Transcript: Having Better Conversations about Data: Tips for Social Enterprises

Music: Welcome - Declan DP and Flying High - Declan DP

Judi Drown: Good afternoon, everyone. My name is Judi Drown, and I'm the Head of Policy at Social Enterprise Australia. These conversations are funded by the Commonwealth Department of Social Services. They aim to create a space for change-makers and supporters across the social enterprise community to share knowledge and experiences, strengthen connection and support collaboration across the social enterprise sector.

Today's session is "Having Better Conversations about Data", and it's brought to you by The Data Conversation. I have the pleasure of introducing and welcoming Ruth and Tara, who are running the session for us today. 

Before I hand over, I want to acknowledge the Country and the lands on which we are meeting. I'm on the beautiful lands of the Ngunnawal people. It's a gorgeous day today, perhaps a little windy, and I think Tara and Ruth are sharing the beautiful lands here as well. I want to pay my respects to Elders past, present and emerging and recognise their continuing connection to the waters, skies and lands that we have the privilege of living and working on. I also pay respects to Aboriginal and Torres Strait Islander participants who are joining today and recognise that your presence here holds over 65,000 years of systems thinking and relational care for people, place and planet. 

Now it's a pleasure to hand over to Ruth and Tara. Thank you. 

Ruth Pitt: Thanks Judy. Tara and I are both on Ngunnawal Country in Canberra. We also acknowledge Ngunnawal Elders, past and present and other Traditional Owners of the Canberra region. The Data Conversation has a remote team based all around Australia, and wherever we are, we acknowledge that we're on Aboriginal land. 

We'd like to say a big thank you to all of you who've made time in your busy social enterprise days to attend or to watch this webinar. We're a social enterprise ourselves, and we think data is a really important topic for social enterprises, because we are managing so much data and so many different types of data. Depending on your social enterprise model. You might have the business side data, like sales data or inventory data. You might have timesheet and employee data, and then you've also got your social good data. You might have information on participants that you're working with, or you might have impact data. That's data that helps you to identify, quantify and demonstrate the difference that you're making for people and planet. All of this data can help you to achieve your mission as a social enterprise.

Data can help you make more informed decisions. It can help you assess if you're on track to reach your goals, and it can help you to have good conversations. Whether that's with your social enterprise team, with potential funders or with the community that you're working in, data can help support you in having great conversations. 

What we'll be covering today is our five top tips for improving your data practise over time. 

Tara Spokes: We also want to acknowledge that all of you attending today will have your own experiences with data. If you're really new to data for social enterprises, we will cover a lot in this session. Grab a tip or two and start there. The recording and some materials will be posted to the Understorey website for you to come back to over time. If you've been in this area for a while, hopefully, there will be at least a useful tip or an interesting twist that's new for you. 

Our intention is that you will leave this session knowing a little bit more about data and feeling a little bit calmer or more focused on what you might do differently in your own social enterprise.

Before we go any further, we'd like to introduce ourselves. My name is Tara, and I'm the Founding Director of The Data Conversation. Before this, I've worked in research and teaching roles at university and also on a number of data projects at the Department of Social Services. 

At university, I was studying how the brain is involved in different aspects of human behaviour. I loved using data to better understand what we as humans do, how we interact with other humans and how we make decisions. 

When I moved to work in government, I really enjoyed supporting people to access and use their program data to inform operational decisions and reporting.

It was really exciting for me to see how we can use data in an everyday, practical way. 

Then in 2022, I set up The Data Conversation to help not-for-profits and social enterprises better access and use their data in a practical and useful way. I'll hand over to Ruth to introduce herself.

Ruth Pitt: Hi, I'm Ruth. Before I came to work with Tara, most of my career was in program evaluation or in impact measurement. One of the things I enjoy about this work is that the questions that we need to answer, if we're going to set up an impact measurement system, are the questions that help teams have some really thought-provoking and useful conversations.

