Data storytelling is a method of taking complicated data analyses and presenting them in a way that is tailor fit to the intended audience in order to assist in complex business decision-making. Gartner defines data storytelling as “visualization + narrative + context”.
In the past, data storytelling was a data analysis method restricted to data scientists or analysts. This has now changed with the introduction of self-service business intelligence (BI) tools such as AnswerRocket.
Data Visualization as a Tool for Storytelling
Most effective data stories begin with a relevant visualization. Relevance refers to both the visualization’s depiction of the data as well as its usefulness to the audience in question.
For example, a relevant visualization of regional sales figures could employ a map that illustrates these numbers intuitively. For a salesperson who wants to use the visualization in a presentation, the map may be perfect.
For the team member who wants to dive deep into the nitty-gritty numbers, a pivot table may be more appropriate. A visualization tool that’s relevant, and therefore effective, should provide customization options so that said team member can quickly pick the chart type that makes the most sense.
Finding a Data Narrative Using BI Software
Your data story is not complete without a proper narrative.
Historically, data narratives were culled together and put into a report by data scientists and other analytics experts. Then, the business user would be left to draw conclusions and build out data stories from that predefined narrative.
Now, with the help of AI and machine learning, a non-technical user can go straight to their business intelligence tool and ask their first question. For example, a sales leader might be interested in, “What were sales by territory for Q4?” The ability to ask an every day business question in conversational, organic speech is the beauty of natural language processing, one of many technical advancements brought by AI.
Once that answer is generated in a matter of seconds, the user can leverage results to go further down the process of building out a data story. AI speeds up this storytelling process immensely.
Context for your Data Story
When storytelling with data, the audience should be considered when choosing the way data is framed or positioned.
“It’s the context around the data that provides value and that’s what will make people listen and engage” – Gartner.
The context of the data story will help guide the audience to an elevated understanding of the data being presented.
For example, an increase of 10% of sales in Q4 will look promising until provided the context that the goal of Q4 was a growth of 20%. Further, positive trends can overshadow stagnating metrics and opportunities for immense growth if taken at face value.
It is also important to consider who you are building the story for, as a salesperson is likely to have more interest in data that’s actionable for their role than in data that’s targeted to the finance department.
As we discussed before, the ability to ask questions in natural language dramatically increases access for non-technical people, allowing them to tailor data storytelling to their own needs and interests.
Data without context is just that, just data. It can contain certain insights, but these insights can only be unlocked through the use of context. Random data points will mean nothing to the audience or user until you show them what to look at and what to compare them to.
The Importance of Storytelling with Data
Data storytelling can assist non-technical members of an organization by simplifying complex sets of data into digestible, relevant content.
It can empower entire organizations down to the employee level to make informed business decisions to optimize business operations.
AnswerRocket and Data Storytelling
Through the use of an AI analytics tool such as AnswerRocket, data storytelling can be largely automated. AnswerRocket is a query-based data analytics software that can automatically detect trends and other insights based on the questions you ask.
The software is also simple enough for anyone in an organization to use by implementing natural language processing (NLP) and natural language generation (NLG) in the platform itself. This means that a user can ask a question of their data in natural human language, and the system will output an answer in the same easy-to-understand language. U
pon asking questions of your data, AnswerRocket will use machine learning algorithms to analyze a large scope of data to uncover many different insights that may not be visible on the surface level.
These insights can provide a narrative to your data story and answer the “why” questions the user may pose. The platform will also perform analyses on your data to discover the perceived best visualization based on the question asked.
AnswerRocket also provides the ability to continue to ask questions that stem off the first initial query in order for the user to come to a productive conclusion, which is an essential component of data storytelling. In short, using AnswerRocket can provide full data stories and data storytelling tools when asked just one question.
