Data science techniques at the heart of government transformation
Data literacy has a vital role to play in developing government policy and in delivering services that meet the needs of people across the UK.
However, if we are to get data science to become part of the everyday toolkit of government we need to ensure that the opportunities of using these techniques are understood and taken up by those outside of the data scientist community.
Therefore, we’re also undertaking work to help those who aren’t data scientists in government make the most of the opportunities that these new techniques have for the delivery of their services and the development of policy.
What we’re doing
As ever we’re starting with a discovery phase to understand the breadth of potential users, what they need to know about data science, and how they want to receive this information. This will help us to make sure that any products we develop are based on real user needs of people in government.
At the same time we’ll pull together potential content based on the work of data science specialists both here and across government. We want to identify the areas they believe will be most helpful to colleagues from different working backgrounds. By hearing from them and understanding how they use data science, we can start to plan our approach beyond specialist groups and into wider government.
Building on existing content and communities
The data science community globally already produces a huge range of blogs, online courses, and other resources where they share their knowledge and help each other improve their skills.
However, many of these are targeted at specialist users or people looking to become data scientists. We want to also include people who work with data scientists, or who have a high-level understanding of the opportunities presented by these new technologies.
We want to build much broader understanding of data science. We want to do this by using an open and community-based approach - providing ways of accessing knowledge and resources that have been created elsewhere. We also want to encourage those who aren’t so familiar with data science to incorporate the techniques into their work.
At the moment we’re open about the form of the final product, and the user research will help us get closer to understanding what might work best - it could be a digital service, in person training or something else.
We will blog more about our findings from this discovery stage in the next couple of months. In the meantime, if you have any examples of good practice, products or resources please do flag them to us: we’d be very keen to hear from you.