In impact measurement, we ask a lot of questions about what does success look like. To answer that, we have to help social enterprises unpack and define what they're trying to achieve in a really specific and tangible way. We also help people answer questions like:

• What counts as evidence? 
• What's the role of stories versus numbers in understanding change? 
• What do people find meaningful? 
• What do they find credible? 
• Who gets to have a say in what we measure? 
• Who gets to choose what we value? 

When I started working in-house for not-for-profit organisations as an internal program evaluator. I found that we were spending more time talking about other kinds of questions. These are questions like program A collects data in Excel, program B has a file on a Google Doc, and program C is using a database. How do we pull this together in a consolidated report by tomorrow? Our dashboards and past reports just say how many participants are under 15. Now we have a new funder, and they want to know how many are under 12. What do we do now? Or even simple questions like, does anyone know where we put the surveys? 

While I still love impact management and evaluation, and I still enjoy supporting teams with these kinds of questions, at The Data Conversation, we also help with practical and logistical data wrangling issues. We advocate for funders, boards and management teams who are asking for impact data to acknowledge and fund the time and work that goes into data management tasks. 

As you've heard, Tara and I are nerds. We do love talking about data, and we see it as a really positive and exciting thing. We recognise that is not the case for everyone. We know that data has these negative connotations for some people.

We talk to people who feel really weighed down by their reporting requirements. We talk to people who feel uncomfortable talking about finances and financial data, or who feel overwhelmed with how to get started with impact measurement. 

Maybe you have a story. When people have a story they're telling themselves, like, I'm not a numbers person, I'm not a tech person. It's because those topics make them feel anxious. That's actually where we're going to start today. Our first tip is about dealing with that sense of overwhelm and anxiety. 

Our first tip is to think of it like planting a garden bed, not building a garden shed.

Okay, so what do I mean by that? Imagine you're going to build a shed. That's a project. It's got a beginning, a middle and an end, and then it's done. You're going to need the skills to build the shed, and if you get it wrong, your shed will fall over. 

If we think instead about planting a garden bed, it's cyclical. We can improve the soil each year. We have lots of opportunities to learn more about what works and what doesn't work for our garden. Sometimes we'll have to work really hard to get the seeds in the ground, but then it will just take time for things to grow, and we can't rush that. Sometimes plants don't make it, and that's okay. We'll plant something different next year. 

It's the same with data. You have to cultivate data use over time. Maybe there are skills that you need to learn. Maybe there are projects to invest in along the way. But overall, it will be about shifting the culture of your social enterprise towards a culture of data, and that cannot happen overnight. When you think about data. Instead of thinking about a rush to get a project done. I hope this analogy helps you feel a little bit calmer. Take a deep breath, it's going to be okay. 

I know that organisations feel pressured to provide data and improve practise, but if you try to approach data with a sense of curiosity and growth, rather than rush and anxiety, it'll be a much better process.

We liked the garden analogy, and so we're going to use it, possibly overuse it. It's going to come back up in our presentation. I'll hand over to Tara to give an example of changing practice from our own business.

Tara Spokes: As a consultancy social enterprise, we collect data on where our time goes, and our needs around this have evolved over time. Initially, we tracked our time against a few key categories like client projects, community initiatives and business work. We found that those big categories, like community initiatives wasn't informative enough, so we broke it down into lots of smaller categories. Things like the time that we were spending on preparing an activity, the time I was spending on doing the activity, and then the time I was spending on talking to other people about that activity. As our team grew, this level of detail was just too much, so we consolidated into a useful set of categories for where we are now. 

We also needed to see the data in different ways over time. Originally, we only needed the built-in dashboards that the software provided, and that was plenty. We've recently moved our timekeeping data into a Tableau dashboard, and now we can share that information in different ways across the team in a way that's useful for each work area. Even with all this work, we know that it will probably change again in the next couple of years. 

