Eight tips for overcoming common data challenges for funders
Our Data Champions programme brings funders together to collaborate and learn how to grow a data culture in their organisations. Having recently launched our third programme, facilitator Dirk Slater takes the opportunity to pause and reflect. In this blog, he highlights some of the common data challenges, and offers tips shared by the Data Champions for overcoming them.
As a network builder and facilitator, I’ve worked with many different people and organisations looking to improve how they collect, present and protect data. In my experience, I’ve seen the immense value of knowledge exchange within peer networks, and the insights that can come from collaborative learning.
360Giving’s Data Champions programme is a great example of a peer network of people working in funding organisations, who want to harness the power of data and use it to make better decisions. In this post, I’ll sum up some of the common insights and lessons that our Data Champions have learnt so far.
What is the Data Champions programme?
Our Data Champions programme brings funders together to collaborate and learn how to grow a data culture in their organisations. Over the six months, the participants take part in regular workshops and group calls covering a variety of topics including responsible data, data strategy and how to build a data culture.
In November 2020, we launched our third programme with 32 participants from a variety of funding organisations. With peer-learning at its heart, the programme provides a unique insight into the common data challenges grantmakers have and some thoughts to resolve them.
8 Insights from the Data Champions
Here are some of the insights that have come up over the course of the programmes from participants sharing their challenges and recommendations, and learning from each other.
Start by asking a question
Data projects are most effective when they are intentional and focused. Often, organisations flounder when deciding what data to collect and how it will be used, so starting with a question will help create focus.
Don’t always expect the question to be answered
Using data will likely lead to answers but it will also raise more questions beside the one you started with. As you get a better understanding of your organisation’s grantmaking impact, don’t be surprised if you have more questions which require more data to answer. Your data project workflows are more likely to be cyclical rather than linear.
Do modify and change questions as you go
Your understanding and needs behind the initial question will likely change as you collect and analyse data. You might realise that you are asking the wrong question. For instance, the question, “where are the gaps in the services that address deprivation in our community?” might be the starting point. Yet, if you are wanting to improve the effectiveness of your grantmaking, you might be better served by phrasing the question, “what are the drivers of deprivation in our community?”
This learning is also relevant for those data collection methods that use questions, such as surveys. Testing your questions on a small sample of people will help you understand if you are using the right language and asking it in the right way. Then you can adapt the questions to make sure you get the most useful and accurate data.
Data is a team sport
Everyone plays a role in a data project, particularly if it’s connected to understanding an organisation’s effectiveness. Individuals will play an incredibly diverse range of roles and many won’t have anything to do with spreadsheets, maths or statistics. The important thing is to be intentional when building your team and get everyone invested in the project. This means ensuring those involved understand how the project will benefit them and the organisation, and how they can contribute.
This includes those who you are collecting the data from so that, rather than it being an extractive process, the project can be an opportunity for everyone to learn something. For example, for grantees, an exercise on collecting data about what they did might not be as beneficial to them as collecting information about what they learned.
A data culture is a learning culture
Making effective social change is about understanding complex issues and how to bring about change for the better. Data plays a key role in learning about the issue and how to tackle it and so, when building a data culture in your organisation, you’re also building a learning culture.
Less data is more
We live in an information rich age, with data at our fingertips. Thanks to strides in technology, we have increased opportunities to collect data. However, that doesn’t mean we necessarily should. Storing, managing and analysing data takes time and resources. The more data you collect, the more complex it will be to manage and organise.
Data is power…
… and with power comes responsibility. You have a responsibility to protect the data you have collected. You also have a responsibility to inform those that you have collected data from, how that data will be used. (The topic for an upcoming Data Champions workshop is ‘Responsible data’, so we’ll be sure to share some insights after that for those who are interested.)
The learning continues
Bringing together a variety of grantmakers in the Data Champions programme creates a unique and valuable space for peer-learning and networking. Here, I’ve highlighted just a few of the learnings that were shared and were helpful for the participants up to this point. I’m sure there will be more.
After reading this, do you think you could improve how effectively your organisation works with data? Which of these learnings do you already do in your organisation? Are there any that you could suggest your organisation or team focuses on to enhance your work? It might be useful to discuss them with a colleague.
Dirk Slater is a facilitator and network builder. He has spent the last 25 years working in over 30 countries building the capacity of social change organisations to collect, present and protect data effectively. He is the co-editor of IFRC’s Data Playbook, a collection of social learning exercises designed to strengthen humanitarian efforts. Follow @FabRider on Twitter.