5 Analytical Skills that Every Data Analyst Should Have

Ultimate Guide to How to improve Analytical Thinking

Richard Warepam
5 min readJan 30, 2024


Photo by Myriam Jessier on Unsplash

Are you passionate about data analysis?

By clicking on this article, I bet you’re either a seasoned data analyst or someone eagerly looking to dive into this field.

If you’re considering a career in data analysis simply because it seems trendy and offers a lucrative salary, this article might not grab your attention.

But if you’re like me—someone who finds joy in transforming raw data into compelling stories—then you’re in for a treat.

In each of us, there lies a set of hidden skills, especially if data analysis is more than just a job for you.

Some of you might not have discovered these skills yet, but believe me, if data analysis excites you, it’s because these inherent abilities are waiting to be honed and nurtured.

By developing these skills, we can make our journey in data science as brilliant and illuminating as the sun.

So, what exactly are these skills? Let’s dive in.

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Let’s get started!

Skill 1: Why? How?

Have you always been naturally curious? Remember back in high school, when you bombarded your teacher with all those insightful questions? Or maybe that was just me!

This innate curiosity is actually a valuable skill, especially in the realm of data analysis.

The more curious you are about the business problem you’re tackling, the more questions you’ll ask your stakeholders.

This not only clarifies the goals and objectives but also significantly enhances your data analysis process.

It allows you to start your project with a clear, well-defined plan, setting you up for more effective results right from the get-go.

So, embrace your curiosity. Ask more questions. Asking doesn’t indicate a lack of knowledge; it shows a genuine commitment to solving business problems in the most thorough way possible.

Skill 2: “Our company’s revenue increased by 15% last quarter.”

What’s your take on the statement I just made? Did it strike you as positive or negative?

I bet most of you felt pretty upbeat, maybe even thinking about a celebration for our company’s success. But what if I flip the script and say it’s not as great as it seems?

Consider this: the average revenue growth in the market last quarter was 25%. Now, that’s an important piece of context.

If I had started by saying, “Our company’s revenue grew by 15%, compared to the market average of 25% last quarter,” your reaction would have been quite different, wouldn’t it?

This example highlights the critical importance of context in data analysis. It’s all about understanding the full picture that the data presents.

This skill of comprehending data within its context is essential for any data analyst looking to excel in their field.

Remember, the ability to fully grasp the context of your data can make a huge difference in your analysis process.

Skill 3: Breaking Down things

Let’s delve into the third crucial skill every data analyst needs to hone: developing a technical mindset.

Reflect back on your school days—were you the one who enjoyed solving math problems? Or maybe that’s just me again!

Having a technical mindset means tackling each problem methodically and logically.

When you’re dealing with data analysis, this approach isn’t just beneficial; it’s essential. You can’t jump straight into analyzing data right after identifying the business goal.

To effectively address the main problem, start by breaking the process into smaller, manageable steps.

This includes gathering your data and then thoroughly cleaning it before you dive into the core analysis.

As a data analyst, nurturing a technical mindset for every task is not just necessary; it’s transformative. It paves the way for deeper insights and more impactful results.

Skill 4: Data Design

Imagine you’re working on a spreadsheet. The entire process where you arrange your data neatly to spot patterns and insights is known as data design.

This skill, like many others, grows stronger with practice.

Data design is a critical skill, almost an extension of the technical mindset we talked about earlier.

In this phase, you’re rearranging cells and organizing data, making it easier to discover various patterns.

As a data analyst, it’s important to not just understand but also continuously improve your data design skills with every project you tackle.

This focus on data design is key to unearthing valuable insights from your data.

Skill 5: Data Strategy

Wrapping up our list of essential skills for data analysts, let’s talk about data strategy.

It might sound a lot like data design, but it’s actually a bit different.

Data strategy involves managing not just the data itself but also the people, processes, and tools involved in data analysis.

Think of it as a kind of resource allocation skill. It’s all about choosing the right tools and approaches for the specific business problem at hand.

For instance, if the business issue calls for a simple dashboard, you might lean towards using spreadsheets.

But if you need something more comprehensive and interactive, tools like Tableau or Microsoft PowerBI become your go-to options.

Understanding how to strategize and make decisions about factors beyond the data is crucial. That, in a nutshell, is what data strategy is all about.

Wrapping this up!

As data analysts, we often ponder the best way to think analytically about each project we undertake.

The key lies in employing the five skills we’ve discussed throughout this article in every project.

By harnessing these analytical skills, you’ll find that your understanding of the problems becomes five times clearer and more concise, even before you begin the project.

Therefore, taking a step back to focus on the big picture and applying analytical thinking, as outlined above, is essential for every data analyst.

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Richard Warepam

Data Scientist & Writer | Google Certified Data Analyst | As a Mentor - Writes on Data Science and AI | My eBooks: https://codewarepam.gumroad.com/