The Soft Skills every Data Analyst needs

Most aspiring analysts are really scared of the term "soft skills". These skills seem to belong to a mystical ominous realm of abilities you ought to have, but for some reason you don't and you need to make an extra effort to acquire.

Let's demystify the blanket statements that people throw in when it comes to soft skills. More than that - let's translate into normal human language what these clichés actually mean!

1. Analytical Thinking

This is probably my favourite one of them all. You might be thinking to yourself - well, I've studied to be an analyst, I must possess this, right? And you'll be right to think so. Analytical thinking in its core is actually critical thinking. This is a skill you'll develop further and further in your career, but it boils down to questioning everything you see and trying to find an explanation for it.

Example: Suppose you're performing data cleaning on a data set. You notice a bunch of NaNs. What do you do? Do you go in and remove them, because they're NaNs or do you stop and ask yourself some questions: why is the data missing here? Is this expected? How much is it? Is there a pattern? Can I cross check with another variable to see whether the there's some kind of a regularity in the missing data?

This, folks, is analytical thinking.

2. Problem-Solving

Data analysts encounter a wide range of data-related challenges, from data quality issues to incomplete datasets or ambiguous research questions. Strong problem-solving skills empower us to approach these obstacles creatively, devise effective solutions, and ensure accurate and reliable analysis.

What this means more often than not in the real life of an aspiring analyst is: how well can you use Google to your advantage? Also, if you're already on the job, do you know who you can ask in case your Google search efforts turn out to be futile?

Use Google and Chat GPT (the latter with more caution) to find answers to your problems. When you're facing a difficulty and you're unsure how to proceed the best course of action is to read everything you can on the topic, gather 2-3 options that seem reasonable to you and then ask someone more knowledgeable for advice. Searching is a skill and you'll make yourself a favour if you begin practicing it and excelling at it. The only thing that's required is patience and in time you'll know exactly how to do research in order to solve your problems.

3. Attention to Detail

To me as a long time educator and analyst nothing beats this one. The most stupid mistakes I've made in my career were due to pacing and missing important details out. The accuracy and reliability of data analysis depend on our attention to detail - write this on a post-it and glue the post-it to your wall. Small errors or oversight can have significant consequences in a data project, leading to flawed insights and misguided decisions. Data analysts must exhibit a meticulous approach, double-checking data, validating assumptions, and scrutinizing every step of the analysis process.

Most of the time what this means is to take your time when you're doing something, regardless in which tool. Don't skip steps, don't rush. The second element is to always double check your work with a pair of fresh eyes. After finishing a task in your analysis, get up, walk around, talk to someone about something unrelated. Let your brain rest and forget about the project. Then return and check your work. Do this every time you're working on a key piece of your project and you'll never be in a situation where your results are wrong.

4. Communication

Here's one where most people start shrugging. The thing with analysts is that we very often have to communicate technical details to people who have no idea what we do. And they don't really want to get behind what we do, which is why they hire us. We often serve as the bridge between technical complexities and business stakeholders. While using tools such as visualizations and dashboards to illustrate our results is certainly helpful, you should always know your audience and try and adapt your explanation style to their level of understanding. If you're speaking to someone clueless about data, try to form your narrative as if you're talking to a 10 year old. This is not demeaning - quite the contrary, people will appreciate you trying to explain field specific terms to them in an accessible language.

Here are some other aspects of communication:

• Getting requirements from stakeholders and doing so in an approachable manner without ruffling their feathers

• Applying expectation management when dealing with stakeholders and clients

• Project management - effectively conveying to your project leads/managers how long a task will take

5. Data Visualization

Yes, in a way this is also a soft skill. Presenting data in a visually compelling way enhances its impact and facilitates understanding. Data analysts skilled in data visualization leverage various tools and techniques to transform complex datasets into intuitive charts, graphs, and interactive dashboards that facilitate effective communication and decision-making.

We're apparently talking about the design part of visualizations, for example, how to set up a dashboard so that interpreting said dashboard is as effortless and pleasing as a cup of silky latte on a cold winter afternoon.

The thing is, I've come to the conclusion that very few people have a natural talent for visualizations - in my fare share of educating people I've seen maybe 2-3 rare talents. All the rest were really bad at it - myself included. What you can do about it is take a design course like this one*. This will help you immensely in knowing what you're doing when creating visualizations.

6. Collaboration

Data analysis is rarely a solitary endeavor. We collaborate with team members, stakeholders, and subject matter experts across various departments. Strong collaboration skills foster effective teamwork, enable knowledge sharing, and enhance the overall quality of analysis and decision-making.

What this means in normal language is - be curious and be open to help. When you're unsure what to do, ask someone. When you see a colleague struggling, offer your help. It's as simple as that.

7. Learning Mindset

You know by now that data analysis is a rapidly evolving field, with new technologies, tools, and techniques emerging regularly - pretty much every month there's a new trend, new tool or a technique. While you should totally be open to new findings, do yourself a favor and focus at one thing at a time. If you're in the habit of scrolling constantly through various platforms you're risking becoming overwhelmed and not picking up anything that you'll see through.

What to do instead:

• Make a list of skills you feel you could improve

• Find a suitable course for one of them and enroll

• Don't start another course before you finish the first one

That's it, folks, let me know if I've missed something out!

*I have no affiliation with Udemy (unfortunately)