Data analysis is at the heart of an ODA’s function, and hence experienced, skilled analysts and data scientists are essential. They are needed to draw meaningful insight from large, complicated datasets and provide actionable insights for a range of different audiences.
However, an ODA is never going to work just with a group of data scientists alone. To achieve data-enabled public service reform, organisations often need complementary skills such as legal expertise to create information sharing agreements; technical support to integrate IT systems; communications to ensure data is used in a transparent way that warrants public trust; a political liaison function to ensure each project has the backing and support of politicians; and project management resources to pull the whole thing together.
Providing and managing these resources is not always straightforward. With demand rising fast for data scientists across many sectors, many public sector organisations struggle to be able to offer competitive salaries. Time can also be a challenge. Existing data and IT staff may find it hard to prioritise supporting an ODA when they have heavy demands from their commitments to their own organisations. Bringing teams of experts sourced from multiple organisations into one place can present additional practical challenges.
Nesta’s view is that it is possible to address some of these staffing challenges and create a better, more open and more inclusive ODA model. This could be achieved by drawing on the strengths, skills and resources of many local groups and individuals, for example from businesses, universities, charities and civic hacker groups. Step four, of the six-step process for an Office of Data Analytics project described earlier, involved assessing which teams and organisations need to be involved to complete a particular data project. Wherever the people come from, it’s important that an ODA is able to assemble the right roles and competencies for any given data project.
Some of these roles are looked at in more detail in the report; in summary the key roles include:
- Project management - overseeing the design of the project and the coordination of all partners’ activities.
- User research - assessing the real needs of those whose work the data project is designed to enhance.
- Data science - collecting, cleaning, matching and analysing data to produce insights
- Technical - putting in place the necessary tools to upload, share and analyse the data and creating data products.
- Legal and information governance - ensuring that data is being used, shared and analysed legally and ethically.
- Implementation - the organisations or teams conducting the data-informed action.
- Data providers - organisations providing data to create the data product.
