommendation on data sharing leans on the Open Data Institutes Assessing risks when sharing data: helpful information. It draws focus on its 2018 report on the Windrush scandal, where in fact the department concerned [the Home Office] shared data without fully assessing its quality with the prospect of citizens being wrongly detained, removed or denied usage of public services, for example of how damage could possibly be due to the imprudent sharing of government data.

The report concludes by reiterating a recognition that government data is really a leading reason behind inefficiencies, that underlying data issues have to be fixed, that focused effort, funding and prioritisation is vital for data management in government, and that there exists a perennial threat of initiative petering out when confronted with adversity.

These recommendations seem broadly consistent with those created by Michael Gove, the immediately former secretary of state of the Department for Levelling Up, Housing and Communities.

The Scots enthusiasm for data established fact, and featured in his notableDitchley Park speech, given in July 2020. This postulated the leveraging of data analytics within plans for a modernisation of hawaii.

Inside it, Gove said: Government must evaluate data more rigorously, and which means checking data so others can judge the potency of programmes aswell. We are in need of proper challenge from qualified outsiders. If government ensures its departments and agencies share and publish data a lot more, then data analytics specialists might help us more rigorously evaluate policy successes and delivery failures.

The department he lately led was behind the Levelling Up and Regeneration Bill,announced in the Queens Speech in-may, which include proposals for digital planning powers to get to local authorities in England and Wales, predicated onopen data.

Gove was, however, sacked by the prime minister, Boris Johnson, on 6 July 2022 for being truly a treacherous snake, despite being widely viewed as the most efficient minister in his top team.

Johnson remains the prime minister, despite having resigned as leader of the ruling Conservative Party on 7 July, 1 day after dismissing data evangelist Gove.

This is the ineluctable political context of the NAOs Improving government data: helpful information for senior leaders.

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