Season 2: Episode #5 | Common Mistakes in Data Analysis and How to Avoid Them

🚀 Even the best analysts make mistakes – but some can cost businesses serious time and money. In this episode, Vadym and Ievgen break down the most common data analysis pitfalls and how to avoid them.

🔍 What you’ll learn in this episode:

1️⃣ Why jumping into data without clear goals leads to misleading insights.

2️⃣ How bad data quality, wrong metrics and cherry-picking distort decision-making.

3️⃣ The dangers of misinterpreting correlation, ignoring outliers, and using flawed benchmarks.

Podcast listing

Vadym:
Welcome back to The Data Crunch Podcast! I’m Vadym, Growth Marketing Manager here at OWOX, and today, we’re tackling a crucial topic: common mistakes in data analysis and how to avoid them.

Businesses rely heavily on data to make decisions, but what happens when the analysis itself is flawed? That’s what we’re diving into today.

As always, I’m joined by Ievgen, the Head of Marketing at OWOX, to help us understand this. Hey, Ievgen, are you ready to explore some data pitfalls?

Ievgen:
Hey Vadym, absolutely! Data analysis is powerful, but only if done right. Even experienced analysts make mistakes - sometimes small, sometimes massive. These errors can lead to bad business decisions, and wrong decisions, right? wasted resources, and more importantly, lost revenue.

But the good news is that Most mistakes can be avoided by following best practices. Today, I want to discuss the most common pitfalls and how businesses can improve their data strategy, their data processes, frameworks, and tools to extract real, actionable insights and minimize the number of mistakes.

Vadym:
That’s what we’re here for! Let’s start with the first mistake—what’s the number one, the biggest way data analysis can go wrong?

Ievgen: 

One of the most fundamental mistakes is jumping into data analysis without clear goals. Without a defined objective, you’re digging through numbers without knowing what you’re looking for.
Like which questions should business ask to reach those goals.
Which metrics should you measure to answer those questions? To reach the business goals 

For example, a SaaS company might want to improve user onboarding.
Instead of pulling random data points, they should first:

• Define their exact pain points in the onboarding process. Like, map out the funnel. Understand if there is actually a funnel. 
• Identify metrics that measure success - like user drop-off rates at each stage of that funnel.
• Most likely it’s now time to add some measurement to that funnel, because without the process I’ve just described, there is a huge change if the tracking was not in place.
• And only now, it’s time to use that data collected to build a report, to visualize the steps, the drop-off rates, put all the metrics down, add some cohorts, and then - optimize onboarding steps and improve user experience.

So without clear goals, data analytics turns into guesswork, leading to confusion instead of insights. Like you should understand what you’re analyzing and what the goals are.

Vadym: 

That’s a great point. I think a lot of businesses make the mistake of collecting data for the sake of it instead of asking: What are we trying to achieve? Without that direction, you’re just staring at numbers with no real plan.

Ievgen:
Exactly. And even when businesses do set goals, they sometimes fail at another critical step: using unprocessed, messy, basically just raw data.

So raw data often has errors, duplicates, missing values, or inconsistencies - that happens. And that’s ok. But if you don’t clean it properly, your analysis is doomed before it even starts.

So best practices for transforming raw data into what we call - business-ready data include:

1. Standardizing formats (date formats, currency, units, etc.
2. Removing duplicates to prevent overcounting
3. Storing data in a data warehouse
4. Modeling that data using the tools like OWOX. So each of the data pieces, each of the tables doesn’t live disconnected from each other. We envision business-ready data as a database with tables, or data marts, that look like real-life objects like Users, Clients, Visitors, Products, Orders, AdPlatforms, and Website Sessions. So those separate data marts, or objects tables, are linked one to another.
   And the analyst that prepares those tables should also specify the JOIN keys between those objects and the rules - like 1 to N, N to N, what the keys, how to map it all together into a BUSINESS DATA MODEL.

Vadym: 

That’s huge. You might be looking at incorrect revenue numbers or missing customer details - and you wouldn’t even know it!

Modeling your data might take extra effort upfront, but it saves time and prevents bad decisions in the long run.

Ievgen: 

Absolutely.
When your data is modeled, even a newbie can build reports based on the predefined structure of tables, objects, and JOIN keys.

But that brings us to another key mistake: focusing on the wrong metrics.
Not all data points are equally valuable. If your goal is to increase revenue, then focusing on metrics like social media engagement might not be the best use of your time. And if the business user sometimes knows this, the data analyst might not. That’s not his job to be a marketer. But his job is to go and interview a marketer about the specific data points that matter for this specific use case.

So in our case of increasing revenue, Instead of analyzing social media engagement, businesses should prioritize:

• Customer Lifetime Value (CLV)
• Monthly Recurring Revenue (MRR)
• Churn rates
• You name it, but not engagement that leads to nowhere. 

So tracking vanity metrics might look good in reports, but they don’t directly impact business growth. Like website traffic for example. 

Official Youtube Channel

Master Analytics with OWOX BI

• Insider Tips
• Analytics tutorials
• Live Training
• Product walkthroughs

Watch now

Vadym: 

Yeah, it’s easy to get caught up in numbers that don’t really mean much for revenue or growth.

A simple rule of thumb? If a metric isn’t leading to an action, it’s probably not worth tracking.

Ievgen: That’s a great way to put it. And speaking of misleading analysis, another major mistake is cherry-picking data - only selecting numbers that support a specific narrative. Or just the ones you enjoy. They look great.

