In the modern world, there is data production continuously. Every online payment, social media interaction, app click, and business operation produces valuable data. But just data collection does not suffice, it is also vital to interpret it in the right way.

That is where the significance of data storytelling comes into play – it is about converting numbers into insights and creating a story around them. This tool is used by data scientists, data analysts, and businessmen to convey their data in an interesting way.

Data storytelling includes combining data, visualization, and interpretation.
Unlike:
“We experienced a 40 percent growth in traffic on our website.”
The story behind the data is:

“We experienced a 40 percent growth in traffic on our website ever since the start of our social media campaign. This traffic is mostly coming from Instagram and YouTube channels. In other words, visual marketing works great for us and we can further expand our video content.”

A company acquires tons of data daily. Yet, this data has no value if not analyzed properly.
Data storytelling might be valuable because it:

Storytelling can be utilized anywhere from a conference room to a presentation or report.

All good data stories have good data that is reliable, relevant, and clean. Bad or irrelevant data means bad and irrelevant insights.

Images are better than figures and text when it comes to making sense out of the data. Visuals make understanding data easier.
Some common examples of visualization are:

Visualization will always stay simple and clear.

Narratives help to combine all the data and visuals into one story through the answer of four basic questions:

Here is an example of a business tracking its monthly sales.

January: ₹50,000
February: ₹55,000
March: ₹53,000
April: ₹82,000
It is simply a list of numbers. However, let me tell you why it is like that: The sales have been relatively steady during the first three months but then grew abruptly in April. The factors leading to such growth are explained clearly – it is due to seasonal offers and effective digital marketing campaigns.

Therefore, it may be done again during other peak seasons. It is an example of data storytelling.

A good data story mustn’t have too much information.

Some of the mistakes that beginners make are:

This is what makes the difference between amateurs and professionals.

It is necessary to teach people how to work with data without overloading them with it.

Technology has developed to such an extent that it is now possible to construct data stories using artificial intelligence technologies. Artificial intelligence technologies are capable of pattern detection, gaining insights and producing visualization. Interactive dashboards have become the norm rather than the exception.

Nevertheless, human intelligence is also an important component of the process of data storytelling – artificial intelligence is capable of pattern detection, while humans are responsible for interpretation of those patterns and their use in the process of decision making.

Data story is a link between raw data and decisions. Data story transforms data into insights and useful information. No matter what your position – whether you are a student, analyst or business person – you will be benefited from a good data story.