Season 2: Episode #11 | The Art of Asking the Right Questions in Data Analytics

🚀 The right questions lead to the right answers. In this episode, Vadym and Ievgen uncover the art of asking the right questions in data analytics—a crucial skill that can transform how businesses extract insights and make decisions.

🔍 What you’ll learn in this episode:
1️⃣ How to frame problem statements to uncover real business insights.
2️⃣ The role of cross-department collaboration in getting the full picture.
3️⃣ Why defining KPIs, ensuring data quality, and choosing the right reports is essential.

📊 Ready to move beyond surface-level analysis? Learn how to ask the right questions and unlock the full potential of your data.

➡️ Start analyzing smarter with OWOX BI

Podcast listing

Vadym:
Welcome back to The Data Crunch Podcast! I’m Vadym, Growth Marketing Manager at OWOX, and today, we’re talking about something that’s often overlooked but absolutely critical- effective questioning in data analysis.

Asking the right questions can completely change the outcome of an analysis, and if you don’t frame your questions well, you might miss valuable insights. To break this down, I’ve got our Head of Marketing, Ievgen, here with me.

Ievgen, where do we even begin with this topic?

Ievgen:
Hey, Vadym! This is such an important conversation because data will answer your questions - if you ask the right ones.

Too often, people jump straight into writing SQL to get reports, spreadsheets, and dashboards without thinking about why…  WHY they’re analyzing data in the first place. But real insights come from structured questioning. Before looking at a single number, you need to be clear on what you’re trying to achieve.

Vadym:
Before we get into the steps of crafting effective questions, let’s make sure everyone knows how to stay connected with us. 

If you're watching this on YouTube, don’t forget to hit that subscribe button and leave us a comment below. We love seeing your thoughts and answering any questions you might have about data analytics. 

For our listeners on other platforms, make sure to subscribe and turn on notifications—you don’t want to miss our weekly episodes, released every Thursday, where we dive deep into the nuts and bolts of data analytics. Your engagement helps us keep this content coming and reaching more data enthusiasts like you.

Now, Ievgen, what’s the first step in ensuring we're asking the right questions in data analytics?

Ievgen:
It all starts with defining the problem statement. Without a clear goal, you can end up lost in a sea of data.

For example, let’s say an e-commerce company wants to assess sales trends. Instead of asking, "What were our total sales last year?" a better question would be, "Did our electronics sales increase in March 2025, and what factors influenced this change?"

The second question sets a direction - it’s specific and actionable. That’s the difference between just looking at data and using data to solve a real business problem. Answer real business questions.

Vadym:
Got it. So, it looks like we need a well-defined problem statement before diving into data.

Ievgen:
That’s right! Once you know what you’re analyzing, the next step is to answer the question “Who has the right information? Who holds both knowledge about business objects and data”. You can’t get the full picture if you’re only looking at one department's data. Just Marketing or just Sales. Or just Finance. 

For example, if you’re investigating some sales, you’ll probably need input from:

• Sales or e-commerce teams - based on the type of business - they’ll have the raw sales numbers at the highest level of granularity. They know where that data is coming from, but also where that data is available, what are the right metric definitions, etc. 
• Marketing teams – They can tell you what campaigns are running, and what actions led to those sales. And they know where the marketing data is stored - whether is GA4 Export to BigQuery, some spreadsheets, databases, warehouses…
• Finance teams – They’ll show you the complete picture of completed orders, and real money in the bank account. Stripe account. Quickbooks or Xero. Plus, they know how those sales impacted the company revenue.

By talking to the right people from the right departments, you ensure that your analysis is well-rounded and not just based on one perspective or just the subset of data available across the business.
So this is all about who to extract the knowledge, and the context from, but also about where to get the right data. Which dataset or table to use, what’s the source of truth for that type of data. 

Vadym:
That makes a lot of sense - if you don’t have input from the right teams, your analysis might be incomplete. So, once we’ve gathered input from stakeholders, what’s next?

Ievgen:
This is where curiosity comes in. Good analysts don’t just take data at face value - they dig deeper.
For instance, if sales dropped, don’t just look at sales data. Ask:

• Did seasonality play a role?
• Did we invest less in ads last month? 
• What other things happened in our marketing?
• Did competitors offer better pricing?

One answer often leads to another, and asking follow-up questions helps build a complete picture.

Vadym:
That’s interesting - it’s about not stopping at the first answer but going deeper.

Ievgen:
Exactly. Once you know what you’re looking for, you need to ask yourself a question, “How to measure success?”. That’s where Key Performance Indicators (KPIs) come in.

For our electronics sales example, some useful KPIs could be:

• Total revenue from the electronics category
• Conversion rate from marketing campaigns
• Average order value for electronics buyers
• Customer Lifetime Value

Choosing the right KPIs keeps your analysis focused and ensures you’re tracking meaningful results instead of just looking at numbers without context.
And by the way, last year I recorded a nice video on how to define YOUR KPIs. Let’s leave the link in the description of this video.

