As the saying goes, a picture is worth a thousand words. But in today’s world of big data and visualizations, a picture often means a thousand additional words of interpretation and explanation for those users not skilled at fully unlocking the picture’s potential. And when the underlying data changes, causing the picture to change, even more words are required to explain what happened.

Make no mistake, visualizations can provide extraordinary insights into data locked in spreadsheets and databases, but they face limitations when it’s time to move from viewing to understanding to explaining. Fortunately, we are now at a time where non-technical users can begin to reap the benefits from the massive investments companies have made in big data and analytics, thanks to innovative natural language technology that tells the stories trapped in data.

Storytelling, with Words

In a strange and ironic turn of events, the visualizations that promised to be an easy way to turn data into insights have become incredibly hard to use. Visualization and dashboard companies touting “data storytelling” capabilities through pixel-perfect dashboards and interactive graphs are delivering heat maps and dot distribution maps, leaving users to wonder things like, “Why are there more red dots than blue dots this month?” “What do the larger bubbles in these overlapping bubble charts mean?”

After some time spent interpreting the patterns and anomalies within visualizations, the user may surface the golden nugget of insight she was searching for, along with numerous additional questions she did not even think to ask. This is not necessarily a bad thing, but opening up a Pandora’s box of questions is only helpful when we have the explanations as to why these patterns and anomalies exist. It’s tough to tell the story when you’re not really sure what you’re viewing. In fact, there’s only one thing missing from current data storytelling capabilities (and it’s pretty crucial): the actual story.

The challenges of interpreting visualizations coming out of business intelligence platforms pale in comparison to the challenges of creating the visualizations themselves. Embedding data into the wrong visualization format or cramming unnecessary data into a dashboard can lead to misleading interpretations of the information and, subsequently, poor decisions. Due to the complexities involved in creating these dynamic displays of data, a significant investment is required to hire experts to construct and explain these graphs to business users. Not only are business users frustrated that they can’t easily access understandable information, IT is frustrated that they’ve spent a substantial amount of time building something that isn’t quite fitting the bill.

Business intelligence vendors are responding to these frustrations by bringing a flood of data discovery tools to market, enabling business users to access, analyze and visualize data on their own. Per Gartner, “Traditional BI and analytic models are being disrupted as the balance of power shifts from IT to the business. The rise of data discovery, access to multi-structured data, data preparation tools and smart capabilities will further democratize access to analytics and stress the need for governance.”

Although there’s a democratization of data and analytics underway, the democratization of actual information remains elusive. Data discovery tools still require a degree of data (and technical) literacy, which the knowledge worker or analyst may possess, but the everyday business user does not. What’s needed is not more self-service data access and analysis, but rather, applications and technologies that explain and report on all of the data in a natural way.

Self-service Narratives

Organizations are waking up from their visualization daydreams and realizing that there is a still a huge gap between interpretation and information. Luckily, innovative solutions, driven by artificial intelligence, are bridging this gap by delivering data-driven stories, providing the “why” in a way that pictures can’t possibly achieve. Through a unique blend of data analytics, reasoning and narrative generation, these stories are now being used to explain what happened, how to achieve desired outcomes, and even predict what may happen in the future.   

Self-service tools today typically operate with a drag and drop functionality – drag and drop a data set onto a canvas and receive analysis that prompts ‘discovery’ in the form of further iteration, slicing, dicing and interpretation. Imagine a similar model, but instead of receiving analysis which requires further action to translate that insight into information, you receive the information directly. A self-service narrative which explains in plain English the story the data is trying to tell. These solutions exist today and are being utilized by leading companies who are equipping their employees with tools to make them smarter and more productive.

Operationalizing Storytelling in the Enterprise

Advances in technology are empowering employees at all levels to have instant access to information that enables smart decision making without requiring expensive resources and tedious analysis processes. Now, everyday decision makers can receive plain language explanations of how their business is performing, with prescriptive recommendations on areas of opportunity and improvement.

Take for example the call center employee who is dealing with an unsatisfied customer. She may see in her dashboard that the customer complaining about mobile service is in the red area of the dashboard, at risk of canceling her contract, but she still does not really know why. With an accompanying narrative explanation, this employee can understand the context behind the customer’s sentiment, and most importantly, instruct her on the optimal offer or approach to entice the customer to stay.

Or the sales manager, who receives his regular sales report and notices that one territory is currently underperforming. He can drill down for more granular detail, but he probably does not have the time. Instead of interpreting the data on his team’s performance, he can receive a report in plain English containing the most relevant details regarding the current state of the business. When it’s time to report to management or across the organization, he can receive a summary of overall performance, highlighting what is most interesting and important. When it’s time to conduct an individual salesperson performance report, he can receive a custom narrative on each salesperson’s activities, with action-oriented recommendations on how they can improve.

These scenarios are only possible when organizations operationalize storytelling in the enterprise. They can do this by realizing that visualizations are part of the story, but not a substitute for explanations, descriptions and other natural language documents that enable employees to do their jobs, and do them better. The idea that everybody needs to be data literate and a skilled data analyst is ludicrous. Literate? Yes. Data literate? I don’t think so.  

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