We keep hearing about data storytelling and its importance to making business decisions. Decision makers have come to depend on their analysts to provide them with the most up to date, actionable information based on data.

Unfortunately, most features to enable data storytelling are like PowerPoint on steroids. They are usually static snapshots of an analysis someone actually had to perform to tell the story in the first place.

The snapshots don’t often tell the full story of what’s interesting or important to the readers, and the data analysis required to generate them is typically very time-intensive so the resulting report is often difficult to interpret and a day behind. 

Enter Scrollytelling!

An attempt to solve the problem of static data storytelling made way for a recent trend called scrollytelling. Readers can view associated media, like videos and photos, interact with charts and be immersed in a dynamic experience while scrolling through on a webpage. It makes for a beautifully designed and immersive experience, but it isn’t quite suitable for a business environment where decision makers need a concise recommendation presented up front.

Both scrollytelling and common BI dashboards suffer from the same challenge, readers often wish they were accompanied by an an analyst to narrow down what’s interesting and important and require the reader to interpret the findings (sometimes in the wrong way).

The Art Behind Telling a Good Story

Beyond being a marketing buzzword, Data Storytelling careerists know what it takes to tell a concise, actionable, data-driven narrative. Classic data mining and knowledge discovery training always starts with identifying the business problem first, identifying clear goals or hypothesis to test, and then iterating through a data modeling process to select the right algorithms to produce recommendations (and then lather, rinse, repeat). As new statistical techniques were introduced and big data challenges emerged, these processes evolved but stayed generally the same.

Organizations have since been trying to capture and scale this practice more recently, as evidenced by newly trending roles in the uppermost echelons like Chief Data Analytics Officer. These roles are charged with rolling out a data science team within their organizations, albeit an easier said than done practice.  As this business practice has become more common, it has become apparent that 1) it is rare to find a single person that is the data science unicorn who is an expert across several specific skill sets and domains, and 2) there aren’t enough people in general to fill these roles to keep up with what the business demands.

Enabling Citizen “Data Scientists”

So what is the solution?  A term coined by Gartner, citizen data scientists are folks that are data savvy, though their main roles reside outside of the IT and Data Science teams. Gartner describes these people as “power users” who understand data, technology, and come in the form of Business Analysts and other similar titles. Also, they can become data savvy because they are using a new generation of self-service BI tools.

Citizen data scientists will enable the scalability of analytics across an organization and truly maximize the value of their data. As we’re seeing, data storytelling will continue to evolve but its importance to making business decisions will never change.

(About the author: Pia Opulencia is the Director of Platform at Narrative Science. She leads the development team building the next generation of Quill, an Advanced NLG platform that generates data-driven narratives at scale)

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