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.
A note on resources
The step-by-step pathway has been carefully designed, but we’re still developing the supporting material. Resources that we’ve planned but haven’t publicly released are bolded but not yet hyperlinked.
Step 1: Ensure Leadership is on Board
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.
Step 2: Understand Data Science and How it can be Applied
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.
Attend Tutorial 1 to solidify the framework’s concepts and get a practical introduction to data science. Once you’ve taken it all in, contact us for guidance in your project’s initial stage. We also encourage you to join our data-for-good Slack community for ongoing discussions between data practitioners inside and outside of not-for-profit organisations.
Step 3: Identify Areas of Opportunity in your Organisation
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.
To help you imagine possibilities, we’ve collated examples of other not-for-profits taking advantage of data, as well as tips on choosing a data science project for your organisation.
©Our Community Pty Ltd
Scoping the Project
Step 4: Ask the Right Questions
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.
Step 5: Write a Project Brief
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.
Step 6: Understand Data Access and Quality
To help you understand data access and quality, our help sheet walks you through the different steps you may need to take to access your data and how to overcome common obstacles you may face along the way. It also identifies the skills needed for your project based on the kind of data you have.
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.
Step 7: Plan your Data Project
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. One issue you might encounter is how to budget for technical work. Feel free to contact us for guidance and suggestions.
Step 8: Identify your Required Resources
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 provide recommendations for useful free or low-cost software and the equipment you may need in our software/hardware help sheet. Our wetware help sheet plots the skills needed for data projects, how to identify existing skills within your organisation and how to recruit from outside your organisation if required.
Step 9: Put Together your Team
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.
We’re developing a tutorial to guide you through each process, whether it’s hiring new talent, finding skilled volunteers, employing a consultant, or upskilling existing staff. Create your own job vacancy using our data analyst and data scientist position description templates and use our non-disclosure agreement (NDA) template to ensure clarity about the responsibility of all parties handling confidential data.
Diving into the Data
Step 10: Kick off your Project
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.
Step 11: Maintain Lines of Communication
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.
Step 12: When Things go Wrong
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.
Step 13: Getting to “done”
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.
Step 14: Communicate your Findings
Congratulations, you’ve done the hard work! Now it’s time to communicate outputs and findings to the relevant stakeholders, including the data owners. Enable an open feedback mechanism to allow for suggestions and queries.
Our five-point checklist guides you through this process. It also explains the difference between “interesting” and “useful” projects and why it matters, and how to ensure the results of your project are put to good use once everything is wrapped up.
Step 15: Reflect, Learn and Share
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. Our blog post outlines who we’ve worked with and examples of other organisations’ achievements – tell us if you’d like to be included!
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.
- Data capability self-assessment
- Reframing your data questions
- Managing project risks
- Using a parking lot for ideas
- Simple change management
- Project brief template
- Data analyst position description template
- Data scientist position description template
Understanding Data Science
- 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
Scoping and Recruitment
- Data skills and capability framework, Department of Premier and Cabinet
- Toolkit for centering racial equity throughout data integration, UPenn
Tools and Software
- 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
- List of useful Australian datasets, Funding Centre
- The Australian Data Archive, The Dataverse Project
- DSS Payment Demographic Data, Department of Social Services, data.gov.au
- Social Health Atlases, Public Health Information Development Unit (PHIDU), Torrens University Australia
Cybersecurity and Privacy
- 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