Enhance your data and financial analysis capabilities by leveraging AI tools such as ChatGPT and Copilot. Learn effective strategies for finding, analyzing, and visualizing datasets to uncover valuable insights and trends.
Key Insights
- Source relevant datasets from internal company resources, Kaggle.com, or financial statements like 10-K and 10-Q reports for use with AI analysis tools.
- Conduct preliminary assessment to identify dataset formats, cleanliness, and structure, and leverage AI tools like ChatGPT or Copilot to assist in data cleaning and preparation.
- Combine targeted and open-ended analytical approaches—calculating specific metrics (e.g., sales figures) and allowing AI to suggest potentially unnoticed insights, trends, and visualizations to enrich your analytical portfolio.
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If you need to do data analysis or some sort of financial analysis, see how AI can help you in this process. The first challenge is to find some datasets you want to analyze. Of course, if you're working within a company and there's some data, that's ideal, so you can actually be running this on data that you're already familiar with, that you get from your company.
If you don't have a particular dataset available that you want to use, you could also head over to Kaggle.com, which has some crowdsourced datasets that you can download. Find something that you're interested in and want to do some analysis on. A lot of these are CSV files that you can use in ChatGPT or in Copilot. The other thing you could also do is find financial statements such as a 10-K or 10-Q, and you can get them from various websites.
Download those, and once you have decided on your dataset that you're going to work with, the first thing is you need to understand at least what's going on—like what kind of data you have, what content is in there, and what the structure is. For example, what kind of columns do you have if it's a CSV or Excel file or Google Sheet or something like that? What kind of data do you have, what format is it in, does it need to be cleaned up before you do anything, does ChatGPT or does Copilot have to help you with that cleanup, or are you going to do that cleanup? And think about at least three different analytical questions that you want to explore. If you have specific types of tasks that you regularly do in your job, think about those regular types of tasks and apply them to your dataset.
If you don't typically do this and you just want to explore doing data analysis, think about things that you'd be interested in knowing about the company or dataset that you're working with and ask it various questions. Or you can also say, with this dataset, ask ChatGPT or Copilot: what are some things that you think are worth analyzing? Do you see anything that's interesting in this data that you'd want to point out to me? You don't always have to go in knowing exactly what you want to look for, but I would say take both approaches. Go in looking for something specific—like talking about things like how much sales, or calculating something.
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So try to do some calculations and also ask it those open-ended questions like: do you see anything that's interesting in this data that I might not notice? Do you see any trends? Those can be some interesting insights that maybe you didn't even think about, but AI can find those interesting data points. And then you can ask ChatGPT or Copilot to generate charts or graphs or create summaries based on your questions. Then you can take all of those and put them together and highlight the key findings and insights, and that can be part of your AI portfolio.
