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Hi, and welcome to Try AI For Growth, a baby podcast out of Make Space for Growth. Here, I share short and maybe surprising stories of how I’ve used technology to tackle everyday challenges – at home, at work, in business. I’m your host, Sara Vicente Barreto, and today, I want to start uncovering how AI can help you analyse business data and uncover business insights. I have a feeling this topic will show up more often in the podcast.
When you’re running a business, data can quickly pile up. I have always been a data geek, and to find that there are ways to work through data even faster is exciting for me. Moreover, the ability to get insights, visualizations and even thoughtful questions from a “silent” partner is quite powerful, especially when one is a solopreneur. From financial reports to performance metrics, there’s a constant need to extract insights. But how do you find time to analyze it all, especially when resources are stretched thin? Sometimes, you don’t even know where to start.
In fact, for many leaders, data analysis feels time-consuming, but overlooking it means missing critical patterns that can guide decisions. That’s where AI has stepped in to save me time and provide clarity. Today, I’ll share how I’ve used AI to analyse different pieces of data for my NGO, from evaluating children’s health metrics, school drop-outs or pulling podcast data, all involving pulling data from large datasets.
Why Data Analysis Matters
Data can tell powerful stories. Whenever people have come to me with a story, I often translate it into what the numbers say. But if they come to me with numbers, I can’t help but make a story out of it. For me, they are inter-connected. Businesses that want to drive in a data-driven world need to i) understand which data matters for their business, ii) collect this data in a way that can be analyse and iii) not stop there, by analysing this data to drive business insights. But let’s be honest, sometimes we collect the data, but the time is just not there to go through it. Moreover, when running a business and attending to many demands, it is hard to make the time to sit down with the data and go through it. Even as a data geek I have struggled to find the time.
Not everyone has the time or expertise to dive deep into data. This is where AI can lend a hand. With the right tools, AI can sift through large amounts of information in seconds, highlighting patterns and anomalies that might otherwise go unnoticed.
Real-Life Examples
This year, I had to spend a lot of time thinking through the metrics for the NGO, the impact of the programs, translating KPIs into measurements of success (or not). But we have a lot of programs, a lot of metrics and not a lot of spare time. I needed to turn around the spreadsheets quickly and enhance the analysis to ensure the data meant something to our donours and, eventually drive funding. I also wanted to be able to analyse the data and challenge the programs impact so I could drive operations for this year.
In the non-profit world, just like in business, data insights can help secure funding, improve program outcomes and ensure transparency with stakeholders. What is there not to like?
Analysing Data with AI
One of our programs tracks the health and nutrition of children by regularly measuring their weight and height. We collect this data across multiple locations, but turning those numbers into meaningful reports was daunting. The data is collected monthly, but not always, the beneficiary is identified by gender and age.
I started by uploading the anonymised data into ChatGPT, asking it to highlight key trends—weight distribution, malnutrition indicators, and outliers that needed immediate attention. I refined my analysis to include age groups, longevity in the program and gender. It was helpful, but it felt short.
I then asked the AI to research OMS standards for child nutrition and provide me with an analysis of the deviation against these standards. That is when the data started to get interesting. Whilst it could not identify Mozambique specific data, it still identified global standards that allowed me a starting point. We know our children will more likely be starting (and ending) below OMS standards, so I needed to understand the evolution to really see if the programs were having an impact. So I requested an extra analysis and asked the AI to look into the standard deviation and how that changed over time. At the end, I asked for 2-3 chart suggestions of how to better depict the data and compared to the ones I used historically. Some I kept, some I left for future reference.
You may ask, but could you not do all that in excel? Yes, most likely I could. But it would take time to sort through the data that often comes in different spreadsheets, map it to different groups and ages each time I want to test a different hypothesis, research on the relevant benchmark data, apply more formulas for standard deviation calculations and evolution over time. Honestly, I would probably not have gotten to as much detail as I did.
To conclude, I drafted the data analysis to include in the donour report, asking the AI to poke inconsistencies and challenge assumptions.
The cherry on top of the cake? A few weeks later, working through another program, all I had to do was to go back to the same chat, input a new set of data and ask the AI to replicate the same analysis. Magic.
