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Funders, do you need a data scientist?

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With funders sitting on a goldmine of data, more are asking how they can best use data and what skills they might need to do that. But do you need a data scientist, an analyst or an engineer?

In this guest post, Michael Jarvis, Executive Director at Transparency & Accountability Initiative, shares guidance for funding organisations looking to increase their data capabilities

Man's hands on a laptop
‘Do we need a data wrangler, an analyst or a data engineer?’ TAI’s new guidance is designed to help funders find out. Photo by Cytonn Photography on Unsplash

In the philanthropic sector, many grantee organisations rely on data to inform their programming and advocacy. No surprise then that funders involved in the Transparency and Accountability Initiative (TAI) collaborative – a global group of funders in transparency, accountability and participation –  have seen a trend among civil society (and government) partners to take on data experts with varying job titles and job descriptions. 

Finding this prompted us to dig deeper, and create guidance for building up data expertise in our June 2020 report, ‘Finding, building, and retaining data expertise in social accountability organisations’.

But what about the funder community themselves? Funders are rarely data specialists. Some may have in-house expert staff, because their programming promotes accurate data production and responsible data use. (This is certainly the case for some of TAI’s membership, such as Luminate and Hewlett Foundation.) However, they are most likely to be experts on data policy and regulation – and its application on issues they care about, be it privacy, gender equity or rooting out corruption – and are rarely data scientists. 

Funders sit on a goldmine of important data that can be mined for insight – not least data relating to funder portfolios: whom they fund, where they are, what they achieve. This can go beyond what is sitting in grants databases, to data locked up in grant reporting that is much more rarely mined for insight. 

The 360Giving community – and the open grants movement as a whole – are testament to the value of data funders hold being increasingly recognised, along with the need to unlock that value so all can benefit.

What data capacities do funders need?

It is important to think about what data capacities funders need and how best to build and access them. What works for civil society can be adapted to work for funders, too. For example, one of our members is in the process of hiring an information specialist and they are using the guidance to help frame the discussion around the most important attributes to go in the job description. They are asking: “Do we need a data wrangler, an analyst, a data engineer?” The guidance we have set out is designed to help them find out.

Understanding data roles: the differences between analysts, engineers and scientists

Even with all the trendy appeal of tapping big data, it is unlikely that a funding organisation will need a data scientist. The core advice that we heard, born of civil society experience, should also hold true for the funding community: start by defining what the organisation wants to solve with data, and then build organisation-wide data literacy so you’re able to make decisions on whether to recruit or outsource data expertise. 

We anticipate we will see more foundations employing people with first-hand experience working with data. Our hope is that they can act as a bridge between central functions and thematic programmes – being able to draw on what data gets collected across the funding organisation, and then how that can inform strategic funding choices and learning in specialist programmes. They should also help build institutional understanding of how funder information can be useful to others, and the value of publishing data in ways that are easy for others to engage with.

For a table showing the differences (in role, activities and knowledge competencies) between data wranglers, engineers, analysts and scientists, see the full guidance note.

Learning from the 360Giving community

We can certainly benefit from learning from what is happening across the 360Giving community. Initiatives such as the Data Champions programme are filling a real need. As the platform for sharing takes hold, perhaps those champions can adapt and share a set of insights tailored for funders. There would be no shortage of users.

Being a Data Champion is about learning how and when data can be used to make better decisions and improve the effectiveness of your grantmaking. Find out more on our Data Champions page.