As a Delivery Lead, an important part of my role is monitoring project health and spotting risks before they become issues. I’m supported in my role with access to a lot of data, quantitative and qualitative, and turning that data into insight quickly helps guide decisions and actions at the right moments in time. It also helps turn backwards-looking statistics into forward-looking planning, with higher degrees of confidence.
Recently, I’ve been experimenting with using Cursor as a lightweight business intelligence (BI) tool, allowing me to bypass the typical setup costs of a traditional BI tool to get fast, actionable visualizations and insights.
Turn Data into Insights.
Most project teams are constantly generating a wealth of project delivery data, including:
- Sprint metrics and velocity trends
- Work item status and cycle time
- Bugs, rework, and incidents
- Team health check and survey results
- Retrospective notes and qualitative feedback
Turning the “what” into the “so what?” isn’t always straightforward. Data is scattered across tools, analysis takes time, and building visualizations can feel like unnecessary overhead. Particularly when contending with time constraints, sometimes data can get collected, but not meaningfully used. Bridging the gap between data and insight is where Cursor has been especially helpful. Cursor can make it easier to explore data, generate visualizations, surface trends, and pinpoint variance.
Because Cursor supports fast iteration, it allows me to start with a question rather than a dashboard. I can explore the data, see what stands out, and drill down as I go. That flexibility makes it a good fit when speed matters and when the goal is learning rather than reporting.
Skip the BI Setup, and Explore Quickly and Iteratively.
The use cases that I’ve found most valuable so far have been deeper sprint analytics that help inform better future delivery projections (particularly when native project management tooling isn’t flexible or robust enough) and team health check analyses that help surface opportunities for improvement.
At a high level, my typical workflow looks like this:
- Prepare the raw data. This might be a CSV export, a table of sprint metrics, health check results, transcripts, or even copied notes from retros. This can be in multiple files, and can be inclusive of both quantitative and qualitative data.
- Ask Cursor to explore the data. I’ll prompt Cursor to create BI visualizations (like scatter plots, correlation regression lines, bar charts, heatmaps, etc) for me to review, analyze trends, and identify outliers.
- Generate summaries and hypotheses. I’m looking for signals in the data that suggest where I might want to dig further to understand or what I might want to guide a team discussion around.
For example, Cursor has been particularly useful for identifying outliers in health check responses (where the averages don’t always reflect everyone’s individual experiences) or in helping me spot correlations between planned timelines and actual timelines. These outputs help me decide “what’s next?”.
The biggest advantage of using Cursor this way is reduced friction. There’s no dashboard to design, no data model to perfect, and no tooling interface learning curve to slow things down.
Watch Out! Avoid the Pitfalls.
Used thoughtfully, Cursor is great at detecting patterns across delivery datasets, producing visualizations on demand, and supporting fast, iterative exploration. However, you still need to know what you’re doing and apply judgment to navigate Cursor BI outputs effectively. Here’s my list of what to watch for:
Do not run/install things you don’t understand.
Cursor will help you quickly set up an analysis environment, but speed without understanding could be risky. In my case, Cursor guided me through setup with Python, pandas, NumPy, Matplotlib/Seaborn, SciPy. I wouldn’t have “approved” Cursor installing these libraries if I didn’t already understand their relevance and how they would be used. (From my data analytics days!)
Clean raw data is important.
If you have a clear idea of what output you want to create, or what questions you want to answer, it is helpful to structure your input data with the end goal in mind. Messy data (duplicate or missing rows, nonsensical values, blank fields, etc) will lead to potentially inaccurate insights.
Validation of the outputs is critical.
Cursor can make mistakes, or perhaps more fairly, interpret your prompting a slightly different way than intended. It might make assumptions that perhaps aren’t correct. I found a few instances of the output data values not being correct through my own validation. That being said, when I pointed out what was incorrect and what I expected instead, Cursor was pretty effective at debugging itself.
The more you iterate, the messier the project will get.
This is why vibe coding doesn’t get you to production-ready software (as of yet, anyways!). Be mindful of over-iteration within a project. This is also where intentional thinking upfront can help you avoid too much “slop”. Maybe the destination is more important than a long, meandering journey here.
Turn Insights into Action and Better Delivery Planning.
The biggest value I’ve seen from using Cursor this way isn’t necessarily in prettier charts or faster analysis. It’s in better delivery planning and team conversations.
Visuals and synthesized insights make patterns visible, reduce “gut feel” debates, and shift discussions from whether something is happening, to why and what should we do about it. Quick BI outputs become signals and conversation starters, tools for risk detection and course correction when needed. Applying statistics to planning can add confidence to roadmaps. Happy analyzing!