As the National Data Strategy’s second birthday fast approaches, Civil Service World and Civica bring together a panel of experts for a progress report

In July, the National Audit Office (NAO) published a guide for senior leaders which described data as government's biggest asset but also a leading cause of inefficiency – providing a succinct summary of the gap between potential and reality.

The National Data Strategy (NDS) published in September 2020, is the latest in a long line of initiatives aimed at closing this gap. It contains five missions – one of which focuses on “transforming government’s use of data to drive efficiency and improve public services” – underpinned by four data pillars: foundations, availability, skills and responsible data.

None of these pillars presents a new area for government, and the NAO argues that it will take sustained effort to finally see real progress against any of them. To support this effort, Civil Service World  partnered with Civica to bring together experts from across government to consider how work around the National Data Strategy is progressing and where leaders can focus their efforts to drive further change.

Civica’s consultancy director, Mark Humphries, began the discussion by outlining why data professionals should view this as an important moment. Data literacy among civil service leaders has increased, he suggested, and there is consistency across government on the data issues which need to be addressed as well as the importance of doing so.

“We've had the National Data Strategy; various government departments have put out their own strategies, and we've moved very much into the execution phase now,” he said. “For those of us who have been working in the data management space, it's an exciting and slightly scary time because we have been banging the drum for many years about the importance of managing data well and making sure it's fit for purpose.

“Now the rest of the world, especially central government, is turning around to us and saying: ‘OK, we hear what you've been saying, show us what you can do. How do we sort our data out? How do we make it happen?”

Data foundations

The NDS describes this pillar as “ensuring data is fit for purpose”, and the roundtable showcased the many purposes for which data is used across government – from improving how major projects are delivered in the Ministry of Defence (MoD) to preventing and detecting fraud and supporting work to meet Net Zero targets.

There was general consensus that the NDS provides a useful starting point for departmental strategies across many of these areas. Several participants said that it had helped to steer or shape work, either in their own teams or across their departments, though they also suggested that more practical or detailed guidance would be helpful as work moves from planning to execution.

Zainab Zorokong, head of data analytics strategy at the MoD, suggested that more practical guidance would be helpful as the department thinks about how it can implement the strategy across its large and complex organisation. Rashmit Kalra, head of data governance and privacy at the Competition and Markets Authority, agreed that it would be helpful, and save duplicated effort, to have more guidance around implementation.

No-one said that their team did not have enough data, but participants noted that much of the data is far from fit-for-purpose. Chris Small, head of Heat Networks South at the Department for Business, Energy and Industrial Strategy (BEIS), expressed a challenge which was reflected across the board: “We hold an inordinate amount of data within government specifically for the purposes that we collected it, but it's very difficult to use it from a delivery perspective.”

This might be because of quality or lack of standardisation. For example, he said, a lack of consistency in how addresses are recorded makes it hard to match energy data with properties across the country, which would greatly help work to improve energy efficiency. Or it might be because data has been collected with only one narrow purpose in mind and cannot be adapted to others: in another area, Small noted, data collected five years ago for analytical purposes could not be used for delivery so that the department was having to pay to collect the data again in a usable format.

Graeme Thomson, head of fraud analytics and strategy in the Cabinet Office’s Counter Fraud Centre of Expertise, suggested that to improve the quality and use of data across government it will be essential to develop a “data mindset” so that officials think about the wider value and importance of data rather than just focusing on why it might be of use in their immediate role.

“When you want to understand data, it's not purely just the data that you need to run your policy, it's other broader elements of data available to you,” he said. “We need to develop a data mindset, so people understand how to use data, the quality of data and what it can mean to people.”

Data skills

Building data skills across many industries is another pillar of the NDS, and government has long recognised it faces a particular challenge in this area. Attendees at the round table agreed government needs more data skills but not only in specialist roles.

Brad Chew, product owner and data lead at BEIS said capability needed to be improved across the board. Having joined the civil service from the private sector, he noted that in his previous work “everyone was a lot more geared up on data –they knew where it was, why it had been collected; everyone was a lot more ready to use it and report on it.”

Thomson suggested that improving data understanding would also help to tackle challenges around data collection and quality. “If you don't know how to use something, you don't understand the importance of it,” he said, “[and] you don't put effort into collecting it or to capturing it or to maintaining the quality of it.”

Humphries agreed, wanting to create a culture where users would drive demand for better data. “Anyone who is in regular receipt of reports in Excel, or static reports printed out, should develop the reflex to challenge it and ask why they don’t have access to this data in a dynamic dashboard that is kept up to date,” he said. “If we develop that challenge, that will drive so many other good behaviours behind it.”

However, Small cautioned that there would be limits to how far government could match the complex skills needed in some policy areas. His own team, he said, was largely made up of people who had joined the civil service from the private sector and were fairly data savvy. “We can all run complex things in Excel, but it's not Excel that we need to be using,” he said.

He outlined the challenge for teams working on complex, long-term projects which require deep expertise and systems it would be impossible to build in-house. He suggested that government needs to be more flexible and creative in how it works with external partners in this area, rather than relying on short- and medium-term procurement cycles.