Our second top tip is to think through the whole data cycle. The data cycle is a common term, and you can see a number of versions of this on the Internet.

We'll walk you through the version that we work with. 

The planning stage is where you think about the questions you want to answer, the stories you want to tell and the decisions that you need to make. Then you work backwards to work out the data that you'll need to collect. For example, if you're a retail social enterprise and you're wondering what people like about shopping with you, what's drawing them to your business? You could answer those questions by collecting data from your customers on their experience, like a feedback survey. Maybe you get your data from your sales platform on repeat customers. 

If you're a social enterprise engaging with young people, and you want to tell government and funders about the challenges experienced by young people and the need for programs like yours. You might want to ask your participants about the challenges that they face and the experiences that they're having, and then collect stories about the change your program made in their lives.

Or if you're a recycling social enterprise and you want to make a decision about whether your new textile program needs changing. Do you need to move to a different region? Do you need growing storage space? You might want to track the amount of textiles you've diverted from landfill, the amount the program cost and the number of people signing up for your program.

All of these data points will help you to make your decision. There are other planning tools that can help identify the types of information you want to collect. Perhaps you have reporting requirements if you have funders. You can use those to plan out the data that you'll need to collect. If you want to be able to talk about your impact. Common tools you might use include theories of change, program logics, and outcome measurement frameworks. Other tools might also include your marketing strategy, a financial planning document or other business planning tools. 

Then, as with any planning activities, it's also a good idea to engage some of your key stakeholders, like staff or your community, in that planning stage. Including these different perspectives will lead to a more robust and useful data collection plan. 

Having a clear idea of those decisions, stories and questions you have can help you to prioritise the data that you're most interested in.

Ruth Pitt: To come back to our garden as promised. Think of your data like a garden. Think about the harvest before you plant. Imagine planting 100 tomato plants, and nobody in your family likes tomatoes. That's not a great use of your resources. Our planning stage is where we think about what vegetables we're going to want to harvest and make sure we're planting those seeds. Growing what we need, but also not growing anything that we're not going to be able to make use of.

Tara Spokes: The collecting stage is where you gather the data through surveys, forms, or information systems like a CRM. You might collect stories through interviews and focus groups. You might collect some data all the time as part of your day-to-day business, like your financial records or program delivery data.

You might also have some data that you collect in short bursts. For example, if you're running a survey or conducting a series of town hall sessions. You might also have less formal data collection methods, such as team reflections or your notes from events and meetings. These all count as data. 

As much as you can, try to reduce the barriers for people to enter the data. This can include making the collection process more user-friendly. Also keep in mind that it's easy to access. If the form or system where you want them to record their information is hard for them to find, open or get back to, that creates a barrier for them and reduces the chances of them entering the information that you're after.

If it's a system that you want your team to use for everyday record entry. Make the system easy to access on whatever device they're likely to have at hand. Build in the recording points to the team's normal workflow as much as possible. Try not to use too many different systems for them to log into and have open. 

Another source of friction can be the time that you're taking. We all know, people are often super busy and can be rushing to get things done. If we can narrow down our collection to the things that you're going to use. People can lose patience if the collection format feels like it's taking too long, and we've all been there. Reducing those barriers for the user can include both tech and time. Keep the tech simple to use, easy to find, and the length of time needed as small as possible.

The managing stage is where you review your data and make sure it's correct, clean and complete. Doing this work will mean it's in a more useful format, ready for the later stages. Wherever possible, have your data set up in a central and secure location. This provides a clear platform for you and your team to manage. You'll also be able to see the data that your enterprise holds without switching between silos or platforms. 

We recommend setting aside time to look at summaries of your data and try to spot any errors or issues. Catching and correcting these early can save heartache when you're at those later stages. Don't wait until you need to use it. Set aside time for this. The later you will thank you for it, we promise. 