Vadym:
Right, it’s about choosing metrics that actually reflect success. So, after defining KPIs, what comes next?

Ievgen:
Now, it’s time to answer the question “How to find the relevant data?”.
If you have a reporting system like OWOX BI - you’re good to go. Semantic layer, data modeling layer, conversational UI to get reports - you know where to get that source of truth. 

But if not, you just need to figure out how to get that data.
This involves checking existing sources – The data warehouses, transactional databases, CRM systems, Google Analytics 4, sales reports, or customer surveys. If more data is needed, I’d recommend going after the relevant stakeholders and asking them.

You’ll need to figure out not only where the data is stored, but also how you can get it, and what are the JOIN keys. That might be tough, you might want to start with some sort of VLOOKUP in just Google Sheets, or use SQL. That’s up to you, and based on the amount of data you need to merge to answer that business question from the very beginning.

A key part of this step is also validating data sources. If your data is not modeled yet, if it comes from different places, you need to ensure consistency. The last thing you want is numbers that don’t match across departments.

Vadym:
Right - bad data leads to bad decisions. Speaking of which, I think the next step should be the quality of data, am I right?

Ievgen:
Yes, “How to ensure data quality?” is a critical question to ask. Poor data quality means poor insights.  To maintain data quality, data analysts need to:

1. Filter out irrelevant data – Only use what directly supports the analysis.
2. Clean the data – Fix missing values, outdated entries, and duplicate records.
3. Ensure consistency – Standardize formats and calculations across sources.

Good data quality prevents misleading conclusions and makes insights more reliable, therefore - better and faster revenue-oriented decisions.

Vadym:
That’s a big one. Without clean data, the whole analysis falls apart. So now that we have the right data, what’s next?

Ievgen:
The next important thing to ask is, “What’s the level of report granularity we expected”.
Look, here at OWOX, we recognize the massive volumes of data businesses collect daily.

Unfortunately, much of this data is never utilized. This is largely due to the rigid and outdated nature of many traditional BI tools, which are not only hard to customize but often don't meet the dynamic needs of modern businesses. It just takes forever to build a dashboard… and then the end users still can’t directly iterate themselves.

That's why we envision a future where, so to say, “Playing with data” is accessible to every business professional through something very familiar. 

So spreadsheets are not just about simplicity; they represent the democratization of data analytics, allowing business users to engage deeply with data, beyond mere viewing, to perform complex analyses and make informed decisions quickly and efficiently.

Vadym:
That makes sense - you need the right tool for the job. But once we’ve analyzed the data, who actually uses it?

Ievgen:
This is key. Different audiences need different outputs.
The question here is, “Who are my end users? Who am I building a report for”

• Executives want high-level summaries.
• Sales and marketing teams need a bit more details.
• Finance teams care about revenue and cost impacts and live to dig really deep.

Knowing your audience ensures that insights are presented in a way that’s actually useful.

Vadym:
That’s a great point – analysis is only as good as how it’s communicated. Now, the data has to be presented to the stakeholders, right?

Ievgen:
Exactly, Vadym. One of my favorite questions is, “What are the right data visualization tools?”. First, do some spreadsheets with reports in a table format.
Google Sheets and Excel not only make data easy to access but also easy to understand. Data is only valuable if it’s easy to understand.

But then, spreadsheets offer easy-to-apply visualizations. That absolutely anyone can use to convey data insights effectively:

• Trends over time? Spreadsheets can generate dynamic line charts that update source data as your data grows.
• Comparing categories? Create intuitive bar charts directly within your spreadsheet.
• Analyzing market share? Pie charts in spreadsheets can be assembled quickly, providing a visual snapshot that’s immediately comprehensible.

Using spreadsheets for these visualizations means that every business professional, not just data experts, not just visualization experts, can extract, share, and act upon clear, actionable insights from the data. This accessibility helps ensure that decisions are based on a thorough understanding of the data, fostering better business outcomes.

Vadym:
So, to summarize – effective questioning in data analysis helps businesses avoid wasted effort and uncover real insights. Any final takeaways, Ievgen?

Ievgen:
Just this – data analytics isn’t just about looking at numbers; it’s about asking the right questions first. That’s what leads to smarter decisions.

Vadym:
Well said. Thanks, Ievgen! And for our listeners, if you want to take your data analysis to the next level, go to owox.com and check out OWOX BI. 

Also, feel free to explore our new semantic layer & data modeling tool within OWOX BI. It’s free now to start and incredibly useful for building all of the corporate reports. We empower businesses to make business data accessible to everyone. Check out the link in the description for more details and join our community of passionate data professionals.

And last but not least, don’t forget to subscribe to The Data Crunch Podcast for more insights!

Ievgen:
Thanks, Vadym, and thanks to everyone listening. Stay curious, ask the right questions, and we’ll see you in the next episode!