The biggest challenge in data analysis is how we will expose data to the public in a simple and effective way to give easy and immediate results. To make a long story short, “how this data could be read correctly to lead the company’s decision based on solid data analysis?“.
Perception and interpretation from an aggregated data depends to variety of reasons. One of the main aspects is to consider the audience and the use case of the represented data. Creators/Authors attempt to create a bridge between the user’s conscious and the aggregated data by data visualization.
When it is the time for data visualization creation, the developers could follow some unwritten rules and search for the answers to some questions:
- What if the data should be exposed to a heterogeneous group of users?
- What is the best way to expose a data analysis?
- What approach should I follow to make this data “talk” to improve the user’s decision ability based on that analysis?
The result is the matter:
Achievement of the results can be classified in variety of categories, such as infographics, reporting and etc. Each of these categories have mutual and exclusive boundaries together. The importance of the audience’s diversity may alter the level of the usage, comprehension, and details for the different results. For arguing this matter, explicitly we put the emphasize on two categories:
- Data Storytelling
The general definition of a Dashboard is “a way to show a different type of visual data in one place”. We see Dashboards almost every day, the car dashboard is the best possible example: easy and accessible data that do not require any interpretation for speed, temperature, km traveled. Information that everyone knows and therefore does not need any further explanation.
More often we are urged to attract users by stimulating their interest in what they are seeing, without considering their background knowledge. In consequence, the data visualization will transfer to actions that are illustrated through data.
To make data interesting we need to let them talk and, what situation is better than telling a story with this data?
This is the basic concept of data Storytelling, guiding and directing the user through the data with narration and enriching it with infographics to ease the interpretation and increase the level of the analysis.
To better explain the concepts behind Dashboarding and Data Storytelling let’s use an example, the same dataset, but two different approaches:
“Dataset – Snowboard Coaches and master instructors in Italy, focus on gender equality“.
The visualization of data can be a purely analytical process, where charts are displayed in a logical sequence, and organized by meaning and importance. The structure should be followed by a path that users need to acknowledge to it. The classic path goes from the generic summary that drills down to more and more detailed information:
- How many instructors and coaches are distributed across the territory by level and gender, details by the license expiry date, and analysis by age and gender.
Or it can be a narrative story, with the same data which can be more descriptive, introducing graphic contexts, infographics, and explanatory texts to make the analysis more complete. Do not assume that users know the topics and the arguments should be treated in a way to be interpreted easily by a wide variety of audiences.
- What is the history of snowboarding in the winter Olympic games?
- What are the disciplines?
- How is the training of the trainers and coaches structured in Italy and which are the professional figures whome are qualified to teach?
- How is the figure of the coach distributed by gender?
Storytelling arises interest and guides any users through the data by integrating them with infographics that attract their attention by deepening the importance of information. Telling a story with data allows you to understand the reasons for analysis and therefore enforce the users to ask themselves further questions.
Even though the dataset is the same, the information shown in the two presentations is completely different. The first (Dashboard) is purely technical, analytical, and non-descriptive. The data are direct and interpretable only by insiders. We can say that we have many data and few descriptions.
We can therefore conclude that a pure dashboard while following the correct principles of data visualization, will not be understandable for everyone but only to a small group of users who already know the subject.
The reach of our data presentation depends on the number of users. A narration with the data can enhance user involvement and motivation to explore new analysis, while a dashboard is best for technical users seeking simple and direct access to all necessary information.