A guide to not-for-profit data projects
Most not-for-profit organisations recognise the potential of data – public data, data they've collected, or data they could be collecting – but fewer know how to start putting this data to good use.
That’s where Data Projects from Go to Whoa! comes in. We produced this guide so you can get things rolling and ensure your data projects are as successful and sustainable as they can be.
In it, we distil lessons we've learned from guiding not-for-profit organisations through the process of becoming data-driven – and from Our Community’s own journey on this path.
You'll begin the step-by-step process by developing an understanding of data science and scoping projects, and learning how to ask the right questions. Next, you’ll learn to lay the foundations for project success, and make sure you've got the right tools and skills at hand. We’ll talk about deriving insights from data — and overcoming any obstacles that crop up. Finally, as you wrap up your project, we'll discuss how you can keep up your momentum and move forward strategically.
We suggest following the steps in order. Along the way, Our Community's Innovation Lab can provide free advice and support. We can also help build connections with other organisations who may have knowledge and resources (or even data) to share.
We hope Data Projects from Go to Whoa! proves useful in making your organisation data-driven.
Before you start any project – data related or not – it’s important to set realistic expectations and goals about what you hope to achieve. It’s key to ensure the right leadership is in place. We guide you through this with a five-point checklist to ensure your data project has the support it needs.
How often have you read that “big data is the new currency shaping our world” or similar? You might have read it on our front page. But what does it mean for your organisation?
Not-for-profit organisations can use the power of data to make more informed decisions. We’ve developed a framework – Developing data capability in your not-for-profit – as a starting point to help you identify the kinds of data your organisation may already be working with and what you can do with that data.
By now, you should have developed some understanding of what data science is and some of the ways it might help your organisation to achieve its goals.
You can now assess how you are using data already and where you would like to do more, using the framework Developing data capability in your not-for-profit as a guide. Once you’ve done that, you’ll be ready to develop ideas for a data project. In particular, you’re looking for places where data might improve efficiency or effectiveness in your organisation.
Armed with your project idea, you're ready to start distilling questions you'd like to answer.
In Tutorial 2, we show you how to frame your
questions in such a way that they can be effectively answered by data –
whether they’re questions about your fundraising, gaps in your services
or how to gather more accurate information about the people you serve.
We’ve also produced a companion worksheet with a worked example to get you thinking about questions that are answerable by data and that drive action.
What are your aims for the data project? How are you going to achieve those aims and in what timeframe? We’ve developed a handy project brief template for you to set these down, so you'll spend less time re-creating the wheel and more time focusing on your organisation’s needs.
Then come along to Tutorial 3, which solidifies the template’s concepts, touches on our risk log, parking lot and change log worksheets, and encourages you to think about co-design and centring
racial and gender equity throughout your project.
In Tutorial 4, we cover the various dimensions of data quality, the importance of recording good quality data, and how to educate staff and volunteers about data quality. These principles will enable you to begin identifying the potential strengths and weaknesses of the data available in your organisation.
By this stage, you’ll have a good idea of the scope and purpose of your project and a project brief. You'll also have some knowledge about data access and quality. In the next steps, you’ll learn how to identify the resources you'll need, how to recruit from outside your organisation (if necessary), and how to put together a team.
This step is where you should lay out your data project in concrete terms – that is, in terms of finances and timelines. This project budgeting help sheet is a good place to start. Feel free to contact us for further guidance and suggestions, such as how to budget for technical work.
Data project resources can include software (i.e. computer programs or tools), hardware (i.e. computing or storage machines) or wetware (i.e. the squishy organs in the heads of whoever works on your project). And with all three, the choices and decisions can be overwhelming.
We can provide recommendations for useful free or low-cost software and the equipment you may need. We can also discuss how to identify existing skills within your organisation and how to recruit from outside your organisation if required.
How do you go about putting together a data project squad? This step will vary depending on the size of your organisation and the skills and roles of the people working within it.
You might hire new talent, find skilled volunteers, employ a consultant, or upskill existing staff. Create your own job vacancy using our data analyst, data scientist and data engineer position description templates and use our non-disclosure agreement (NDA) template to ensure clarity about the responsibility of all parties handling confidential data.
The pins are aligned – time to bowl! Our help sheet has pointers to orient you at this critical point. At this stage you should revisit your project brief to check that you're on track and update any new aims or procedures. Know who your target audience is and be clear about what you want to achieve (your outputs) at the end of the project.
We mentioned earlier the importance of considering all stakeholders. You should keep diversity and inclusion at forefront of your mind and consider the communities you aim to serve throughout the duration of your data project. For more on this, see the external resources.
