What makes a good data analyst? This is a question that I often pose to my colleagues when I’m training them to become more engaged in web analytics. More specifically, I ask them to consider the character traits they might expect of someone with this role, and then to reflect on whether traits, such as those below, resonate with them or not.
I believe that anyone can become an analyst if they put enough effort into studying and applying themselves. The skills required for the role often come more naturally to some; it is these people who tend to find more enjoyment in data analysis than others. Enjoying what you do is key to having an interesting and fulfilling career, so before you apply for one of the countless analysts jobs that have popped up in recent years, it’s important to ask yourself – is this really something I’d like to do?
Trait 1 – Natural curiosity
One telltale sign of all adept analysts is that they are a curious bunch. This almost childlike interest in how things work and why they work the way they do can be a major asset since it makes the job more enjoyable and fun given the “investigative” nature of the analyst position.
Deep down, analysts are problem solvers and pattern finders. For naturally curious people, finding patterns from a big data set is not a chore but an exciting puzzle to be solved. The main motivation for the job comes from the task itself; everything else is just an added bonus. The best analysts I’ve met are very passionate about their profession, and in my opinion you cannot have passion for data analysis if you are not a naturally curious person.
Trait 2 – Critical thinking
A critical mind not only helps you be objective in your analyses, it also makes you aware of your own biases and limitations. Anyone who has read Daniel Kahneman’s book “Thinking, Fast and Slow” will know that the human mind is inherently biased, and that it requires a lot of cognitive effort to think statistically. Being aware of these biases helps tremendously in your day-to-day work as an analyst.
This trait could also be called “scientific worldview”, as it is usually accompanied by the ability to be a critical thinker and a bit of a skeptic. Going through university education helps many people to develop a critical mindset, but it’s not absolutely required. Like with the traits listed here, some people are naturally more prone to critical thinking than others. Either way, the knowledge and ability to apply the scientific method (very roughly put: observation – research – hypothesis – data acquisition – analysis – review – results sharing) is a major asset for any analyst. Being methodical in your approach also ensures consistency in your work, which is very important when you want to be able to replicate the results. I also know that in today’s hectic work life, that comes with countless ad hoc requests, following the scientific method might sound like a joke. Nevertheless, it’s important to understand its importance in discovering new information and being able to challenge the status quo.
Trait 3 – Understanding your data
In order to understand your data, you need to be competent in several fields. First and foremost, you need to be good at maths – especially statistics. Saying that you need to be good with numbers might be a bit too obvious, I know, but the importance of statistics cannot be emphasized enough. People who are “good with numbers” are not just skilled mathematicians, they are also able to apply their maths skills to various kinds of business problems. Having a robust knowledge of statistical concepts, e.g. sample size, variance and significance, is a fundamental requirement for any sort of quantitative analysis, and any analyst worth their salt should know about the complicated relationship between correlation and causation.
However, you need more than just a strong statistical foundation to truly understand your data. You also need to have the business acumen to understand where the data comes from, what different variables are in play and how they are measured. More often than not it’s also necessary for you to clean your data before you can do any real analysis – in some positions this can even represent the bulk of an analyst’s daily work.
Trait 4 – High attention to detail
Attention to detail is a good trait to have in almost any profession, but for data analysts this is one of the main requirements. Rushing to complete a task and then delivering false results might have dire consequences for the organization, and ultimately for the analyst. Don’t get me wrong – everyone makes mistakes every now and then – but with the kind of work an analyst does, it would generally be difficult for someone else to spot a mistake before a business decision is made based on an analyst’s work.
Having good attention to detail usually means considering several different approaches to a problem, and not falling in love with your first solution. Also here, a methodical approach based on the scientific method comes into play. If you are lucky enough to work in a big team of analysts, you can have your work “peer-reviewed” by your colleagues to check it for errors. In many organizations this is a mandatory step before any actions are taken based on the results the analyst has provided.
Trait 5 – Mastering technologies and tools
It’s certainly a good time to be a data analyst, with countless tech stacks and tools available for you to choose from. Be it Python or R, Adobe or Google Analytics, Tableau or Power BI – you have a world of choice and you don’t have to master every possible tech and tool.
What matters is that you fully master the tools and technologies at your disposal, and that you keep an eye out for their latest developments. A good analyst also won’t pigeonhole themselves into too restrictive a tech stack, and is able to (or even eager to) learn new data gathering and analysis methods if need be – more on that in the final trait.
Trait 6 – Ability to explain your results in simple terms
No matter how clever you are or how elegant your analysis methods might be, you have to be able to communicate your results to your stakeholders to be seen and appreciated as a top analyst. An analyst, preferably a team of them, can have an enormous impact on any business, but only if the insights they produce are understood by the decision makers.
When communicating your results, you should stick to the bottom line; make sure you adjust your terminology to your audience’s level of knowledge and emphasize the business implications of your findings. The ability to visualize data is a very valuable trait as it is often the easiest and most effective way of communicating the results of a complex data set.
Trait 7 – Continuous learning
The final trait listed here ties most of the other traits together, and it is the desire, or perhaps even the need, to continuously improve at your job; to understand that even a whole lifetime will never be enough to learn everything there is to know about data analysis. The innate natural curiosity listed in the first trait should be more than enough to keep a great analyst yearning to learn more, fine-tune their methods, and discover new tools and technologies.
The best way to keep learning is to maintain an active exchange with other like-minded people, share ideas and learn from your experienced peers. This is also the main reason for this website, so please be sure to leave comments or questions below if you have any!
Thank you for the traits.
Really helpful and I enjoyed reading this.
Thanks it was really helpful
I enjoyed reading this. The points highlighted are things i need to work on.