The people running the U.K. government's digital transformation programmes need to be more tech-savvy if they ever want to hit the ambitious targets set for public sector digital transformation.

This is according to a recent report published by the National Audit Office (NAO)—the U.K.’s independent public spending watchdog—which found most digital change decisions in government are made by "generalist leaders who lack the expertise to fully comprehend and tackle digital challenges."

It warned government IT projects suffer from a "specialist skills deficit"—only 4% of civil servants rated as digital professionals, compared with an industry average of between 8% and 12%.

In a statement, Gareth Davies, head of the NAO, said the government needs "stronger digital expertise and sustained support from senior departmental leaders" to meet its digital transformation ambitions.

After all, digital transformation programmes—especially those of the size and scale of government projects—never stand still. Nor do the technology, protocols, and processes required to implement them.

Take DataOps, for example, a relatively new term in the world of computing related to the issues involved in handling vast amounts of data.

DataOps is pivotal in digital transformation

A methodology with roots in software engineering, DataOps aims to integrate the work of development and operations teams by promoting a culture of collaboration. It's been developed to streamline the entire data pipeline, from data collection to analysis and everything in between.

And this is important for all organisations—not least governments. With the right data, departments can make better decisions, improve operations, and create more value for people looking to use services such as renewing a passport, filing a tax return, or purchasing a rod licence to go fishing.

However, if the data pipeline is slow, inefficient, or prone to errors—if it's based on a complex network of legacy systems and siloed data going back decades—then the overall experience is likely to reflect this.

This is where DataOps comes in and why it's important to have people within the government and the civil service who are fluent in the language of large-scale data projects.

Collaboration matters

One of the big plus points setting DataOps apart is it can help promote collaboration and communication between different data processing and analysis teams.

And this is important because government agencies must break down silos and encourage greater collaboration to gain a more comprehensive view of the data.

This approach—which can be worked at scale while ensuring data is handled securely and complies with relevant data protection regulations—is undoubtedly gaining traction.

But though the goal is simple, the actual data management process is far from easy. Most government departments use multiple databases to collect and store their information. Some of these databases might be on-premises, and others are likely to be in the cloud. There may also be hybrid IT environments (where some data is stored in both places), and this only adds to the complexity.

DataOps is currently a hot topic, and recruiting more computing and IT specialists into the public sector is essential. The stakes couldn't be higher.

According to the NAO report, central government departments are estimated to have spent an eye-watering £456 billion on the day-to-day running costs of public services, grants, and administration.

In a candid admission, it warns the government lacks data on the hidden costs of many of its existing services. This can include the additional business processes—often manual—required to compensate for missing functionality in the legacy systems.

At a time when budgets are being squeezed like never before, one of the outcomes of digital transformation and modernisation has to be getting a grip on this spending. By streamlining the entire data pipeline, DataOps creates a framework for this to happen—but it requires people who are sufficiently experienced and qualified to deliver on those promises.