Data scientists in current times are evaluated on the basis of their evolving knowledge and skills with the emergence of new technological features. As the data environment continues to grow, coding skills have become increasingly vital. Along with such developing skills nuanced needs emerge with the rise of technology over the time. Evolution of data-driven digital business has made data storytelling a new born yet much-talked skill among data science and analytics community. In simple words, data storytelling is the process of translating data analyses into layman's terms so that non-expert people can understand the essence in order to influence business decisions and actions. The basic idea behind it is to connect dots between sophisticated data analyses and decision makers who may or may not be experts or lack the ability of data interpretation.

While working with data and analyzing its outcomes and insights, a lot of challenges can overhaul the process including pre-established conclusion, cognitive bias, oversimplification of output and convenience sampling. While pitching the most understandable notes of a data, interpretation is important for better decision-making, as oversimplification can cause misinformed decision. This is where data visualization and storytelling comes into being.

Below are the best practices to follow regarding data science storytelling:

• Involving stakeholders in the creation of analytical narrative can help reduce information loss. It gives complete and clear understanding of the conclusions and lessen the loss of potentially important degree in the final decision.
• Careful consideration of data and analytical method is necessary to support factual precision and protect from sampling and other biases.
• It is extremely critical to aim for simplification that leads to correct decision because oversimplification may change the decision which can cause severe shortcomings.
• Data visualization should be used for story telling but not to eclipse the critical highlights in the argument.

According to some experts, narrative is a method to simplify and make sense of a complex approach while simplifying context, insight or interpretation. Data storytelling is a feature within data visualization tools which enable a better interactive experience with data. A number of business intelligence tools now include some extent of data storytelling to attain their core purposes. Some even consider it as the next big thing in collaborative computing. The examples of data storytellers in the organisations is at the infancy of emergence.

Many businesses and their bold and impressive insights may suffer an unfortunate fate if they do not get successfully molded into data stories. Designing and drafting key insights is one thing but to communicate them to other is essential too. In fact, both are equally important to derive value from the data operated in an organisation. Data storytelling depicts an exciting, interesting and fresh field of expertise where creativity and science converge at one platform. It is being anticipated that more and more data storytellers will emerge in the market to ensure survival and adoption of more incredible insights.