10 Soft Skills A Data Analyst Needs – A Beginner’s Guide

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According to a 2022 survey, 51% of employers rated problem-solving as the most important skill they look for in candidates when hiring. 42% look for teamwork while 32% of recruiters voted for communication skills.  

We all know that employers are giving more importance to soft skills than ever before. Companies and businesses have realized they don’t only need technical skills but a combination of soft and hard skills is the best. This change is not limited to a specific field and data analytics isn’t excluded as well.  

This article is dedicated to letting you know the 10 most important soft skills a data analyst should have. Let’s get straight into the topic. 

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10 Soft Skills A Data Analyst Needs - A Beginner’s Guide
10 Soft Skills A Data Analyst Needs – A Beginner’s Guide

Why should you even care about soft skills?  

If you’re not clear about the importance of soft skills then bear with me.  

Technical skills aren’t enough, you need a certain level of soft skills to do the job flawlessly. This is not just in the data field it’s everywhere. Companies and employers are paying special attention to non-technical skills. You can survive an interview with a little less technical knowledge than other candidates but not without soft skills.  

You can learn technical skills but soft skills need experience and time to develop and improve. That’s why it’s necessary to pay attention to it as well. Most of us don’t actively work on our soft skills but everyone has some level of it. You just need to recognize and improve it.  

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10 most important soft skills for a data analyst  

  1. Communication:  

This is something that pops up every time someone is talking about soft skills. But it’s worth it. Communication plays a major role in everybody’s work.  

As a data analyst, you should be able to communicate your process, ideas and results to your clients and stakeholders. And most people aren’t aware of industry jargon, you can’t talk to them just like you would do with a fellow analyst.  

You need to know how much they know and get to their level while talking to them. Once your words are understandable and relevant to them you both can have a better conversation. One huge advantage of good communication is your audience can connect to you and this helps in building good work relations with your clients and fellows.  

Communication skills improve as you practice. A small tip is to ask your colleagues and fellows to highlight the area of improvement. Most of the time we don’t realize where we lack so asking others really helps.  

  1. Breaking down and identifying the problem:  
Problem-solving is an important soft skill for data analysis 

Mostly the problem statement is pretty short and sweet but its solution isn’t. To know the root cause of the problem you’ll have to look in many directions and deal with a lot of data. This can be tricky if you don’t break down the problem. Sorting it out in small pieces will be easier to work with. You’ll have clear directions of where you’ve to look and where the problem can be. 

This gives you a good starting point. You can dig deep into the problem in a strategic way rather than just looking here and there. It kind of organizes your work. Let’s understand this with an example.  


Let’s say our problem statement is, why our ads aren’t converting? Let’s divide this problem to know which areas to look at. You can see whether the ads are generating leads or not.  

If there are no leads it indicates your ads aren’t appealing to your audience or they’re targeted to the wrong audience. Another scenario can be that you’re getting leads but they’re not converting. There can be many reasons for that like the web page isn’t clearly showing them what they came for.  

  1. Analytical thinking:  

Analytical thinking is how you approach the problem. It’s about how you explain it and decide a way to solve it in an organized way rather than just trying various methods. If you see the first skill, breaking the problem statement, it also involves analytical thinking.  

  1. Critical thinking:  

Analytical and critical thinking isn’t the same. They both go together most of the time but there’s still a little difference.  

Take this as criticizing the problem as if you’re a critic. Is the problem statement correct or we can make it more specific? Is the collected data enough and accurate? Is your analysis answering the problem statement completely or do you need to dig deeper?  

Most of the time you’ll be using critical and analytical thinking simultaneously.  

  1. Social skills and teamwork:  

A survey was conducted in Norway in 2021 in which employees from various fields were asked to rate the importance of different soft skills in their professional life. 58% of employees rated teamwork as extremely important.  

Now, this doesn’t mean you’ve to be an extrovert. When we say social skills, it means we’re talking about having a good attitude, treating others well, actively listening, and helping others. These are not even specific to the data field, it’s a general skill.  

