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Why Our #dataforgood Project Almost Didn’t Happen

We’re now over 12 months into our Data for Good project, and it’s been an incredible journey for us with an incredible charity. But it could so easily have been nothing more than a pipe dream.

Alistair Adam
|
29 April 2021

In November 2019, I attended a data science conference.

One of the speakers that day was from Rolls-Royce’s data science team. He spoke about a Data for Good project they were running, partnering with the Motor Neurone Disease Association and several global consultancies and tech providers. Their goal was to develop technology that would allow people with motor neurone disease to have a conversation in their own voice, even after they have lost the ability to speak.

It was inspiring. I loved the idea of using data science and technology to advance worthy causes.

I came back to the office, fired up to start our own Data for Good (DfG) project.

Getting buy-in was straightforward. The rest of the data science team were as excited as I was. And my boss liked it too. Beyond being a hugely rewarding thing to do, we rationalised that a DfG project wasn’t a one-way value exchange. Working with new technologies, on new problems, with new partners – those are valuable real-world learning opportunities. Win-win.

By the end of November, we made the decision to commit our data science team’s weekly training and development time.

We were on.

Then it hit me.

I had absolutely no idea where to start.

We’d never worked with a charity or social change organisation before. Do you just call up the switchboard? Who do you need to speak to? How will we find the right project – one that offers our team learning opportunities and delivers the client tangible value?

Thankfully, we got a big helping hand from The Data Lab and several other organisations. But, even with that help, it took over four months after that first moment of inspiration for us to properly get our #dataforgood journey underway.

So, I’m hoping that the advice we’re sharing below will help other data science teams that, like us, are inspired to take action but don’t know how to get started.

Disclaimer: this is how we approached it. If you’ve been on a similar journey and went about it a different way, or found other things that helped you get started, get in touch - I’d love to hear from you.

 

5 tips for starting a Data for Good project:

  1. Start with a cause your team is passionate about
  2. Get help finding the right partner
  3. Find a project that will be valuable for your partner and for the skills development of your team
  4. Make sure there’s a project sponsor ‘client-side’
  5. Think long-term and be realistic

1. Start with a cause you’re passionate about

As in the Rolls-Royce example above, you might already have an innovative idea that the world needs.

Our starting point wasn’t as clear-cut. We wanted to help, we just didn’t know exactly how.

So for us, the first step was to talk about the causes, movements and social change initiatives that, as a team, we really cared about.

We agreed to focus on social care.

Note: At this stage, you don’t need to get too specific about what the project itself will be. What’s really important is to choose an area that excites your team. Something that makes you eager to put in the effort, the hours, and that lights a fire to bring about positive results.

2. Get help finding the right partner

Once we’d agreed on social care in the broad sense, we had to find an organisation to partner with.

And this is where I’d strongly recommend bringing in outside help. There are just so many great causes out there, trying to find one by yourself can feel like looking for a needle in a haystack.

First, we spoke to The Data Lab in Scotland.

They put us in touch with the Coalition of Care and Support Providers in Scotland (CCPS) and the Scottish Council for Voluntary Organisations (SCVO).

The CCPS then connected us with Real Life Options, a UK-wide charity that supports people with learning disabilities, Autism and age-related needs to lead more independent lives.

From the first time we spoke to Real Life Options, we felt a strong cultural fit and they had a very specific, very costly problem, but they lacked the analytical resources to address it.

A few conversations later, and we were delighted when the team at Real Life Options agreed to partner with us.

3. Find a project that will be valuable for your partner and for the skills development of your team

One of the reasons Real Life Options felt like such a good fit for us was because we could see how our project would help address a big financial issue for them and let us apply our skills in analytics and data science to a new area.

Real Life Options is a charity that supports people living with disabilities. The charity has a large network of employees providing hands-on support to people living all around the UK. Like many organisations in the care sector, employee retention is an important topic. And one that’s become even more critical as a result of the pandemic. Training their staff is a big investment and, where they have staff leaving unexpectedly or after a short tenure, they often have to use expensive short-term contract staff and invest more in recruiting permanent replacements.

The goal of our DfG project with Real Life Options was to use analytics and predictive modelling to reduce employee attrition rates. By giving the charity insights around why employees are likely to leave, and a consistent method of identifying at-risk employees, they could then develop preventative strategies and targeted support frameworks.

We liked the project right away. The data modelling principles are the same as we use in the corporate world, only here we’re focusing on employee retention instead of customer retention.

 

4. Make sure there’s a project sponsor at your chosen partner

We definitely got lucky here, but we can’t overstate this point enough.

You need to have full buy-in from your partner. They need to believe in the project and share your determination to drive it forward. I’ve heard from former colleagues that without a vocal project sponsor client-side, these type of projects can fizzle out.

This is especially important if your chosen partner doesn’t have an in-house analytics or data science team. Whether it’s access to the right people, the right data, or someone to help champion the work you’re doing and make sure it gets used in the right way, we were extremely fortunate to have Paul Cusworth (Director of Digital and Enablement) fully involved from day one.

 

5. Be realistic about how quickly you can deliver results

This might not be true for every company, but for a company like Optima, we had to be realistic about how much time we could commit to the project, and what that meant for how quickly we could make progress. We put four data scientists on the project, each allocating their full training and development time to Real Life Options. Knowing this up-front meant we could give the charity a realistic idea of when they would start seeing results.

Even with multiple data scientists working on it, half a day each week still doesn’t allow you to make as much progress as you might like. Things will take longer to complete compared to paid-for client work where more resource is allocated. And again, that feeds into what makes the ideal project– you want to find projects that deliver a lot of value, but equally where the client is happy to work to flexible timescales.

And bear in mind that just because you’re working on a pro bono project it doesn’t mean there’s any less rigour or security required around data handling. DfG projects still require contracts, NDAs, data security protocols and so on, just as you would for any other client. All of this set-up, including getting access to the right data, takes time.

 

Closing Thoughts

We’ve learned a lot in the last twelve months and we’ve come a long way. We completed Phase 1 just before Christmas, and the question was never - will we do Phase 2? - it was just a question of when we could get back to it. Real Life Options has been the perfect partner, and we’re grateful to them for their continued support. And it’s rewarding to see our work bearing fruit and informing their strategies and decisions around such a central issue to their financial wellbeing.

So my enormous thanks go to Steph Wright at the Data Lab and Nancy Fancott at CCPS for being so helpful and generous with their time in guiding us on the all-important first steps in our Data for Good journey.

Get in touch to find out more about our DfG programme, or for advice on how to get started with your own.
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