From question to insight: A day in the life of a data analyst
TL;DR: Let's be real: Data requests are never as clear as "please analyze X for Y reason." They're messy, urgent, and require lots of back-and-forth - just ask any analyst who's gotten a late-night Slack from their VP of Sales. While ticketing systems try to fix this, they ignore the reality that data analysis is inherently iterative and needs tools built for real-time collaboration.
The job of a data analyst and data scientist is usually pretty easy because you tend to get requests with plenty of lead time and the requests are always crystal clear: the person asking the questions explains exactly what they’re looking for, why they’re looking for that data and what they would do with the results. Said no data analyst ever…
What this usually looks like in reality is:
- You receive a Slack message at 9PM from your VP of sales that looks like something like “Hey, we have the board meeting coming up, and I need to figure out why we’re seeing a drop in conversion in EMEA.”
- You go back and forth trying to understand exactly what they’re asking, and you probably pull in some folks from rev ops or other analysts
- Once you understand the ask fully you go on your own to pull some data, you pull up your favorite IDE to whip up some python scripts and you dig around
- Finally, you think you have the answer or you need some feedback, you uploaded your results to Google Sheets, create a chart and message your VP in Slack
- They get back to you and tell you that it’s not quite right. You go back to your code and go through this process a few more times
This isn’t the fault of any one individual. It's not that the VP of Sales is ambiguous, nor is it about the analyst's comprehension. It’s simply because exploratory data analysis is an iterative, evolving process. It's about trying, failing, retrying, and refining.
Yet, the struggle is real. Analysts often find themselves using tools designed for engineers, not for them. The lack of streamlined tools can make showing and sharing their work a Herculean task.
Many companies, aiming to expedite this cycle, opt for a ticketing system. These systems typically enforce upfront requirements and guidelines on how to make clear requests. But in reality, how often is your VP of Sales or CEO really going to go file a ticket? And is it the analyst really going to take the time to go and correct the bosses behavior? Probably not…
However, we believe there’s a better way and this is why we’re created Fabi.ai. We believe the data analyst should easily be able to explore and iterate in real time with their business stakeholders and other data analysts when they need support. Because we understand that data analysis isn’t linear. It's a journey of discovery, filled with discussions, debates, and decisions.
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