The Secret to Data Democratization: Data Stories Told by Machines
By: Brooke Landrum
“To take data—to be able to understand it, to process it, to extract value from it —that’s going to be a hugely important skill in the next decades.” – Dr. Hal R.Varian, Google’s Chief Economist
Communities of analysts have used the concept of data stories as their north star to process, visualize, and share data.
Data storytellers aim to make sense of data and share it with their users — this is typically done by weaving charts and graphs together in a compelling format or by manually summarizing data in a report.
As a result, the analytics and BI market is ~$25B and growing 20% a year.
However, the usage of the massive BI investment remains low. Only a quarter of the employees who have access to analytics tools use them on a consistent basis.
The reality: Visualizations don’t tell stories. People do.
Donald Farmer, former Microsoft and Qlik executive, stated in our recent webinar that, “Visualizations require a certain set of skills to understand it and create it. So, you need to be able to move beyond visualizations as your only means of communicating data.”
Data stories are viewed as a key strategy to increase the usage and effectiveness of analytical tools within companies, but they need to move past visualizations or manually reporting.
How can we improve? Enter Data Storytelling Technology.
Data and Analytics leaders should build storytelling competencies within their teams while also looking for ways to automate data storytelling.
Technology exists today that can identify and deliver simple data stories with the need for a manual effort by analysts. This output is typically in the form of a data story within a data visualization.
For example, Quill, allows you to scale your data storytelling capabilities instantly by simply adding an automated written summary to accompany all of your visualizations. No matter how great of a data storyteller you are already, it can be hard to keep up with the ever-expanding data needs for people across the organization.
By dragging Quill into your BI tool of choice, you can set the dimensions and measures you’d like to translate into human-sounding stories.
As a data storyteller, not only does this written story clearly communicate the data trends to your end-users, but it can also inform the types of charts and graphs you’d like to accompany the story in the dashboard.
There are also a growing number of data storytelling automation tools that can handle more complex analytics and storytelling techniques.
For example, Lexio, takes a more extreme data storytelling stance – it takes data, analyzes it, and turns it into stories written in plain-Language. Instead of a dashboard, users interact with a newsfeed-style experience and longer-form stories to inform their decision-making. For less data-savvy and on-the-go users, stories can provide a better experience than dashboards alone.
Lexio end-User David Kemp, EVP Global Sales for Fike stated that, “[Lexio] just tells you what the data is telling you in plain-not-up-for-debate-100%-objective-English.”
This type of automated data storytelling eliminates the work needed to prepare data in order to put it into action. It even suggests areas to explore further.
Data and Analytics leaders should be evaluating these advanced tools as part of their always evolving Analytics and BI portfolio.