Storytelling is one of the oldest forms of art. Since the very beginning of mankind’s history, storytelling has been the most powerful and communicative way to share information. This particular type of communication differs from reading and writing as in storytelling the result is adjusted according to the audience and ever more to teller’s skills. This means that a fact that that has already happened, or told in case of imaginary stories, is being reproduced in specific way by the teller for specific audience. Thus, the story contains characteristics that improve the transmissibility and successfully share the message in a more efficient way (Smith, 2015).
However, the word “storytelling” is often used in many ways. This article defines the concept of storytelling as a way of transmitting a message in an entertaining and memorable manner.
According to the international literature, in order for an act to be characterised as a storytelling one, this act should have certain features (National Storytelling Network, 2017). Thus, an act is characterised as a storytelling performance act if it:
  • Presents a story that has a beginning, a middle and an end
  • Uses words and actions that interact with the audience
  • Encourages the active imagination of the listeners
  • Communicates the message that the audience will come away with
  • Involves the audience to the story
  • Makes the audience wonder about the next parts of the story
  • Imprints pictures on audience’s minds
  • Avoids too many complicated words and too much information
  • Its content is illustrative and easily memorable

From Storytelling to Data Storytelling

Many definitions have been stated for the term of data storytelling. One among them is the definition that Howard Dresner published in July of 2015. According to Dresner’s definition, data storytelling is described as a set of features within visualisation tools that enable a more interactive experience with the data. Moreover, Dresner points that data storytelling is the next big thing in collaborative computing (Rouse, 2015).
In the age of information, people are facing an increasing shortage of time and an increasing range of choices. On top of that, people need to be informed in an efficient way. This means that in most cases, the way in which information is provided becomes even more important than the information itself. At this point is where the use of a storytelling approach is able to create the expected results. Storytelling can be both efficient and powerful, when it contains all its critical features (Kumar, 2014).

Parameters for a powerful Data Storytelling

It is a fact that data storytelling is more and more used when it comes to data visualisation and information sharing in general. Journalists, designers, developers, consultants, sellers, scientists, marketeers and other professionals benefit from the use of data storytelling, as it is an efficient way to communicate the knowledge that is extracted from a set of data.


In general, as with storytelling, a successful data storytelling is characterised by specific features. In particular, a successful data storytelling should contain the features mentioned before and in addition it should:
  • Have beginning, middle, climax and conclusion
  • Be simple and use labels
  • Work with questions
  • Contain interactive visualisations
  • Provide context and directions
  • Be narrative and as much close to a linear experience
Thus, a data storytelling application should not use purely reader-driven visualisations as used in dashboards and usual reports. In contrary, it should use interactive graphs and features that encourage the participation of the user. Also, its structure should contain parts as beginning, middle, climax and conclusion. The beginning could be about the data sources and a short description of the data set. The middle and climax could be questions to be answered and pointing picks respectively. Finally, the conclusion could be a short explanation of the results and contains the message that the story is about.
Also, it is a fact that the more different parts of the brain are activated the more memorable and impactful a story is. This means that the content should avoid the extensive use of numbers as much as possible and replace them with simple and everyday words with clear meaning. Although, there are occasions where figures can speak for themselves and these kind of replacement is not necessary.
Data stories should not be treated as exploration tools. They are rather narrative experiences that provide context and direction, not just numbers and charts. Moreover, the key point of a successful data storytelling visualisation is to include all the components to allow the audience to read it in a linear story mode. The more linear an experience is, the more it feels like a story to the audience.
A successful data storytelling presentation is equivalent to its simplicity. Moreover, it should work with questions, labels on graphs and interactive visualisations at each segment of the story. As with every story, also in data storytelling it is very important to know which are the key points of the story, what is the knowledge extracted from the data and what is the audience need to understand. Based on this, a detailed analysis on the data set is always a crucial prerequisite.

Data Storytelling and Analytics

In order to create a data storytelling, data set should be initially analysed and processed. The results of this process will be then used as input in the data storytelling application. Based on the purpose of the analysis, the complexity  and the type of the data set, the analysis could be anything from a simple statistical analysis to a more complex process that might be using machine learning techniques. And that is what Data Analytics is all about.
Data Analytics is “the process of examining raw data with the purpose of finding patterns and drawing conclusions about that information by applying mining methodologies to derive insights” (Monnappa, 2017). Also, Data Analytics is defined as “the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialised systems and software” (Rouse, 2017). Either way, Data Analytics is a series of actions in order to get from raw data to meaningful conclusions. These techniques are widely used in various industries. They play an important role on decision making process as they suggest actions and improvements on company’s strategic plans based on the analysis results.
Data storytelling is closely connected with Data Analytics concepts. As mentioned in previous paragraphs, a successful data storytelling, among other parameters, should also contain climax. The climax of a data storytelling is the moment when the major and most important result of the analysis is illustrated. There are cases where the results are given or can easily be found. However, there are cases where the results are not obvious and they are hidden in the data set. In these cases, the story can become extremely interesting and for sure innovative.
Therefore, a good data storytelling also requires the knowledge of retrieving unexpected results from a set of data in order to emphasise and visualise these findings at the appropriate moments of the story flow.


  • Kumar, P. (2014). Data Storytelling: A Definition? Retrieved from [source]
  • Monnappa, A. (2017). Data Science vs. Big Data vs. Data Analytics. Retrieved from [source]
  • National Storytelling Network, (2017). What is Storytelling? Retrieved from [source]
  • Rouse, M. (2015). Data storytelling. Retrieved from [source]
  • Rouse, M. (2017). Data analytics (DA). Retrieved from [source]
  • Smith, A. (2015). February 25, 2015. The Power Of Storytelling. Retrieved from [source]