Check your data. Check your data for correctness. For example, do you have lots of customers born in the year 2025? Unless you're working with babies, this might suggest there's a problem with your data. Do you have a count of recycled materials that just doesn't make sense? These sorts of errors might be occasional mistakes, but they might also suggest that your staff or your customers aren't clear about what you're asking for. Or they aren't able to or don't want to provide the information at the time that you're asking for it.

Check that your data is clean. One of the things you might look for is duplicate records, to merge them where you can. Look for if there's problems with consistency. For example, are some people entering text using abbreviations, and other people are spelling out the full labels? Cleaning up your records during this stage means they will display better for you when it comes time for analysing, sharing and using your data. 

Check that your data is complete. Here you're looking for information that's regularly missing in any field, and then of the missing data, is there something in common you can see? For example, is there one staff member who's not entering the customer's postcode? Or one business partner that never refers their clients to your program? Are there a few questions on your survey that nobody is answering? Missing data can help you spot where you might need to improve your messaging, your processes or even your business relationships.

This managed stage can seem tedious and boring, but the rest of your stages are easier if you do this stage well. You'll also have more confidence in your data and the stories you're telling if you do this stage well. 

Ruth Pitt: To come back to the garden again, it's like the weeding and the maintenance. Even if you've done a lot of work to set up a great low-maintenance garden, you'll still need to do some weeding and watering. Even if all you have is a few pot plants on the balcony, you still need to do some weeding and watering. 

It's about planning for that maintenance work, and that's something that can be missing. I've seen funding opportunities for community organisations to support them with data projects, but the funding assumes it's going to be like building a garden shed. You'll get the funding for the database, you build the database, and then you're done. That funding opportunity is a risk if you haven't factored in the future work that you'll need to do to; review the data, review the database, make sure your data is clean, make sure the database meets your current needs and keep everything useful. Data is never set and forget, and it's about planning accordingly.

Tara Spokes: The analysis stage is where you consider what your data is telling you. It might include statistical analysis, but more often it will be using basic counts or percentage figures from your financial or program data. If you've been collecting stories, you might be looking for common themes across those stories that you've heard.

This stage is often the fun part because you get to see what people are saying or how the program is going. We suggest you give yourself time to ask questions and think about what the data is showing you. Instead of just whipping up your numbers for your next annual board review, give yourself or your team some extra time to interrogate the data and think about the patterns or numbers you're seeing. Pulling together the same KPI numbers each year, because that's what the board has asked for, will tell you some things, but you'll miss so much of the information that you have in front of you.

Keep in mind the stories and questions you had when you were in the planning phase, but also be open to interesting new stories that the data you've collected is now telling you. The sharing stage is where you provide summaries of your data to your different audiences. You can do this by creating reports, presentations or fact sheets to share your findings with your communities, funders or other people in your organisation.

Keep in mind that you can use the same set of analyses to share in different ways with those different audiences. You might report to your funder in their portal, then use some of that data in a social media post to share with your community. 

When you share information, only share it in a way that doesn't identify the person behind it. You may have heard of de-identified data, and this often just means we are taking people's names and addresses out. However, if you're working with a smaller community, other information can be identifying too. For example, if you're telling the story of someone from a small town who has Huntington's disease and whose house burned down last week, people of that community will know exactly who that is without their name being mentioned. Whether you're sharing quotes, stories or numbers, be really careful about privacy.

The using stage is where you consider the stories from your data as part of your day-to-day business or wider strategic planning. It's one thing to be sharing your data, but are you actively using it for your own decision-making? To be using your own data means that you're making use of the insights that you have from your data to improve your services and business. They're helping you to achieve your goals. This might be using your financial inventory or program data for business decisions, program planning or even marketing and hiring operations. 

Examples of using your data might be when you are reviewing which parts of your program to continue or drop. You might look at the program data to see what the numbers show you about which parts are being taken up and which aren't. Do you need to add more sessions? Or move your resources from one work area to another? When you're thinking about spending money on marketing, you might look at which parts of the community are engaging with your program. That would be your client demographics, and compare that to who you want to be reaching.