These are some basic traits that can help you build good relations. And regardless of your position or field, we all depend on each other. Sometimes you’ll be working in a team and it’ll be important to have good relations with your team members to get your work done.  

  1. Learning and keeping yourself updated:  

The whole field of data analytics is evolving pretty fast. New strategies, techniques and software are coming into the market. To stay competent you’ll also have to keep up with new information. 

Be a quick learner and identify what way of learning suits you. You can allocate some time every week to see the updates and learn.  

  1. Creative thinking:  

Creative thinking is shifting the viewpoint to draw different meanings from the same thing and finding new ways to approach a problem. This is what a data analyst does right? Solve a problem and draw conclusions from data, so there’s definitely a space for creativity in this field.  

  1. Organizational skills:  

The same survey that we mentioned in teamwork shows that planning one’s own work is extremely important for 52% of the employees.  

As an analyst, you’ll be working with a lot of data and you’ll have to be organized. It’s not like you should be, you’ve to be organized or else it’ll be a big mess. You might be working with 2 or 3 clients at the same time and dealing with all kinds of data. If you just put every file in 1 big folder it’ll be so difficult to work like that.  

Organize your workspace. Make separate folders for each client and make sub-folders to categorize the stuff.  

Organizational skills aren’t limited here it also applies to your time. You’ll have deadlines to complete your project, meetings and a lot of other stuff. If you just give 5 to 10 minutes daily to see what you’ve to do today, it’s enough to sort out your entire day.  

  1. Automation and scalability:  

If there’s anything you can automate you should. A data analyst wants to make the analysis process easier for common people. If you see a report that’s often required you can automate that. It’s more like making things understandable and approachable for the company. And yes you’re kind of removing yourself out of the equation but you can then focus on bigger and better tasks.  

  1. Industry and business knowledge:  

Basic industry knowledge is important in making analyses for that industry. It doesn’t mean you have to know every bit and all technicalities, but understanding your stakeholder’s business is crucial. You should know and understand what they do and how they do it. Also, try to understand why they’re asking for a certain report or dashboard. This will help you provide them with exactly what they want. 

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Frequently asked questions 

  1. Is data analysis a technical skill or a soft skill?  

Data analysis is a technical skill and to solve a problem statement you’ll need more technical skills related to it like SQL. However, you can incorporate your soft skills like analytical and critical thinking in the process as well.  

  1. Should you focus more on technical skills or soft skills?  

You definitely need a certain level of technical skills to get your job done. However, soft skills are the ones that add charm to your work. It separates you from your counterparts, especially during a job interview. So it’s best to keep a balance between both, and besides learning technical stuff also spend some time improving your soft skills.   

  1. Should you mention soft skills on your resume? 

Yes, you should mention soft skills on your resume or CV as employers look for that. However, make sure that your CV doesn’t revolve around that. Keep the main focus on your experience and achievements.  

In a nutshell

Soft skills are as important as technical skills are. You can get the job done with your technical knowledge but your soft skills set you apart from other candidates. Here are the 10 non-technical skills that we talked about,  

  1. Communication 
  1. Breaking down and identifying the problem  
  1. Analytical thinking 
  1. Critical thinking  
  1. Social skills and teamwork 
  1. Learning and keeping yourself updated 
  1. Creative thinking  
  1. Organizational skills  
  1. Automation and scalability 
  1. Industry and business knowledge 

Many non-technical skills are general, they’re not specific for a data analyst rather they’re for everyone. As said before these small habits and skills help you build relations in the industry and help you create your own reputation.  

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Published by Rahul Bhattacharya

Rahul is a journalist with expertise in researching a variety of topics and writing engaging contents. He is also a data analyst and an expert in visualizing business scenarios using data science. Rahul is skilled in a number of programming languages and data analysis tools. When he is not busy writing, Rahul can be found somewhere in the Appalachian trails or in an ethnic restaurant in Chicago.

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