8 Soft skills you need as a Data Analyst

We often hear about how soft skills or “people” skills are important in determining if someone is a good fit in a team. These personal attributes enable one to interact effectively and harmoniously with other people. Often, these are intangible but extremely important for almost every kind of position. 

Things like empathy, open-mindedness, and a willingness to learn are all soft skills that we can utilize whatever industry we are in. We at Mammoth Analytics have spoken to several data analysts over the past few months to see what soft skills they feel benefit them most, and how you can improve yours. Here are our top findings.

Set right expectations from the start

You should set expectations correctly at the outset of the project to avoid headaches later in the project. What your client says they need and what they actually need might not be the same. Also, there is no guarantee that what they want is the best course for them. Hence, it becomes important to set up communication well, so that everyone is on the same page.

You need to become an active listener to distinguish needs from wants. Put on the advisor hat early on. Help your client define the right objectives. The more time you can spend setting expectations early on, the better.

Unfortunately, sometimes you are helpless. Your client may not have good enough data that can support business decisions. You are not a magician as you can’t turn “nothing” into “something”. You need to have the courage to walk away from a project if it is not going to be of value to your client. Your integrity matters.

Interpersonal communication is key

The soft skill many successful analysts possess is clear, succinct interpersonal communication. If you are gathering requirements, you need to probe and question the customer. Be diplomatic if you’re questioning their beliefs. Continue until the requirements are completely unambiguous.

Clarify, clarify, clarify. Don’t let an assumed mutual understanding of some complex topic, lure you into thinking that you see things the same. Test to check that a common understanding is there. People do not like admitting that they do not know something.

If you’re asking someone to do something, you need to be explicitly clear about what you need done, why, and by when. It can be easy when you’re trying to be more formal or more respectful to try to avoid just straight-up asking when they can have that document filled out and returned to you.

Nevertheless, being direct, clear, and succinct is vitally important to eliminate gaps in understanding. “I didn’t realise you needed me to do that” is not necessarily a failure of the person speaking, it’s often your failure to state your needs well. You need to be on top of the communication as much as you need to be on top that latest data automation on which you are working.

Influence without authority

Another key skill is the ability to positively influence people in such a way that others follow and act willingly as opposed to complying because of the authority factor.

If you are not a decision-maker, then it is your responsibility to present your work in such a way that the decision-maker considers it thoroughly before they act.

If you are the leader, it’s wise to remember that you are working with your team and the team is not working for you.

Record minutes of significant communications, so that you can clear up misunderstandings arising later, due to someone’s poor memory of the discussion, decisions and action points agreed.

It can be frustrating in the beginning, but as you build credibility it will become easier. You might know the right thing to do in many a situation, but if you are not able to convince the stakeholders in a positive manner, you will have a hard time implementing your suggestions. This can even cause career growth-related issues as results are everything at the end of the day.

Improve your presentation skills

Being an analyst means interpreting numbers into business actions and recommendations. You need to be able to interpret complex data into understandable information allowing senior stakeholders to make better decisions. Consequently, it is crucial that you are able to clearly convey your findings to the right people.

No matter how complex your models are; if you can not explain the insights you have discovered to people with no technical knowledge, they not only may not grasp what you are trying to convey, they likely will not get on board.

You need to constantly think about presenting your thoughts in a manner that your audience understands you completely. This not just requires you to understand your work, but also the audience.

Remember that data and analysis are tools for telling a story. They are not an end in of themselves, so don’t do your math and expect that to fully dictate the story. Instead, understand the point of the story and use math to tell the story. Your job is more like a curator’s than an artist’s; it’s not just about the creation of an object, it’s how you present it and shape the perspective and context around it to generate the desired takeaway.

This doesn’t mean being dishonest, misleading, or distorting analysis to meet a narrative. It means telling the truth in a way that fits the framework of the audience so that they can accept it and act accordingly, instead of getting confused by detail or nuance that they are not prepared or willing to understand.

If you are not seeing your recommendations implemented, your inability to communicate your findings might be part of the problem. Empathy will be of clear value here.

Look beyond numbers

When you have some results you want to show the team, seek to understand what are the implications of your analysis. Will it result in layoffs or an expansion? Does this make someone look bad or look great? People don’t like admitting that they do not know something or they have done a mistake. Empathise with your colleagues and consider the consequences of your actions. In other words, do not always say what you know, but always know what you say.

Be firm with your conclusions

Sometimes, people will come to you looking for numbers to back up their desired conclusion, when the real world truth tends to be inconclusive, and when conclusive, you will see outright failures as often as clear successes. As a result, it is critical to learn how to communicate firm conclusions in a very clear way, with strong evidence, but not requiring an understanding of that evidence to have confidence in the conclusion.

Focus on providing more helpful answers than “we don’t know” or “can’t say for sure.” Develop standard and justifiable approaches for presenting ranges of possibilities or estimating confidence/likelihood of the desired outcome.

Provide transparency & visibility

You need to be ready to share with your team, your results, and also how you arrived at them. Be transparent about areas where you may have made assumptions, or have doubts. Work with your team on the workflows you create and try to design your solutions such that it is always possible to audit and debug them.

Always keep someone in the loop with your work. Two sets of eyes are better than only one, and by being transparent, you have the chance of reducing any personal biases which could affect your findings.

Hire and mentor

It really helps if you spend at least some of your time interviewing new potential team members. You will find you can develop the perception to sense gaps in the team make-up, and hire to complement the team unit.

See this as a challenge in its own right. People are harder than data and analysing potential candidates could be harder than understanding a million rows.

It also helps if you are a good teacher and can contribute towards the career growth of your colleagues. Conduct and participate in peer reviews, training and hands-on sessions. It might even be pairing together for little projects. By doing this, you are doing the noblest thing you can do: sharing your hard-earned knowledge. This will naturally engender mutual respect, and set up a chain reaction of positivity in your team.

Remember, you need to make sure enough people know how to replace you. This is the only way to be free of your work and keep moving further in your career.

These are the top soft skills we think data analysts should embrace. As we have found, it is not enough to be able to crunch the numbers. You need these soft, but important skills to have a successful outcome. Remember that your goal is to derive value from your data analysis to make some business decision, and that will be virtually impossible without these key soft skills.

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