One idea to consider is using multiple sources when you can. For example, when you're thinking about trying out a new service offering or closing one down. You might look at community feedback from a survey, as well as your financial costs and expected income. 

These are all examples of using your data in your day-to-day operations and making evidence-based decisions.

Then we come right back around the cycle to thinking about and planning for what you might do differently in your next cycle. 

Ruth Pitt: We presented this idea of data as a garden recently, and one of our audience members shared their take on it, what worked for them. Their take was that it's important to acknowledge there will be different seasons. We really liked that one and added it. 

We've got to acknowledge that there's spring planting time, there's summer harvesting time, and there are winter frosts. There will be busy times when you need to invest a lot. There are times when your system will tick along by itself. There'll be times when the ground is a bit bare, and it's not looking great. It's a seasonal process.

A lot of our data work does happen in a seasonal way. Maybe you are reporting to a funder every six months. Sometimes that data cycle is about picking one thing that will make things easier in six months. Our financial data happens on that financial year cycle. Maybe you do an annual impact report. It means that you'll go through the full data cycle every year, and you've got an opportunity to improve things each time. 

If we come back to the data cycle, there are a few different ways that you can use it. One is as a reflection tool. At each of these stages, think about what you're doing well and take some time to celebrate and acknowledge that. Sometimes we spend so much time thinking about where we want to get to, and we forget to celebrate the progress that we've made. Make sure we do this step of celebrating what you've achieved. 

You can also use this data cycle to identify where you're having challenges and where you'd like to improve. We think it's really important to normalise and acknowledge that everyone is learning, everyone is improving, and you are absolutely not alone if you're having data challenges. I think sometimes people see other social enterprises with a great dashboard or a nice fancy impact report and feel like they're a long way from that. That's like comparing your life to somebody else's Instagram reel. You don't know what kind of data challenges they're dealing with behind the scenes. Maybe they don't have data challenges anymore, but they've done a lot of hard work and investment along the way to get there. So start from where you're at now and improve from there. 

The other reason we want to normalise the challenges is that we want people to feel comfortable joining learning communities and asking peers for support to share those challenges. Or when it's the right time for your social enterprise to invest in whatever kind of data support you need. Just as you invest in advice from a lawyer or an accountant, you can ask people for help with your data. You can use the data cycle to try to work out what kind of support and advice would be most helpful for your social enterprise at this point in time. 

You could also use the data cycle as a planning tool to help you think through all the steps in the data cycle before you start something new. Sometimes I see organisations getting particularly excited about one phase and not investing in the others. One example of that I see is that people get very excited about surveys and put a lot of resources into designing the survey, putting it out and collecting that data. Sometimes they forget that someone's going to have to clean and manage that data, someone's going to have to analyse all the data, and someone's going to have to write it up or communicate it in some way. 

If we don't have a lot of resources, we don't want to put lots of our time and money into the survey stage and then not have any resources to do anything with it. It could be better to do a smaller survey that you can manage, analyse and share, rather than something big and complicated that doesn't go anywhere. 

Another example I see is organisations investing a lot into an impact measurement framework. An impact measurement framework is a great investment in your planning stage for impact measurement data, but that framework needs to be realistic about what's feasible for your implementation. Will you have the right tech for the manage stage? Do you have the right skills for the data analysis stage? And as we talked about before with the weeding and the maintenance, it's about having a plan. Maybe you invest this year in an impact measurement framework, and next year or in two years that's when you'll invest in the platform. It's about being strategic and careful with your resources. 

Finally, we think the data cycle can be a really useful tool if you're problem-solving in your organisation about why things aren't working. We encourage people to zoom back out and reflect on the whole cycle rather than focusing on one stage when things aren't working. An example is I was working with a small community organisation, and the manager said to me, look, my frontline workers are great. They're doing a great job. They're great with their clients, but they're just not filling in the data that we need. I've reminded them, and I've nagged them, and now I'm stuck. Can you help me with this challenge?

