Oxford Dictionary describes soft skills as:
"Personal attributes that enable someone to interact effectively and harmoniously with other people."
Soft skills are individual personality traits that determine whether you will work well with your colleagues and be a good fit at a company. 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’re in. We at Mammoth Analytics have spoken to a few data analysts on Reddit 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.
You need to set the expectations right and need to put extra effort right at the project scoping phase. You need to be a key listener. Many a time 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 what is good for them. You need to put on the advisor hat and help your client define the right objectives. Hence it becomes important to set up communication correctly so that everyone is on the same page. The more time you can afford doing this, the better.
Sometimes, you are helpless. Maybe your client does 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. A lot.
The soft skill some successful analysts spend the most time on is clear, succinct interpersonal communication. If you're gathering requirements, you need to probe and question the customer (while being diplomatic if you're questioning their beliefs) until the requirements are completely unambiguous. This is harder than it seems if they or you are assuming a mutual understanding of some complex topic when actually that understanding isn't there. People don't like admitting that they don't know something.
If you're asking someone to do something, you need to be explicitly clear about what exactly you need them to do, 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, but it's vitally important to reducing misunderstandings about who is waiting on whom for what. "I didn't realise you needed me to do that" is not (necessarily) a failure of the person speaking, it's (often) a failure of you when you asked them to do it vaguely. You need to be on top of the communication as much as you need to be on top that latest data automation you are working on.
A key skill relevant to data analysts at all levels 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. Often, you are not a decision maker, but it is your responsibility to present your work in such a way that the decision maker considers it thoroughly before they act. You are working with your team and the team is not working for you. 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.
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.
No matter how complex your models are, if you can not explain it and its output to people with no technical knowledge, they likely won’t get on board. You need to constantly think about presenting your thoughts in a way so 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 aren’t an end in of themselves. So don’t do your math and let that 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 or 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 aren’t prepared or willing to understand.
If you are not seeing your recommendations implemented, this might be part of the problem. Empathy will be of clear value here.
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 don't know something or they have done a mistake. Empathise with your colleagues & consider what your actions will result in. In other words, don’t always say what you know, but always know what you say.
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. So it’s 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.
Get good at 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.
You need to be ready to not only share with your team what results you have but also how you arrived at them. You need to let your team know if you doubt your results. 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 eyes are better than one and by being transparent you have the chance of reducing your biases affecting your findings.
It really helps if you spend at least some of your time interviewing once you are part of a team. It helps your team immensely if you can develop the skills needed to build that team and expand it. 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. It might be training or 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 of your hard earned knowledge. This will naturally build the respect of your colleagues towards you & 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. Did we miss any key soft skills you think are important? Let us know in the comments!