AI helped me spot patterns faster, cut the data into different segments easily, and compare with external benchmarks (and look for those benchmarks). This allowed me to focus on the interpretation and conclusions, the actions to take and the adjustments to make. I love my data. But I have to say I love it just a bit more now.
Connecting to the Business World
People often think the charity world is different from running a business. In fact, the difference is only in its purpose. For the last 20 years, we have applied business management best practices to the charity, and I believe that has made us different. It is no different as we have been experimenting with AI. But if you want a few ideas of how this could translate to your business?
- Do you have customer survey data that you keep collecting but never really analysing? Try and use AI to provide you with data visualization, provide data insights, go deeper into the analysis and suggest actions
- Do you have heaps of sales reports but haven’t had to time to sort through them? There are multiple visualisation tools out there, no doubt, but maybe you are a step behind and none of your data is integrated just yet. Try to upload anonymized sales data by product, client, or region, and ask for analysis on seasonality, product trends, or anomalies.
- Do you keep hearing about social media analytics and getting all these csv files that you never get to look at? Try working with Data Analyst. Only this week I downloaded the lifetime data of the Make Space for Growth podcast to drive quick data on more famous episodes, user locations and key platforms. Took about 1 minute to download and 30 seconds to get it into ChatGPT.
The AI Tools – Side Note
I have not used any special tools (yet). I started with data in plain old excel format and uploaded it to GPT. Depending on the type of data, anonymising records before submitting is important. What I did do differently is I used the Data Analyst GPT, a GPT agent that is specialized in data analysis, visualization and working with structured data. According to Data Analyst “itself”
“I excel at tasks involving spreadsheets, CSV files, datasets, and statistical analysis. My responses are more focused on data-driven insights and problem-solving in analytical contexts.”
Additional benefits include that it can:
- Run Python code directly, allowing it to perform data analysis, generate charts, and solve complex mathematical problems. General ChatGPT typically doesn’t have this live coding ability.
- Do Data Manipulation: it can read, clean, transform, and visualize datasets you upload, producing real analytical outputs (like summary statistics, graphs, and dataframes).
- It has Interactive Dataframes, showing you tables, filtering data, and allowing you to manipulate spreadsheets directly in the chat
- Provide Custom Visualizations: generating plots and charts on demand, making it easier to present data visually.
AI Lessons Learnt
As always, I learn each time I have an interaction with AI. Some lessons are not changing.
1. The Power is in the Prompt
Providing detailed context—like specifying the type of data and the insights I need—has been key to getting accurate results.
2: Ask for Visuals or Breakdown by Sections
I often ask AI to ‘summarize this data by location’ or ‘break this into three key findings.’ This makes the results clearer and easier to present.
3: Refining for Accuracy
The first result isn’t always perfect. I ask follow-up questions or add additional datasets until the results feel comprehensive. In data analysis, I find it even more relevant to break down the “discussion” into steps. Perhaps you want to start with getting key insights, then diving deeper into an insight, recut the data to different segments and then move to visualizations. I would not ask everything into one go until you feel like you have a defined set of analysis that you always run.
What Can You Do?
If you feel like you are collecting all this data but, in fact, you have not spend much time running through it other than to add up a few totals, perhaps this gets you started. Start small:
- Pick one aspect of your business —whether it’s sales, costs, or survey feedback
- Upload that data to and ask for patterns, highlights, key trends or actionable insights
- Experiment with refining your prompts until the insights match your needs and you have learned from the data.
- Add extra visualization prompts if you also need it for reporting to external stakeholders
Why don’t you take on a challenge for this week. Do you have a report in your inbox waiting to be analysed? Don’t over think it and ask AI to handle the first draft. It’s an easy way to experiment with data analysis without investing hours of manual work.
AI is transforming the way we analyze data, and I hope today’s episode inspires you to give it a try. If you’ve used AI for data analysis, I’d love to hear your experience. Share your stories or questions, and let’s keep learning together.
Thanks for joining me for this episode of Try AI for Growth. Don’t forget to subscribe, leave a review, and share your thoughts. Until next time, keep experimenting and keep having fun.
Photo by Lukas