I said to them, okay, let's have a think about it. When do those frontline workers see that data? She said, oh no, you haven't understood. This is data for the funder. Every six months, I put the data into the portal for the funder. That's what it's for. We talked through this a bit more and unpacked the situation. It turns out that the frontline workers felt like they were sending data into a big black hole and nothing ever came back. Then every six months they'd get a reminder to keep sending data into the black hole. They couldn't see their data moving through that data cycle. 

Instead of working more on the collection phase by doing more reminders and more nagging, we focused on the use phase instead. That team was already meeting every two months, and had a big get-together every two months. We started bringing mini data reports to those meetings and looking at them together. That meant the whole team could see when there were gaps in the data, and the whole team got to learn from that data as well. They started seeing things like, okay, we didn't realise that many clients had this kind of problem. I knew my clients had those problems, but we didn't know that across the whole organisation we were seeing so many clients with that problem. That enabled them to design new programs and set things up. Once the frontline workers could see the benefits of the data, they had fewer problems at the collection phase.

Tara Spokes: That takes us to our third top tip, which is to add a spoonful of sugar for those who are finding data a bit uncomfortable or are not so interested. This could be the decision-makers of an enterprise, the staff that are entering the data, or the customers who are being asked to provide feedback.

The idea of this analogy, channelling Mary Poppins, is that adding a spoonful of sugar helps to make a task that is otherwise mundane or daunting more enjoyable. We can bring this idea into the different stages of the data cycle. 

In the planning stage if we include information that others want to be able to talk about. Do you have program or business partners, staff or clients that want a particular issue highlighted? If so, make room for collecting that thing in your data collection plan. It might be recording housing security of your staff if you're a work-integrated social enterprise. Or the origin of the recycling products that you're working with. Or the reason a client was refused.

At the share stage, sharing your data with the people who will be entering the information. Ruth has just given you a really great example of where engagement of frontline staff improved after they could see the data they were entering. Another example of this might be when you ask for customer feedback. You could add a paragraph in your next newsletter or put up a small poster next to your cash register outlining what people have said. 

At the use stage, use your data in ways that excite and inspire you. If you're a social enterprise founder or manager and you have not come from a business background. You might find the financial data difficult or overwhelming to use, or it might feel less important to you than your program data. As a social enterprise, your ability to sustain your program depends on your financial success. So, to motivate the tracking of your expenses and your income, you might use your financial data to track the proportion of your profits that are going back into those programs. 

Another motivation might be to use the data to make that person's day easier. For example, we've worked with a team moving their assessments from paper to digital entry, and now they can generate a report at the click of a button. That's something that used to take them hours. If you want customers to provide their information and set up an account with you. Let them know that they can have an easier purchase process if they have an account set up. I'm sure you'll come up with many other ways that you can make the data process more meaningful to your work and communities. 

Ruth Pitt: To come back to our data garden one more time. Our tip is to cultivate something for yourself. Even if it was supposed to be a veggie garden, and even if you don't have a lot of space. If you love flowers and your family loves flowers, planting a few flowers will make gardening more fun. Some of you are pleased to hear that this is our last data gardening analogy. 

Moving on to our fourth top tip. Adapt your language for your audience. Different audiences will be more relevant at different stages of the data cycle. At our planning stage, a lot of that will be internal to our social enterprise, and we will want to use language that inspires and motivates.

At the collection phase, we need to make sure we're using clear, everyday language that makes sense for the people we work with. 

Then at the sharing stage, maybe we need to think differently about what the right language is for our funders, but what's accessible and meaningful for the community that we're working in. To give a really simple example, maybe our social enterprise has, as part of its mission, to improve belonging and connection in our local community. In our collect phase in our survey, we might want to break that down into some more specific questions, like, did you meet someone new at this event? Or have you made friends through our program? Then when we're reporting back to government, our overall report might be using language more like social cohesion and social inclusion.

