The Simplest Guide to Data Storytelling — Elements & Examples!
Think of this, have you ever wondered how the world would look without any context?
- What if you don’t get your salary at the time you expect it to arrive? What next?
- What if you have all the best KPIs in the world that track your business most accurately, what next?
- What if you always are struggling around figuring out what happens to the economy if inflation or a price hike is expected, what next?
The key here is to understand that every number we come across in our lives, has a ‘why’ and a ‘what next’ associated with it. The vicious cycle of numbers making sense and showing you different perspectives of the same thing is never-ending.
And how better can you convey the ‘why’ and the ‘what next’ and the ‘how’ and every question about a number/data point than using a Data Story?
Any data science life cycle which does not contain Data Storytelling is obsolete. The whole idea behind an analysis is to be able to curate a data story that tells the audience exactly what the data tells, but only better and with added ‘context’ that gives each number the ability to bear impact.
Data Storytelling is the art of conveying compelling messages using data in the form of numbers/visualisations etc. that can help drive better and more comprehensive understanding of the data. This also helps your audience retain the information for longer.
In this article, we’ll go over an example of how data storytelling with all the elements that I think make a data story stand out. This is based off of how I perceive data storytelling so please feel free to add/remove any elements that’ll help you tailor a better data story.
What we’ll cover in this article;
- The Plot
- The Hero
- The Origin Story or the Beginning
- The Build-up
- The Plot Twist
- The Villain
- The Supporting Character
- The Climax
Sounds like it’s straight out of a movie right? Well, that was the intention. The movie is a hit when the story is anyway, so let’s jump right into it.
The Plot
It is of immense importance to understand the plot before you get to any other element of the data story. The idea is to remember that a plot determines the importance of characters and the story revolves around how the plot changes with every turn of the data story.
Let’s consider this, you’re a business analyst for a movie theatre chain. The plot is such that you’re analyzing your data to understand why your movies have suddenly shown a declining trend in terms of customer capacity and therefore leading to heavy losses that you intend to subside.
The key points about the plot here are that you are in a company that has 3 branches and each branch is located 5 km from each other you host only one type of seat for a movie go-ers and the price points don’t differ across the three theatres and you host 3 shows every day. Of course, this is imaginary, but hopefully, the story will start making sense once the characters are out.
The Hero
The Hero. There’s no need to describe the importance of the Hero in the movie. Of course, it’s the star and you will always want the hero to shine. The hero is the common element throughout the story. You start the movie with an introduction to the hero and you end it concluding with what happens to the hero after whatever they go through in the story.
Back to our movie-theatre example;
The hero here is the KPI that you calculate, the most basic, customers per day. The idea is to ensure that your customers every day keep increasing with time and that you keep your tabs on the customers per day to ensure that any signal of the hero’s weakness is noticed and things are put back into place.
Of course, in our hypothetical situation, the hero is not doing that great.
The Origin Story or the Beginning
When the hero is decided, the idea is to introduce the hero here. The better the introduction, the better the understanding of how important the hero is.
In our example, our company is showing a loss of 30% this week whereas it was a profitable phase just a month ago. In this case, it comes to the number of customers per day that have been declining by over 15% week-on-week. If this situation continues, the company will go bankrupt in no greater than 3 months even with fewer shows every day.
Therefore, the customers must return to your theatre to save the company.
The Build-up
The build-up is essentially adding more context to the story. It comes in a way that you know the situation is such. But the problem is probably a lot worse/better than you think.
In our example, it may be so that you notice that along with having lower customers every day, there are bad reviews about your theatres online which accelerate the decline in your customers per day is facing. You may as well be bankrupt in just 2 months and not 3.
Most elements are correlated when things are moving in a certain direction. You must observe these signals and understand what they mean before it's too late.
The Plot Twist
The infamous plot twist. This is not a necessary element all the time in every data story, because it always gets you when it's the least expected and pushes a lot more work that could otherwise be avoided while over-complicating things.
The plot twist may be such that in our example, you find out that the customers are trying to come to your theatre but the payments are not going through due to which they are forced to book tickets in an alternative theatre.
Guess the problem wasn’t the customers at all?
Let’s assume that thankfully there’s no plot twist in this story and keep moving ahead.
The Villain
After you know the context, the build-up, and the hero, there is no doubt that the hero will encounter his villain pretty soon. The Villain will come around and be in plain sight once your understanding of the previous elements is on-point.
In our story, it’s pretty clear that the customers are not going to the theatre daily and the reviews are bad. On digging deeper into the reviews to see if there’s a common thread, you may find the reason for this is that the Air Conditioners in your theatre is not functioning to the best of their ability thereby leaving customers unhappy.
Most issues with companies are such, they don’t necessarily have to relate to the data. They can be issues out of your data that can pinch.
The Supporting Character
This is a two-way street. Your supporting character can be positive or negative. The idea is that your supporting character amplifies the impact that your hero/villain may be having on the situation.
The supporting character in your story here would be so that your air conditioners don’t function in the afternoons when they’re required the most, leaving your customers unhappy. Fancy a reason why?
Let’s find out.
The Climax
The heading says it all. Weave it all together to come down to the common thread.
You take your report to the executives about why your company’s customers’ daily visits are going down. They call the relevant individuals to understand who’s responsible for making sure the ACs work well and that they should do their job better. Turns out one of the employees did not get the repair of the AC done assuming it was still functioning well and therefore, a careless mistake led to your company almost going bankrupt.
(Not the best conclusion, but hey, you get what the conclusion should be like)
How does it work?
Think of it this way, if the company does not see that its ticket sales are going down and question why? They don’t find out that there’s a shortage of customers due to certain reasons and eventually end up bankrupt.
By weaving your understanding of the data and ensuring that the price points of your tickets, the number/timing of your shows, or the demographics of your customers are not a problem, you save your company from making efforts in the places where they’re not required.
Identification of a problem is important. But what’s more important is addressing the root cause. Only when the company sees a disastrous impact of the company going bankrupt is probable, prompt measures are taken without any delay to ensure that the situation is as per the status quo.
For more about data storytelling, do give this website a look:
Conclusion:
Every business question, whether big or small, has an associated story. Every decision a company makes has an impact and a story to tell. If you think about it, these stories are what companies do. CSR work, high discounts, introductory offers, and every move a company makes carries an impact and a story.
As a Business Analyst or Data Scientist, it is your job to ensure that the numbers you show tell a story.
And a story to remember.
Do check out my previous articles here, if they interest you!
For all my articles:
~ P.S. All the views mentioned in the article are my sole opinions. I enjoy sharing my perspectives on Data Science. Do contact me on LinkedIn at — Yash Gupta — if you want to discuss all things related to data further!