Successful data projects rely on diverse teams of people with a range of skills and knowledge. The rich contextual data abundant in the social sector makes this diversity particularly relevant for projects conducted within not-for-profit organisations.
Data experts and SMEs (subject matter experts who understand the data being analysed) must be able to communicate effectively with one another. Our help sheet outlines how to effectively manage communication throughout your project and achieve true collaboration.
You might come to a point where you realise that things are going to go wrong and that your project might not track exactly as you had initially planned. The very nature of data projects means that you can never predict exactly what insights the data you’re analysing are going to bring. Keep your expectations in check and remind yourself that the process is a learning experience.
We’ve compiled a list of common pitfalls, how to avoid them, and what to do when you encounter them. Get in touch with us for assistance about how to overcome roadblocks. Odds are, we’ve been there and can help guide you back onto the right track.
Even the most solid of project briefs can be brought undone by insights you didn’t know existed in your data. Data can be excavated endlessly, but if you want your project to lead to action – whether it’s raising more money or developing more efficient processes – you need to learn when to call it quits. As you near the end of your project, it’s a good idea to check back in with your stakeholders to ensure you’re on the same page about how your project is tracking.
Our blog post provides tips on how to know when you’re done, the importance of sticking to your initial questions, and why you need to have clear endpoints.
Congratulations, you’ve done the hard work! Now it’s time to communicate outputs and findings to the relevant stakeholders, including the data owners. This will ensure the results of the project can be put to good use. We also suggest enabling an open feedback mechanism to allow for suggestions and queries.
Now that you’ve completed your first data project, give yourself a pat on the back!
Then consider how to keep the momentum going: what did you learn, and how can you build on your project to improve the next one? Revisit Developing data capability in your not-for-profit – where does your organisation sit on the data capability pyramid now? It’s worth reflecting publicly so that other not-for-profit organisations can learn from your journey.
Lastly: get involved with the larger NFP data world! As part of our mission to boost the social sector’s data capacity, we’ve been fostering a community of socially minded data scientists. We held Melbourne’s first Datathon for Social Good and we run the Data for Social Good meetup. We’d love not-for-profit folks to take part and make connections.
All pathway resources
All our tutorials, templates, worksheets and publications in one easy-to-find place.
To register, see our Tutorials page. Here’s the suggested sequence:
- Tutorial 1: The why & why of data science
- Tutorial 2: Asking the right questions
- Tutorial 3: Initiating successful data projects
- Tutorial 4: Getting hands-on with data quality
- Five ways to ensure your data project has the support it needs (Step 1)
- Developing data capability in your not-for-profit (Step 2)
- What to consider when accessing data (Step 6) Coming soon
- Three things to keep in mind as you kick off your project (Step 10)
- Common pitfalls and what to do about them (Step 12) Coming soon
- Breaking up with your data project: knowing when you're done (Step 13) Coming soon
A regularly updated curation of useful and relevant resources to extend your understanding and guide your work.
- How the social sector can use natural language processing, SSIR/DataKind
- Decision-making in the age of AI, Nesta
- AI for everyone, Coursera
- Examples of what AI and machine learning can do, Google
- What is a data strategy?, Eliiza
- Data skills and capability framework, Department of Premier and Cabinet
- Toolkit for centering racial equity throughout data integration, UPenn
- Unpacking intersectional approaches to data, Global Partnership for Sustainable Development Data
- Data organization in spreadsheets, The American Statistician
- Ease-to-use web tools for beginners, DataBasic.io
- The data evolution: New tools to help organisations get more from their data, Nesta
- Learn-when-you-want Data Visualisation Training, Information is Beautiful
- Data Governance Toolkit, NetHope
- Data privacy assessment, Tech Impact
- Cyber Security Policy template, Our Community
- Damn Good Advice on Cyber Safety and Fraud Prevention, Our Community, in partnership with the Commonwealth Bank
- Cyber Security webinar, Our Community, in partnership with the Commonwealth Bank
Scrolled this far? Get in touch!
If you‘re thinking about setting out on this pathway, please do contact us
for an initial discussion – we’re keen to know who you are and what you’re hoping to achieve.
We’re striving to uplift the social sector’s data capability, so learning about your goals and challenges is important. Here’s an overview of what we’ve done so far as part of our mission.
Our data science resources and community-building activities are supported by Equity Trustees, with thanks to the Ella & Mitchell Brazier Fund, Charles Lamond Forrest Estate, James Raymond Hartley Charitable Trust, Charles Frederick William Taylor Estate and Truby & Florence Williams Charitable Trust.