In this post, you will find out the four crucial skills to become a data storytelling master and why you should improve them.
Firstly, I have good news for you. Everyone can be a data storyteller and possess the required skills on a “good enough” level. Of course, each of us starts from a different position, and the time necessary to reach a “good enough” level will differ. There is scientific proof that you need about 10 000 hrs to be professional in any picked field, but only 20 hrs to have a basic knowledge of a subject. There are four fundamental skills that made you a data storyteller.:
- Analytical skills
- Data visualization skills
- Communication skills
- Subject comprehension
This skill is a basic of basics. Without understanding numbers and reading them, you cannot adequately prepare a story about them. Even when you are not a “data person” – someone who already has had the skill to transform and interpret massive data sets, you still can learn it.
Our brain is divided into two halves – left and right. The left half is responsible for analytical, logical and sequential thinking. In this part of the brain, the centre of language is located. The right half gives us the ability to perceive in a non-verbal way: see objects in space, compare similarities, have intuition, have a holistic view of something. Most people experience domination of one of the halves. However, it does not determine that you are an artist or an accountant. If all humans have both halves, all of us can analyse and interpret data. Naturally, some of us are more gifted than others, but I would be far away from the opinion that you cannot learn analytics unless you suffer from solid dyscalculia.
But where to start your journey with data analytics if you do not have previous experience?
Foremost, understanding descriptive statistics is a game-changer. Descriptive statistics are methods for organizing and summarizing information. Having those statistics in place, we can start asking the right questions that help us reveal some insights. Mostly, we do analytics to see some trends, picks and falls, a contribution of factors or distribution of one of the characteristics within the population.
Data Visualization skills
Ok. We gathered all required data, organized and summarized them using descriptive statistics. But how make them readable for others?
In lots of companies still, a primary tool for performance reporting is MS Excel. And still, in those companies, the primary manner to present numbers is an excel table. There is nothing wrong with using tables, and sometimes they are even the best way to communicate results. However, we have much more tools to select to communicate numbers effectively. There is quite an impressive range of available data visualizations in any common software like Excel, Power BI or Tableau, just to name a few. Visualizing numbers is a skill like any other. You can learn it and master it.
Nowadays, this skill is more important than ever when we are submerged in the data ocean. Data visualizations are often the only way to make sense of data, find patterns and understand the surrounding world. Data visualization utilize human perception to communicate and receive data. If we do it without proper diligence and mindfulness, we can mislead our recipients, and as consequences, they will draw wrong conclusions. The worst-case scenario would be misleading the audience on purpose. Regardless of designer intentions, it is an ignorance of using and presenting data in an unethical way. There is plenty of sources that provide rules and best practices on how to use data visualizations correctly. So do not miss this opportunity and earn credibility.
So, two first steps in a process have been already done. You found interesting patterns and insights in the data and prepared their visual representation to make it visible to others. However, how to convey the message?
As a species, we are designed to communicate complex ideas and theories because we have speech apparatus, unlike any other animal. And vocal communication is a basic one for us. Thanks to our ability to pass complex ideas and theories, we have built an advanced civilization. But even when we speak the same language, we often cannot efficiently articulate our thoughts, and the receiver can misinterpret our message.
From a data storytelling perspective, there are two crucial components of communication. The first one is to use language adjusted to the audience. It is easy to overwhelm the audience with technical jargon, lose their attention and, in the end, lose their interest in the subject. The second one is the ability to make simple explanations. There is good exercise, at least when you have kids. When you explain your thoughts in a way that a seven-year-old kid can understand, you are the master of communication. To achieve it, try to use as many as possible comparisons, examples and metaphors from your audience experiences.
On top of the three essential skills, there is one more. I have already emphasized several times how crucial subject knowledge is. As Steven Covey said, “firstly understand to be understood”. You will not be a convincing storyteller without knowledge of what answers your audience is looking for. There is a simple rule: people always are interested in their business and problems, not yours. So, when you want to persuade them, you must present benefits or threats for them. From my experience data analysts are overloaded with ETL jobs and do not have enough room to talk with business people about business pain points and challenges. Those conversations would significantly enhance provided information. Data without context and understanding what is behind the scenes are useless.
Apart from mastering analytical, data visualisation, and communication skills, try to become a true partner for the business that you support. Build strong relationships with your internal or external customers and listen to them actively. Most people are willing to talk if you are pleased to listen. There is no better source of knowledge than subject matter experts. With those competencies, your ability to have a real impact within the organisation and building your personal brand will grow.
From Beginner to Master of Storytelling
In each discipline, there are levels of mastery. There is no difference with data storytelling. Check out where are you right now and what your aspirations are.
This is entry-level. You even do not know that there is something like data storytelling and you can learn it to enhance your data analysis. I often meet beginners as young people who have just started their career. Their heads are plenty of theory, but they lack practice. In the first place, Beginners should improve business knowledge to prepare better and relevant data analysis. In most organizations, there is plenty of internal training and materials that bring closer inner business processes, rules and characteristics.
I would say that those are mostly data analysts with excellent analytical skills and data visualization skills who do beautiful data visualizations that often are totally useless. The pitfall here is that when you are experienced in one very narrow specialization, you can have the delusion that you know better than others what they need and how to present it. However, they produce products for their customers and should listen to their voice. Maybe your customer does not know how to analyse data but for sure know what questions are interesting and show you in which direction data analyses should go. Recruits are typical data people, and they put too much focus on technical aspects, and they too often use very technical language when they communicate with non-technical people. They should focus more on the business side of analysis and less on the analysis itself.
As you can see on the matrix above, I valuate communication skills and business acumen more than data visualization skills in data storytelling. People who already know what the pain points of business are and can draw the audience attention do not need to know advanced data visualisation techniques to impact. However, there is a potential risk to easily mislead the audience if someone uses data without proper knowledge about basic best practices of data visualization. As I have mentioned earlier, data visualization techniques are based on human perception, which is a very fragile cognitive apparatus. Leaders have a special mission in spreading data culture across organizations because they feel comfortable with data, know how to use and present them, and, thanks to their position, can make or influence data-driven decisions.
Masters have proficiency in all four skills. What is more, thanks to the linkage of business knowledge and analytical skills they are true advisors, who can set directions of future growth.