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

The National Audit Office (NAO) lately printed a guide for senior government leaders on bettering the use of data, which factors to endemic difficulties in reaching advantages from data sharing.

Variable data high quality is cited within the guide as a hindrance to efficient data use: “Data collected by one part of government may not be of sufficient quality to be used by a different part of government for a different purpose. [The] Government’s Data Quality Framework offers a more structured approach to improving the quality of data held by departments.”

The record of barriers to better data use in government cited by the spending watchdog is appreciable. Standards are laborious to implement as a result of, in accordance to the NAO: “The structure of government is heavily siloed and departments have a high degree of autonomy. Legacy systems make it difficult to introduce standards into this environment and government has struggled to make substantial progress over the past 20 or so years.”

Data analytics can also be depicted as insufficient to the dimensions of the issue: “Data analytics and tools work well with good-quality data, although effort is required to engineer the data when it comes from disparate sources. But there are situations where the accuracy and integrity of the data will make analytics difficult to apply, especially for personal data.”

The creation of cross-governmental datasets for a number of customers is sort of a non-starter, in accordance to the watchdog: “Merging personal data which does not easily match is difficult. Further questions arise around ownership, maintenance, funding, privacy, and the risks arising from data aggregation.”

The guide cites two classes of organisation that may act as beacons for government leaders. One is the Silicon Valley tech giants, the opposite is the monetary providers trade, which was compelled on to the trail of fine data government after the monetary crash of 2008, attributable to the systemic unhealthy practices of the sector itself.

It states: “Organisations that perceive and have succeeded in overcoming the data problem fall into considered one of two broad classes.

“Firstly, there are these that are designed and constructed for data exploitation from the outset and don’t carry the ‘baggage’ of legacy programs and methods of working. Examples embrace Google, Amazon and Netflix. As a outcome they’re naturally ready to exploit their data property and may readily benefit from enterprise intelligence, superior analytics and synthetic intelligence.

“Secondly, there are organisations with legacy systems which have been forced to address the data challenge in response to external events. For example, following the financial collapse of 2008, the financial services sector was subject to additional regulatory obligations.”

The report outlines a means ahead that consists of 4 components: embedding data requirements, taking a structured method, addressing legacy points and enabling data sharing.

“The Committee of Public Accounts has urged [the] Cabinet Office to identify and prioritise the top 10 data standards of benefit to government,” it notes.

The NAO welcomes the organising of a CDO Council in 2021, the creation of the Data Standards Authority in 2020, and the creation of a Data Architecture Design Authority, described as “a new body to review, approve and monitor adoption of data architecture principles and frameworks”.

In relation to resolving the legacy problem, the guide backs up the Committee of Public Accounts’ advice that the Cabinet Office and the Department for Digital, Culture, Media and Sport ought to determine the principle ageing IT programs that, if fastened, would permit government to use data better; and make sure that at any time when departments substitute or modify these programs it’s achieved with full consideration of how the programs will assist better use of data in government.

The guide’s advice on data sharing leans on the Open Data Institute’s Assessing risks when sharing data: a guide. It attracts consideration to its personal 2018 report on the Windrush scandal, “where the department concerned [the Home Office] shared data without fully assessing its quality with the potential for citizens being wrongly detained, removed or denied access to public services”, for example of how injury may very well be attributable to the imprudent sharing of government data.

The report concludes by reiterating a recognition that government data is a number one reason for inefficiencies, that underlying data points want to be fastened, that “focused effort, funding and prioritisation” is important for data administration in government, and that there’s a perennial hazard of initiatives tapering off within the face of adversity.

These suggestions appear broadly according to these made by Michael Gove, the instantly former secretary of state of the Department for Levelling Up, Housing and Communities.

The Scot’s enthusiasm for data is well-known, and featured in his notable Ditchley Park speech, given in July 2020. This postulated the leveraging of data analytics as a part of an agenda for a modernisation of the state.

In it, Gove stated: “Government needs to evaluate data more rigorously, and that means opening up data so others can judge the effectiveness of programmes as well. We need proper challenge from qualified outsiders. If government ensures its departments and agencies share and publish data far more, then data analytics specialists can help us more rigorously evaluate policy successes and delivery failures.”

The division he most lately led was behind the Levelling Up and Regeneration Bill, introduced within the Queen’s Speech in May, which incorporates proposals for digital planning powers to be given to native authorities in England and Wales, primarily based on open data.

Gove was, nevertheless, sacked by the prime minister, Boris Johnson, on 6 July 2022 for being a treacherous snake, regardless of being widely seen as the most effective minister in his high staff.

Johnson stays the prime minister, regardless of having resigned as chief of the ruling Conservative Party on 7 July, in the future after dismissing data evangelist Gove.

That is the ineluctable political context of the NAO’s Improving government data: a guide for senior leaders.