5 Data Analysis Mistakes Everyone Makes (And How to Avoid Them)

A No-Nonsense Guide to Making Numbers Work for You

👋🏽 Hey, it’s Ismail. Welcome to data nomads lab newsletter on learning data analytics, career growth, networking, building portfolios, and interview skills to break into tech role as a high-performer.

Ever wonder why your data analysis isn’t giving you the insights you need?

You’re not alone. Let’s break down the most common pitfalls and learn how to sidestep them.

1. Starting Without a Question

Many analysts dive straight into the data without knowing what they’re looking for.

What to do instead:

• Write down your specific business question

• Identify what success looks like

• List the metrics that would answer your question

💡 Pro Tip: Before opening Excel, write down: “By analyzing this data, I want to learn _____”

2. Trusting Dirty Data

Garbage in, garbage out. It’s the golden rule of data analysis.

Common data quality issues:

• Missing values

• Duplicate entries

• Inconsistent formats

• Outdated information

• Wrong labeling

💡 Pro Tip: Create a quick data quality checklist. Run it before every analysis.

3. Forgetting to Look at the Big Picture

Getting lost in the details is easy. Really easy.

How to zoom out:

• Compare current numbers to historical trends

• Look at industry benchmarks

• Consider external factors affecting your data

• Do quick validation with internal Subject Matter Experts (SMEs)

💡 Pro Tip: Always ask, “Does this make sense?” If something looks too good (or bad) to be true, it probably is.

4. Overcomplicated Visualizations

Fancy charts don’t equal better insights. This is very common

Keep it simple:

• Bar charts for comparisons

• Line graphs for trends

• A scatter plot (aka scatter chart, scatter graph) to observe relationships between variables

• Pie charts only for parts of a whole (and use sparingly!). Not my favorite chart type.

💡 Pro Tip: If you can’t explain your visualization to your grandmother in 30 seconds, it's too complex.

5. Not Telling a Story

Numbers alone don’t drive decisions. Stories do.

Build your narrative:

• Start with the business problem

• Show the evidence

• Explain the impact

• Recommend clear actions

💡 Pro Tip: Structure your findings like a news story: lead with the most important insight.

Putting It Into Practice

Let's see how this works with a real example:

Bad Analysis: “Our website traffic increased 25% last month.”

Good Analysis: “Mobile users drove a 25% increase in website traffic last month, primarily through social media links. This suggests our new Instagram campaign is working. Recommendation: Increase social media budget by 15% to capitalize on this trend.”

The Takeaway

Good analysis isn’t about being perfect. It’s about being thoughtful, systematic, and clear. Start with a question, check your data quality, look at the big picture, keep visualizations simple, and tell a compelling story.

Want to level up your analysis? Start by picking one of these areas to focus on this week. Share your experience in the comments below!