public sector with data science
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Emma Tomkins, Public Sector Specialist, SAS UK highlights instances where data science has improved citizen outcomes within the public and private sectors, here

There are myriad issues presenting themselves to UK Government departments at present, not least with budgetary concerns amid Brexit uncertainty. Together, the Government and wider public sector are under huge pressure to improve service efficiency and delivery for users expecting an increasingly joined-up, personalised and rapid service.

In that context, technology is often touted as a solution. But while digital transformation undeniably presents a goldmine of opportunity, it has also become an ongoing thorn in the side of the public sector. Data science and data analytics are two such examples of the potential solutions on offer, promising deeper insights and more intelligently-developed projects. Despite that, the public sector has a patchy reputation when it comes to technology investment. So what are the key challenges preventing more widespread adoption of these solutions?

To answer that, let’s investigate how data science is driving success in accelerating outcomes for the public sector and transforming services to citizens. And I’ll explore some of the practical challenges preventing investment in effective analytical solutions and how these barriers can be overcome.

From observations to actions

Accelerating citizen outcomes is no easy task. In order to achieve real results, a foundation of collaboration between data analytics and data science teams is essential, enabling them to derive meaningful insights for their organisation and drive more impactful decisions.

While the work that the two teams undertake differs considerably, in an effective deployment they should complement each other. Data analytics traditionally focuses on understanding what has happened in the past by identifying trends and patterns in historical data to help inform future decisions. Meanwhile, the work of data scientists leverages artificial intelligence and machine learning solutions to predict future outcomes.

Together, data analytics and data science serve to effectively sift through vast quantities of a variety of data – sometimes arriving at high velocity – to reveal valuable insights that drive better decision-making.

Real results: Data science in action

There is already an abundance of instances throughout the public sector where the value of data analytics is being proved. The police force, for example, is leveraging advanced analytics to enable its teams to cut through the noise and focus on real and emerging threats.

An example from the private sector is Rogers Communications which, with the help of machine learning solutions, is building models to predict the likelihood that a customer will promote their services to others. This is done with the application of analytics to voice calls and communication on social media channels, allowing Rogers to generate real-time insights on customer sentiment concerning the services on offer. As a result, agents are equipped with the necessary tools to determine the most appropriate response in each customer interaction, and in the last year, the project has reduced complaints by more than half (53 per cent).

Practical challenge: Bringing teams together

So there’s good evidence of how data science can yield significant outcomes in a timely way. With such impressive outcomes, it begs the question as to why there are so few cases of data science projects in the UK government and wider public sector?

One challenge centres on how teams work together. Here are some tips for cohesive collaboration between data analytics and data science teams.

Shift the focus from technology to outcomes. Data analytics tends to be focused on technical capabilities – databases, algorithms, APIs and so on – whereas data science is more closely aligned to service delivery. Developing a framework around the business value that can be obtained and showcasing the operational capabilities of data science can help refocus both teams around the importance of citizen outcomes.

Repurpose solutions without recoding. Teams often develop code in their preferred language. But this means their output doesn’t benefit as many people as you might like. Adopting a single governed environment can encourage collaboration while allowing data scientists and analysts to code in their language of choice.

Break down departmental data silos. Departments have built up their own systems over time, with different data sets stored in different places. This prolongs the process of collating and developing a holistic understanding of the digital journey of each individual citizen. A governed data science platform can sit on top of all these systems and quickly search across multiple data sets, enabling teams to collaborate and share data for mutual benefit.

Make it happen: Driving cultural change

Data analytics teams across government are all working to deliver positive public outcomes. Current examples have already shown that once a culture of evidence-based decision-making is established, organisations become better able to derive meaningful benefits from their data, confirming the value of analytics and spreading awareness of its utility.

Considering the large volume and variety of data which is arriving into the public sector at ever-greater velocity, further investment in data analytics and data science solutions is where the future lies. The age of data science will enhance collective efforts in the public sector, helping leaders focus on the areas which matter.

Data science can solve some of the biggest challenges facing society today and it is clear that the teams themselves have a role to play in overcoming some of the practical challenges preventing its implementation.

Find out more about how analytics can support the public sector.


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