This happens when both data teams and business users:

1. Ignore data that contradicts their assumptions
2. Manipulate numbers to justify business decisions
3. Present only positive trends while hiding negative ones

To avoid this, businesses need to analyze the full data and be open to insights - even if they challenge expectations.

Vadym: 

Yeah. The whole point of data is to discover the truth, not confirm assumptions.

Ievgen:
Absolutely. And another big issue is Ignoring outliers instead of investigating them.

Because outliers can reveal:

1. High-value customers that are worth analyzing
2. Fraudulent transactions that need deeper investigation
3. Plus Hidden trends that could lead to competitive advantages

Instead of automatically removing outliers from the analysis, use visualization types like box plots or scatter plots to explore them further.

Vadym:

I love that. Instead of ignoring outliers, ask why they exist.

Ievgen: 

And that leads us to another big mistake: bad data visualization.
Even the best insights are useless if stakeholders can’t understand them.
Look, dashboards should not look fancy or use some specific design. No.
What I am talking about is, for example, Using the wrong type of chart (for example a pie chart for time trends data).
Or overloading dashboards with too many of the metrics and dimensions.
Or basically when you name the fields or charts data without context, so nobody understands this. Nobody gets what that report or a dashboard actually means…
So a good practice is to keep visuals simple, straightforward, and easy to interpret.

Vadym: 

I couldn’t agree more. Good data visualization should be like storytelling - it should guide people to the right insights quickly.

Ievgen: 

Another common mistake is confusing correlation with causation. Just because two things are related doesn’t mean one is causing the other.
For example, let’s say an e-commerce company notices that customers who buy high-end laptops also tend to purchase expensive headphones. Does that mean laptops cause headphone sales? Not necessarily. It could be that people who buy premium tech have a higher budget and are more likely to buy both items.

Vadym: 

That’s such an easy trap to fall into. It reminds me of those studies where people say eating ice cream is linked to an increase in drowning deaths. But in reality, both happen more often in the summer - they’re correlated, but one doesn’t cause the other!

Ievgen: 

Yes. That’s absurd. This leads me to another overlooked mistake - comparing business data to the wrong benchmarks.

Let’s say you run a small e-commerce store and compare your conversion rate to Amazon’s. That’s not a fair comparison! Amazon has massive brand trust, Prime memberships, and global distribution- factors you might not have.
Instead, you should compare your conversion rate to a similar-sized business in your industry.

Vadym: 

Yeah, using the wrong benchmarks can really skew your perspective. 

Ievgen: 

The key takeaway from here is to use relevant, industry-specific benchmarks to get a realistic picture of performance. 

Vadym: Great. Any other data analytics mistakes?

Ievgen: Another major issue? Let me think. Let’s talk about data formats. Not standardizing data formats.

Data comes from multiple sources - spreadsheets, CRM systems, marketing tools, cloud databases, data warehouses - and if it’s formatted inconsistently, you’ll get errors, duplicate values, and misleading insights.

For example:

• One dataset records sales amounts in USD, while another records them in EUR. Without a standard currency conversion, your revenue calculations will be wrong.
• Dates formatted as MM/DD/YYYY in one system and DD/MM/YYYY in another can lead to misaligned reporting.

Vadym: 

If your definitions aren’t aligned, your reports are useless. So yeah, standardizing data ensures consistency across all reports and tools.

Ievgen:
So to prevent these mistakes, businesses need to standardize their data processes. This means:

• Defining clear guidelines for data collection and formatting
• Creating consistent naming conventions
• Automating this process…

Plus, it’s easier to fix this when you store all data in one place - like a data warehouse. That’s the right place to make that standardization happen through data transformations.

Vadym: 

That’s key. Standardization ensures everyone in the company is on the same page. 

Ievgen: 

Exactly. I think I have one more mistake here. And that’s Speed. Look, accuracy should always come before speed. Rushing through analysis can lead to costly mistakes. 

Businesses should: Double-check their findings and have a second pair of eyes review the data. And that’s what we see now a lot in this age of AI. When businesses rely on AI tools that inspect their data.
Please understand me correctly. AI is great. And in 95 percent of cases, AI analysis would be quick and reliable. But if you make decisions based on the other 5% of answers - you’ll quickly run out of business. Because of AI-halucinations, LLM drawbacks, etc.

Vadym: 

Yeah, speed is great, but bad decisions made quickly are worse than good decisions made slowly.

This has been an incredibly insightful conversation, Ievgen. If you wanted to highlight the key mistakes our listeners should pay attention to, what would those be?

Ievgen: 

1. Set clear goals before analyzing data
2. Ensure data is clean and formatted properly
3. Track meaningful metrics, not vanity ones
4. Investigate outliers instead of ignoring or removing them
5. Always use automation to reduce errors
6. And don’t forget to work more closely with business users on all of this.

Vadym: Awesome! For all our listeners, if you want to avoid crucial mistakes in your data analysis process, make sure to check out OWOX BI for smarter data analytics. Just visit our website, owox.com , and start analyzing your data for free today!

And don’t forget to subscribe to The Data Crunch Podcast for more exciting and insightful topics.

Ievgen: 

Thanks for tuning in! Stay data-driven, stay accurate, and we’ll see you in the next episode!

Gain clarity for better business decisions

No switching between platforms. Get all the reports you need in spreadsheets and focus on business optimization

Book a demo