We can adapt the language that we're using for the right audience. Sometimes I feel like people get stuck with the language that doesn't make sense for them. To give a simple example, I was working in an organisation that ran programs for students. They were giving out surveys for those students, and it had basic demographic data. I was reviewing the surveys, and I was surprised to find that they had only male and female as the options for gender. I knew this organisation had some non-binary students, and I knew this organisation was working really hard to be supportive and inclusive and to build their capability to work with these students.

They've done all this work, but then their surveys just had male and female. I asked them, and they said, oh, well, we have to do it like that because we're reporting into a government portal, and the government portal only has male and female. I said, oh no, we can change that. At the collect phase, we can have the survey have male, female, non-binary, and the language, the options that make sense for our community. Then in the manage phase, we can map that back to the report. For example, we could have gender missing for the non-binary students so that their portal will accept it, or we could map them to male. 

I remember the people I was working with were shocked, and they're like, are we allowed to do that? Our data will be wrong. I said, well, the data's wrong anyway because we've advocated to this government funder to change those boxes. They're not acknowledging those students who don't fit into the boxes. We don't have to design our survey in the exact same wording and language as the reporting. 

I do want to acknowledge that sometimes we'll be using standardised and validated tools. There are things like the Personal Wellbeing Index, or the K10 is a measure of psychological wellbeing, and we might be using impact measurement tools like those. For those kinds of tools, it's really important to keep the wording exactly the same, but we can still think about appropriate language for all the other phases. When we're explaining the survey, when we're collecting consent, when we're reporting back to our community, we want to use the language that's meaningful to them. 

To our last top tip, and that's make the most of what you have. If we think through the data cycle, we can think about what we're already doing, what we already have and try and wring every last drop out of it. 

At the plan stage, you might already have a mission, vision, values and strategic plan. Those things are a great starting point for an impact measurement framework. So don't feel like you're starting from scratch if you've already got those things. 

At the collect phase, you might think about where you already collect data. Where are you already asking people for information? If you're collecting a customer feedback survey, one or two impact questions added can be really useful. 

Maybe it's at the share stage. I've seen organisations that have taken the compulsory, essential information they have to collect for their funders. Then, putting in a little extra resources, they've turned it into an infographic. They've shared that infographic on their website and in their social media. That's more appropriate for telling the community the story of what they're doing. They didn't have to add anything to their data collection and the data processes. They've just added something extra at the share stage, and that's made a huge difference. 

Finally, one of the ones we focused a lot on in this presentation is the use stage. Something you could add at a low cost is having a session every three months to look at your data as a team. If you're a solo social enterprise, a solopreneur, buy yourself a coffee once a quarter. Carve out that morning at the cafe to spend the time looking at the data. Ask yourself, what am I seeing here? What am I learning?

If that's all that you can add, it can be really helpful because you're getting through this full data cycle and making use of the data. 

Tara Spokes: To summarise the top tips: 

• Think of your data like you're planting a garden bed, not building a garden shed. It's going to take time, and you'll slowly make improvements and adjustments as you go. 
• Think through the whole data cycle. Thinking through all of those stages of the data cycle, and don't get caught up in just one or two of them.
• Add a spoonful of sugar. Bringing in something of interest can help motivate those who are finding data difficult or aren't interested. 
• Adapt your language for your audience. Make sure you're using appropriate language with each of your different audiences. 
• Make the most of what you already have. You'll likely have some key information that you can use in a slightly different way that will really add value to you or your community. 

Depending on your background, what we've covered today might feel like a lot. We hope you've picked up a few different ideas and tips that you can try in your own business. 

We're super lucky to have received support through SEDI, the Social Enterprise Development Initiative, for a learning community to support small and startup social enterprises with their data. Thanks, SEDI and Social Enterprise Australia. We aim to provide a supportive environment for small and startup social enterprises to talk through their data questions, their concerns, and pull together some practical tips for the issues that are raised.

