Importance of Data Storytelling in Data Science
By: Anju Rajbangshi
Tags: Machine Learning, Data Storytelling, Data Visualization, Data Science, Data Analysis

Introduction

When we talk about Data Science, things that first cross our mind are lots of math, statistics, linear algebra, algorithms, various tools and multiple programming languages. But all of these would not make sense if there was no data. Every day, we see humongous amount of information generated in multiple forms like numbers, texts, images and videos with the advent of so many new devices, portals and platforms. Data Science is a basically a domain which involves making use of these data for profitable business outcomes.

And that is the reason we have numerous roles and sub domains in data science itself like analyzing the data , understanding the data, doing feature engineering on the data, cleansing the data, doing exploratory data analysis aka visualization and ultimately applying our models on the perfectly tuned data to predict outcomes.

One of the significant aspects in the data science and AI space is for a data scientist to be able to present his thoughts and analysis to the stakeholders in laymen terms so that anyone can understand the final message.

It’s like giving a voice to hours of our hard work.

What is Data Story Telling?

Story telling has been influencing the human population since the dawn of history. Stories are the primary form of communication about meaningful information via sources like books, novels, ads, movies and many more.

In data science also , we do a lot of data analysis and visualization using tools like Tableau, Qlik sense, the python scripts and the various python libraries which aid us gracefully in doing so, thereby helping us to generate hidden patterns and insights, colorful and advanced forms of both 2D and 3D plots and graphs.

But why do we need data visualization?

Suppose we need to predict the sales of a particular store and we have been provided with historical data of 200000 rows and 15 columns in an excel or a CSV file. That’s a huge variety of information right. Say, we have data about various products in the store, number of cities, gender of the population, age of the purchaser and many more. Now, it will be very difficult for us to get insights about the data from the excel file. It will be a Hercules task or a nearly impossible chore to go through the rows one by one and understand how each of the observations is contributing towards the sales of any particular product in the store. Also, it will be very difficult to trace out the outliers or noisy data in the CSV file. These are few of the instances why visualization is important.

Another simple example I would like to refer here is, let’s say business wants to know the count of males and the age range who are buying more products from the store.

In such scenarios, we can easily have a generalized idea by visualizing it with the help of various plots.

Below figure helps us in getting the required information with hardly two three lines of python code.

Now, the above plot just shows us what is happening. For e.g. we can infer from the bar-plot that the male population within the age range of 51–55 are the highest purchasers of the product accounting to a total amount of nearly 10000$.

On the other hand, we can see that the female population in all the age ranges contributed less towards the purchase of the product compared to their opposite gender. But why?

This is exactly when Data Storytelling comes into play. Creating a story out of the discoveries made from the data analysis for profitable results.

Dashboards, graphs, visualization only notify us what is happening with the data but it does not give us meaningful insights as to what is the reason behind what is happening. So keeping that in mind, we need to find ways to convert our data into quality information that can help us have rewarding business outcomes.

So, coming back to our example, we need to analyse and visualize more data that has been provided to us or may be understand the life style choices of the female population. We need to dive deeper into the data and link in more and more information and understand why they are lagging in purchasing any product.

Suppose we also observe that sales of some of the products are booming during a particular season or a particular period of time. But why is that happening?

Also we can try to understand why the male population is more inclined to spend on things.

Similarly we can focus more on the category of products women are more interested in and stock up our store accordingly.

These are some of the cases where Data Storytelling contributes a lot because we need to find the reason for what we see and convey the same to the stakeholders in an effective way to assist in better decision making.

As a data scientist, we can always present our work with numbers and graphs. However, we need to know the exact message we are trying to achieve and communicate. That’s why data scientists are studying the data right! To squeeze some sense out of any information.

Various Tips for effective Data Story Telling

1. Have a well prepared Power point presentation or any document highlighting the main pinpoints, include enough graphs and charts and distinguish various insights using different color codes.

2. Understand the problem statement and the industry the problem belongs to. Having an understanding about the domain and the problem contributes a long way in framing the story.

3. Use statistically and numerically significant numbers .Understand why the information has high or low numbers. Frame the story about why large or small numbers are either good or bad in a particular context and what is the root cause that led to it. What action can be taken to improve the outcome?

4. Keep the context as simple as possible.

5. Last but not the least, help connecting the message or ideas with similar past real life experiences.

Why Data Story Telling is important

Now that we know what data storytelling is and also familiar with few of the tips to build an effective data story, let’s understand why it is important.

We will use a couple of examples for the same.

Example 1: Consider the current COVID-19 situation. Whenever we open any news article, first thing we notice are those colorful plots and charts.

Let’s say we have the below visualization. Data scientists and data analysts can easily understand the trends and the patterns along with those numbers.We can clearly observe that the cases suddenly rose exponentially allowing us to understand the intensity of the situation. These kinds of insights can help us in becoming more careful and take necessary precautionary measures.

Source: Google

Few important takeaways from the above plot:

1. The graph with title ‘Total CASES’ tells us how the cases increased suddenly around March and that the total number of daily cases around Jun 02 is around 130K.

2. Similarly, from the graph with title ‘ACTIVE CASES’, we can easily understand that the total number of active cases around Jun 02 are around 3M and that the cases suddenly rose exponentially around March 22.

3. Again from the graph with title ‘CLOSED CASES’, we can easily understand the recovery and death rate over the period of February to June as both of them are distinguished clearly by different colors.

4. From the graph with title ‘ DAILY DEATHS’, we can see that the count of deaths are gradually decreasing but was at a peak mid-April.

Now, because data scientists and data analysts have frequent exposures to these numbers, figures, various analysis tools, they can without any effort understand the above points just by a look at it but if a normal person sees these graphs, he won’t get a hang of what all these means. Hence, it is important to also write or tell a story explaining those colorful graphs in simple English to the business stakeholders or even the common people when any such situation arises.

Example 2: Another beautiful example of Data Storytelling I have encountered recently is ‘Flattening the curve’ via TrackCorona.

This animation of a fictional community clearly shows us some good statistics about the Corona Virus but just playing the animation will not help us in understanding what is happening.

Hence, the below few lines before starting the animation gives us an idea about it and also the reasons why it is happening. This is a perfect example of Data Storytelling.