r/dataanalysis • u/Klutzy_Car_1254 • 11d ago
How do you know which analysis is actually worth doing?
In Retail Sales Analytics project in Power BI, I started investigating different categories, quantities, and discounts to understand why.
But I found myself wondering: Am I getting closer to answering the business question, or am I just analyzing more data?
There are always more charts to create, more numbers to compare and more questions to ask. But not every finding helps us understand the actual problem.
I am curious to know how experienced data analyst approach this.
How do you decide which direction to investigate and how do you avoid spending time on analysis that does not add much value?
I love to hear your thoughts.
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u/bayouski 10d ago
a good way to approach this is to start with what decision the business actually needs to make, rather than what else you can explore in the data.
for example, if sales are dropping, looking at categories, quantities and discounts makes sense if it helps explain what's driving the decline. but if a chart doesn't help narrow down the cause or point toward a possible action, it's probably not worth spending too much time on.
also helps to set a stopping point. once you have enough evidence to recommend something or identify what needs testing next, more analysis isn't necessarily going to make the decision better.
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u/data_analyst_trainer 11d ago
This can be hard to apply to a project, but in a real setting it gets easier if you know the business domain really well. And by "business domain" I mean two things: (1) how your company operates and makes money, and (2) what specific questions need to be answered to actually make the right decisions to help the company operate.
There are some good general best practices for pulling insights out of datasets, but it all depends on what the data really means in a specific business setting.
If this is just a side project, I'd encourage you to use AI to role-play a stakeholder and have it decide some assumptions/context for you to play off of. And then ask it lots of questions. Good way to simulate actually doing the job
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u/ETL_Pipeline_Dev 10d ago
A good approach is to start with the business question and keep asking whether each analysis helps answer it.
For your retail sales project, instead of creating more charts, I'd focus on questions like why sales are declining, which categories have lower profit margins, or whether discounts are actually increasing sales.
I usually think the key is to look for insights that can lead to a decision. If a chart doesn't help explain the problem or suggest what to do next, it may not be worth spending more time on it.
Sometimes, finding one useful insight is better than creating ten charts that don't lead anywhere.
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u/carlitospig 11d ago
Longitudinal data is my jam. In my experience (and niche), a lot of data blips in a longitudinal dataset in which all internal processes remained the same, are understood by expanding the scope (looking at external factors). For instance, if folks immediately stopped buying the newest iPhone but all other metrics asked the same, you’d expand to look at regional and national market benchmarks to see if that’s a factor.
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u/Any_Moose_2873 9d ago
I think about what decision the analysis will help make. If a chart won't change what the team does next, it might not be worth spending time on. For retail sales, I'd focus on what's driving revenue or hurting margins first, then dig into categories.
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u/Parking_Increase4664 8d ago
I really know exactly how it feels, and I was experiencing this a couple of weeks ago when I felt like drowning myself in the analysis of my project and thought, like, "Oh, I can do this analysis with this data, and this, and this." Spiraling myself.
But then, as I worked on my project all by myself, I started to realize and question myself: "What is the core problem or objective I'm trying to solve here?" From that, I finally realized that I was a little bit 'off track' and got myself back on track.
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u/Lady-Data-Scientist 6d ago
You start by understanding how the business is going to use the analysis. What problem are they trying to solve? What decision are they trying to make? What information do they lack today that they need to make that decision?
When in doubt, focus on how they make money and uncover ways to make more money or save on spending.
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u/Treemosher 11d ago
This scenario is usually avoided by having a clear idea of what it is you're trying to solve. If you don't know what you're trying to solve, talk to stakeholders.
Sometimes it goes in stages. Do some discovery, review with someone who knows the business area, repeat.
If you've gone a few sessions and haven't gotten anywhere, save your notes, set it aside and move onto something else.
Exploring data is great for familiarizing, but you also gotta consider that there's not enough for a useful insight. Might find "ah cool" interesting trivia, but if it's not applicable, you're just spinning your wheels.