We've got a little bit of time to talk through the questions. 

Ruth Pitt: We can start with the ones that were pre-submitted. The question was, are there any ideas or tips on how I can help neighbourhood centres improve their data practises?

Hopefully, you got some tips from this presentation, and we'd recommend starting with the data cycle. Think through that data cycle and where your priorities for improvement are. Don't try to do everything all at once. Think through where the biggest challenges are happening and start there. 

Another tip that I find helpful is to think about improving practise as behaviour change. There's a useful behaviour change model called COM-B, capability, opportunity, and motivation lead to behaviour change. If you're going to improve people's data practises, you want them to do things differently. You need to change their capability, which is their skills. You can change the opportunity. So do they have the time for these new things? Do they have the right environment? Do they have the tools? Do they have the support? Then do they have the motivation? Do they get why it's important? 

What I see happening sometimes is when it's their role to improve data practise, we can get a bit stuck on the sea. We can get stuck on the capability, and we're spending lots of time in training. Sometimes what you need is not training but making the system look different, making the tools more available, making the work environment more supportive, and making sure that people understand why it's important, so they've got that buy in. 

That was why so many of our tips you heard today were not about how to do something in Excel. It was about the mindset to approach it with, and ways to build it into what you're doing already. If you're trying to help another organisation improve data practise, that can be something to think about. That's just a starting point, the data cycle and then think about behaviour change.

Tara Spokes: There's another one that was just asked. What are some tips for smaller social enterprises? That is who we're here for in this particular community. 

Again, hopefully some of the tips from this presentation gave you some ideas, but if you're small, just start small with your data change. And focus, pull out one or two things that you can change in the time that you have available. Don't feel like you need to invest a whole lot upfront in these big tools and platforms that you can grow over time. 

Another really important thing, something that we try to do all the time with all of our clients, is make sure you start with what you already have. You are likely already using systems, platforms or software that you can use in a slightly different way or use more of than you already are. Rather than looking for something new and having another licence fee and so on to pay. For example, you may have been surprised that we use Tableau on our time tracking data. That might sound like overkill, but it's actually the software that we already have for our client work. Our team is already using it, so it was quite an easy option for us. 

We wouldn't recommend that people go out and get Tableau so that they can see their time tracking data. Hopefully that makes a bit of sense. 

Ruth Pitt: Yeah, we always start with if somebody's working in Google. If you're used to using Google Sheets, Google Forms, or Google Word Docs, there's a whole suite of materials there. If you're always working in Microsoft, then we'd probably start you in Microsoft. We use Notion to manage a lot of our internal work, and we use their forms and tools. Start with where you're at, start with what you've got and start small. 

Fantastic. I don't have any more questions in the chat, so I'll hand back to the Social Enterprise Australia team. I think you've got some data collection. 

Judi Drown: Thank you so much for that. I think that was fantastic. Yes, we do have some data collection to do. 

There will be a link to a survey up pretty soon. We'd encourage you to fill that in and woud love it if you could let us know your thoughts. We will use that data and share that data, to look at what we're going to do next, how we can support people through the SEDI learning communities and on Understorey.

I want to give a huge thanks to Ruth and Tara for that presentation. I am someone who finds data incredibly scary, and I will delay in any way possible from doing anything with data. I appreciate the chat that you had and those really practical tips. Love the analogy to the garden, but most of all, making it less scary. Making the whole idea of data friendlier and, in fact, enjoyable. Thank you so much for that. 

I want to thank the Department of Social Services. They provide the funding for putting these together through the SEDI program. 

Thanks to Social Enterprise Australia and the team of people behind the scenes, Sherryl, Bree, Caragh and Athanasia, for putting all the work in to get this done. 

Lastly, I encourage you all to check out Understorey and see what other learning communities are coming up.

Thank you, everyone for joining us.