Time and again, data science has proven that it has the capability to take businesses to a whole new level. Organisations across the world are leveraging the superpowers of data. But what’s next when you are done collecting and combining all kinds of data? So, the next thing is to extract value out of that data. And for that, organisations need a data scientist who can uncover insights from that data and translate that into actions or business outcomes.
This all might sound very fascinating, however, it’s not an easy task. To carry the entire process, a data scientist needs to have the storytelling skill — the skill of taking an idea and turn it into a story.
“A lot of people want to pursue a career in data science. And, there is a lot of apprehensions that only if you understand models or write algorithms you can be a part of this journey. However, that’s not the case. A data scientist is at the core of problem-solving; someone who understands business and passionate about data. S/he also needs to be a storyteller in order to find out meaningful insights from the data.” said Sohini Mehta, Head – Wipro Ltd. at Rising 2019
Why A Data Scientist Should Also Be Storyteller
The answer to that is, as data gets deeper and more complex, it becomes imperative to bring in simplicity in it. And storytelling makes it simple and more interesting, drawing interest from listeners and readers alike. Also, Stories provoke thought and bring out deeper insights.
Another reason is that when data and analytics reveal great insights, an absence of narrative makes it hard to relate to the facts. And this is where data storytelling comes into the scenario — it takes data visualization to a whole new level. With the help of real-life instances and experience, a data storyteller helps its audience understand better.
This also helps in understanding the logic behind every data and analysis. Making use of attractive visuals, right format, and a strong narrative, a data storyteller helps decision makers have the clarity of the issue and understand the most appropriate action to be taken.
Things To Keep In Mind While Creating
Knowing Your Target Audience
It is the first and foremost thing every data storyteller should do. Without knowing your audience, don’t go straight up to create your story because it has to be furnished and polished to suit the audience and their level of understanding. To some extent, it might still be acceptable if the audience doesn’t engage with your story, but it is not at all okay if they are unable to understand it.
The Pen-Paper Approach
When you are creating a data story, don’t directly jump into the presentation. First, take a pen and paper, and write down your ideas and flow and once you are done with that, structure your story. Think of a real-life example arouses the audience’s interest. Also, prepare questions for your audience and the solution you would offer. This is considered to be a best practice for everyone who wants to be a good data storyteller.
Discover The Sole Purpose Of Your Data story
This is one of the most important phases when you are creating your data story. Always make sure you know what’s the sole purpose of your story; what value it is going to deliver. And for that, you have to dig deeper to find out what exactly the story can do to make decision making better.
Use Influential And Powerful Heading
Why a powerful heading is important? Because it is the first thing your prospects will see. And an influential and powerful heading will leave a strong impression. Always make sure that your heading is specific and deliver benefit.
Also, don’t forget that is just like the trailer of a movie. The more appealing it is, the more you draw attention.
A Brief Yet Powerful Conclusion
Once you are done telling your data story to your audience, you have to end the session on a powerful note. Make sure your conclusion is not too long — end it in such a way that your audience don’t let the thought of your story go out of their mind.
Wrapping Up
With time, analytics and business intelligence are growing bigger and better. And it is expanding the pool of people generating insights, increasing the need for more data storytellers in the future. Therefore, it is high time that data scientists shouldn’t just stick to numbers and analytical skills train themselves on good narrative around their analysis.
