UK government departments and the wider public sector are under huge pressure to improve service delivery and efficiency. We also know that investments in data analytics and data science play a key role in transforming services to help citizens. So what are the key challenges preventing more widespread adoption of these solutions?

This post looks at some examples of success achieved to date. And I'll also discuss how to overcome some of the practical challenges that still prevent investment in effective analytics solutions.

From observations to actions

Accelerating citizen outcomes requires increasing collaboration across data analytics and data science teams.

Data analytics traditionally focuses on understanding what has happened in the past by finding trends and patterns in historical data to inform future decisions. Data science focuses on the application of AI and machine learning to identify future outcomes.

Together, data analytics and data science can reveal valuable insights across the vast and expanding lake of public sector data.

Real results: Data science in action

Across the private sector, we’ve seen data science projects deliver impressive benefits. For example, machine learning has helped Rogers Communications build models that predict how likely customers will be to promote their services to others. By applying analytics to voice calls and social media communication, Rogers generates real-time insights on customer sentiment about its services. This helps determine the most appropriate response in each customer interaction, and in the last year, it’s reduced complaints by more than half (53 percent).

Practical challenge: Bringing teams together

So there’s good evidence of how data science can yield significant outcomes in a timely way. The challenge, however, often centres on how teams work together. Here are some tips for cohesive collaboration between your 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, etc. – 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.

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. Click To Tweet

Make it happen: Driving cultural change

Data analytics teams across government are all working to deliver positive public outcomes. The age of data science will enhance their collective efforts, helping public sector leaders focus on the areas which matter. Data science can solve some of the biggest challenges facing society today, provided teams overcome some of the practical challenges outlined above.

I’d welcome the opportunity to connect for a discussion on your data science strategy. You can reach me at emma.tomkins@sas.com.