Data availability

Small’s example touched on how another key aspect of the NDS – ensuring data is “appropriately accessible” for use by businesses and organisations outside government. BEIS collects lots of data for analytical purposes, he said. “But what we don't do is open any of that up for practical, private-sector delivery. It's very difficult for the private sector to understand or use that national data set for delivering things on the ground because they simply cannot access it.”

There have been several recent attempts to encourage appropriate data sharing, including through the Digital Economy Act which created a legal framework allowing simpler sharing between public sector organisations, but many officials are still wary of allowing access to data. Humphries suggested that one way to address this is to think beyond simply giving a partner access to entire data sets. “There's more interest on the idea of leaving data where it is and making data either accessible through an API, a sort of data as a service, or data as a product,” he said.

In the field of fraud detection, for example, an official might simply need to know if a person or organisation appears on a risk register. “So, you can centralise your fraud data and build in a simple vetting API which checks a person and gives them a red or green light,” Humphries said. This model keeps the original data secure, protects the details in the data and fits neatly into digital services, he added.

Thomson, who worked on the counter fraud sections of the Digital Economy Act, said the new legal framework it offers has helped to make data sharing faster, and explained how his team is hoping to improve this further by developing standardised documents and templates which will help to speed data sharing agreements.

He also pointed to the importance of relationships as well as policy: “We've seen that once you get two organisations who become familiar with each other if they had to come back to share data the next time, it's a much more rapid progress.”

 Gavin Freeguard, an associate of the Institute for Government and special adviser at the Open Data Institute, added that building relationships across professions will also be important. “Being able to bring that legal and data protection expertise together with policymakers, and those involved in delivering policy, means that you've got access to all of the expertise that you need, but it may also help build a relationship with your data protection professionals, which is less confrontational,” he said.

Freeguard added: “I think there is still in large parts of government a feeling that Data Protection Officers will suddenly come and stop colleagues from doing something, whereas in reality, most DPOs want a more productive and constructive relationship." He also noted that work to put in place data sharing standards and agreements had paid off during the response to the Covid-19 pandemic. “A lot of the time, people were having to work incredibly hard to invent those things or reinvent those things in the heat of the moment,” he said.  “Where that documentation was already in place, it allowed people to move much more quickly in an emergency setting.”

Responsible data

The final pillar of the NDS is about ensuring safe data-use across industries and building public trust in the use of data. This topic emerged as participants discussed the legacy of Covid-19 on government’s data communities. Several agreed that the urgent pandemic-response work saw a greater engagement with data across government – the Cabinet Office’s Thomson described how it helped to “overcome inertia” around using data to tackle fraud, for example.

When considering what lessons could flow from this response, Freeguard and the MoD’s Zorokong, noted that officials must ensure they are considering how public perceptions may have changed during and after the pandemic response. Zorokong said people were more comfortable with data being handled or used in new ways to address a national crisis, but as organisations have moved back to business as usual, “people will value a different approach to the collection and the managing of their data” and this will require strong communications from policy and delivery officials.

Standard life

Crossing several of the pillars of the NDS is the issue of data standards – designed, among other things, to improve quality and interoperability of data. Yet while many data professionals agree that standards are a good thing, Humphries noted that there is a live discussion in the data community about what makes a good standard. “There are opportunities to look at other sectors, other industries and see what kind of standards applied to the car industry, the energy market, financial sector,” he said.

Freeguard argued that a key factor will be not just designing good standards but helping officials across government to “grasp the importance” of them. Data professionals may understand the potential to improve data use through common rules and formats, he said, “But how do you talk to the civil servant of who's filling in a spreadsheet and just saving a particular file format? How do you get across that way you put this number in this cell really matters?”

The answer, he suggested, is to draw out the “human, practical, tangible” aspects – if you do not follow the right format, someone down the line will be unable to do their job well.

Thomson challenged the assumption that standards were always positive, however, saying that “sometimes, standards are part of the problem.”

He explained that in government where most data is stored on legacy systems – themselves a major challenge for digital and data teams – the imposition of too many standards would create “a huge amount of work trying to realign legacy systems and redesign software.”

In the area of counter fraud, they have instead agreed common specifications for data sharing, he continued. “What that means is you can keep your own standard. You can have data on your legacy systems. But when we come to share data in the centre or between ourselves, we will use a specification,” Thomson said.

While Humphries agreed that standards across all government data would be “totally unrealistic”, he said that in areas where data collection or use is relatively stable standards will be vital to drive innovation.

“The emergence of standards is a sign of maturity in any technological revolution going all the way back to the Industrial Revolution,” he said. “The original railways all had different gauge tracks and eventually they standardise.”

With any revolution, he continued, there is a period of “wild and exciting” innovation where standards would constrict change. But once systems mature then a lack of standards can stifle innovation. Humphries added: “When I started out in technology, there were still different, competing networking standards. But once everybody standardised on TCP IP and other web protocols, that enabled a whole series of innovations on top of that.

This round table formed part of an ongoing programme of research around the NDS carried out by Civica, which also included a survey of more than 800 civil servants. The latest findings of this research are available in a white paper.

Download white paper now.

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