Data Storytelling is a method for presenting data using a combination of visual and verbal techniques that are presented as a storyline where the story explains the context of the data, highlights key insights and may also present implications and recommendations.
The purpose of Data Storytelling is to improve data insights awareness, recognition and cognition, including providing additional context to the data, highlighting key insights and potentially offering additional narrative regarding indications, implications and recommendations. Importantly, data storytelling needs to include the data! And it should also include a narrative / storyline, visuals and where relevant, voice, video and animation.
Principles to Consider when Implementing Data Storytelling
Every data tale should follow the basic principles of good storytelling: to have a beginning, middle and ending. The purpose is to be concise, eliminate confusion and bias in order to improve engagement, cognition and when appropriate, pursue a call to action. Basic principles of storytelling are as follows:
- Determine the key insights / point that you intend to make: simple and compelling
- Understand your audience and their familiarity with the data / content
- Provide context: purpose, background, description and exploration
- Tell the complete story – focus on key message: why it’s important and what it means, using method such as purpose, context, key message, implication and call to action
- Reveal the data at several levels of detail, from a broad overview to the fine structure
- Closely integrate the presentation elements – data, visuals, voice, video / animation
- Consider motivations necessary to induce actions – what data / visual / storyline will cause the audience to act? What key fact(s) will the audience respond to the most?
- Consider providing a summary or recap to ensure cognition
Remember, data storytelling is about going beyond the facts, to engaging the audience regarding what understanding / “take-away” or emotion or action they should take. Data storytelling should lead to better understanding and action.
Primary Uses of Data Storytelling
Data Storytelling has many uses and there are many methods for viewing and presenting data in story form. Data storytelling is best used for the following situations:
- Audience needs to clearly understand what the data is telling
- Audience may not be aware of the the data or the implications indicated
- Data or insights may be new / unfamiliar to the user
- Data may be complicated or confusing without further explanation
- Decision and / or action needs to be taken / is recommended
- Audience may not agree to the recommended action to be taken
- Audience may take an adverse action in the absence of viewing the key information
Types of Data Storytelling Storylines
- Changing /Trending
- Drilling Down
- Drilling Up
- Comparisons: Similarities and Contrasts
- Patterns (e.g. 80/20 rule)
- Segment / Dissect the elements
- Aggregate the elements
- Anomalies / Outliers
Key Business Benefits of Data Storytelling
The main benefit of Data Storytelling is improved understanding of what the data is indicating in terms of context, core message / importance and implication – what the data is telling the user to understand, decide / plan and act.
Trends / Outlook for Data Storytelling
Key trends in the Data Storytelling are as follows:
Semantic Layer – The semantic layer is a common, consistent representation of the data used for business intelligence used for reporting and analysis, as well as for analytics. The semantic layer is important, because it creates a common consistent way to define data in multidimensional form to ensure that queries made from and across multiple applications, including multiple business intelligence tools, can be done through one common definition, rather than having to create the data models and definitions within each tool, thus ensuring consistency and efficiency, including cost savings as well as the opportunity to improve query speed / performance.
Video Infographics – The use of video with graphics is increasing, adding additional context and explanation to the data and the visualizations presented.
Real-time Visualization – With IOT and other sensor data being available, efforts are underway to present this data in real-time, showing patterns and changes as they occur using visualizations, particularly maps.
Data Storytelling Applications – While still in its infancy, there are a few applications touting the ability to create more automated and animated visualizations and stories combining data, text, voice and visualizations. Google Fusion is one example for developing and sharing visualization using maps.
AtScale and Data Storytelling
AtScale’s semantic layer improves data storytelling and visualization by enabling data queries and the results, including visualizations to be rendered faster via automated query optimization. The Semantic Layer enables development of a unified business-driven data model that defines what data can be used, including supporting specific queries that generate data for visualization. This enables ease of tracking and auditing, and ensures that all aspects of how data are defined, queried and rendered across multiple dimensions, entities, attributes and metrics, including the source data and queries made to develop output for reporting, analysis and analytics are known and tracked.