Think Like a Senior Data Analyst
Read Time: 5 MinutesSometime around the 22nd month of being a Data Analyst, I was asked to help a colleague on their project. They happened to be working on an area I had extra knowledge of due to a prior position in the company so I gladly said yes. We worked with a grocery retailer and our work revolved around the products being sold and customer behaviour related to the products.
This was the first time I had helped someone else in a big way. Up to this point, I was always the one getting help, including from this same colleague at times.
It was also the first time I started to think like a Senior Data Analyst.
I’ve always modelled myself based on other people when in new situations. I find it’s an easy way to get into the right mindset without doing additional work. So when I was asked to help out, I went into the mindset that my manager seemed to go into when helping me. What happened next was as much a shock to me as it was to my colleague.
I started to think from a much higher view.
Previously, I’d be assigned a project and go into tunnel vision. My only goal was to complete the exact project that was asked of me as quickly and accurately as possible. I had blinders on for anything else going on around me.
But this time was different. In my “manager mindset”, I noticed a range of other factors that we needed to think about.
Looking at our proposed analysis, I wondered if this issue went deeper than we thought. Could it be that it wasn’t just this product having issues, but related products? Similar to the shift we’ve seen from traditional dairy products to alternatives, blaming milk alone would miss the real issue. We’d need to look at more than just the product line we were asked about, we’d need to look at trends across the whole category. This required a deeper dive into the category itself.
When we started digging into the analysis, I couldn’t help but wonder if the problem we were trying to solve extended to other categories. Maybe this wasn’t about a shift from traditional dairy to alternative items, but instead an entire plant-based change.
Would we see similar trends with meat alternatives that were fresh in the market? Could it even extend to non-food items? I started asking questions about whether or not we could generalize this to other, similar issues. If we had a framework to use then we could go about things quicker without sacrificing quality. Part of this thinking was about the products, but another piece was that we’d likely be asked to do more of this sort of analysis. Was this a truly unique analysis or could we generalize it without losing detail, allowing us to repurpose this for other products and categories?
That’s an important thought to have with every project. Data Analysts can often repurpose work for similar requests. Taking the time to see where else this would work is worthwhile.
It’s a multiplier.
If the first time took 40 hours and the second time only takes 5, you’ve got a free 35 hours back. That’s powerful and puts Data Analysts in a position where their work can truly look like magic - How did you do that so fast?!
We needed to consider future aspects early on too. I had held off on this in the past and was lucky my manager filled those gaps. But that help wouldn’t be around this time. I couldn’t fill those gaps in the same way, so we had to think ahead.
Was our solution going to solve the problem going forward or was it a bandaid solution? Did we need to have ongoing monitoring to be sure that we solved it? How would we communicate the project to ensure it had the outcome we wanted? What if we hit unexpected bumps in the road and needed to let the stakeholders know, how would we best do that?
Thinking of these at the start of the project instead of the end opens a world of options. We could bring in experts without rushing them for a same-day response. We could build a monitoring dashboard in parallel with the analysis, using mostly the same query. And most importantly, we could involve stakeholders from the start.
That last part is important, yet we all learn it the hard way. Bringing stakeholders in early makes them part of the project. It gives them some ownership and having them back your project makes everything easier. Instead of convincing them to adopt your actions, you can work WITH them to create even better actions. And when their boss objects, they go to battle for the project, leaving you to do the analyzing instead of the politicking.
Really what this style of thinking led me to is the understanding that a Senior DA takes time before jumping in. Where I would have previously been neck-deep in data right after getting a request as a junior, this seemed silly in my new mindset. I needed to think about the problem and if there were other, better solutions. I needed to thoroughly understand what was happening to give a good answer. And even if I didn’t change how my analysis would go, being armed with this extra information meant I could answer questions that weren’t asked… yet. Because there are always more questions. That’s another part of thinking like a Senior - what else could be answered with this data that would be useful?
Previously I wouldn’t have thought about this until I was asked, leaving me to come up with some vague answer I could add detail to later. Not this time. Instead, I wanted to have answers prepared. I wanted them to know that we were as invested as they were, so we added a section to the analysis just for these questions that might come up. We’d bring them up to show that we truly understood the issue.
I think part of what happened was that I felt the full responsibility for this analysis for the first time and didn’t think I had a safety net. I wanted to be sure I could do everything my manager normally did when I was presenting. Having answers to extra questions, giving context that may not be obvious, and coming with solutions that were the most likely to be adopted, not only those that were “optimal”.
So you don’t need to BE a Senior Data Analyst to THINK like one. You can do this from day 1.
Question assumptions.
Ask for more details and ensure you fully understand the implications.
Build solutions that can be easily adopted and frame them as such.
Because at the end of the day, an analysis that fails to create action is useless.
I don’t know about you but I hate feeling useless.
— — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — —
Whenever you’re ready, here are 2 ways I can help you:
1. Data Analyst Launchpad - My course on how to build a resume and cover letter that gets results. I share 7 years of data analyst experience, including interviewing and hiring for most of 2023.
2. A Coaching Call - If you’re struggling with applications or want to level up your skills, I’ll give you a plan to get there and the resources that I’ve used to get there myself. If you’re unsure if I can help, send me a message and I’ll provide any guidance I can right then and there.
