Once your business has started collecting and combining all kinds of data, the next elusive step is to extract value from it, noted Forbes. Your data may hold tremendous amounts of potential value, but not an ounce of value can be created unless insights are uncovered and translated into actions or business outcomes. During a 2009 interview, Google’s Chief Economist Dr. Hal R.Varian stated, “The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades.”
This is what happening in present times. Summing up the abilities predicted by Dr. Hal we can say that from data analytics to data storytelling, the data-driven industry has advanced rapidly.
The evolution of data-driven digital business has made data storytelling a new entrant yet much-talked ability among the data science and big data analytics community. It is a process of translating data analysis into layman’s terms. This simplification can help non-expert people to understand the essence in order to influence business decisions and actions. The basic idea behind data storytelling is to link dots between sophisticated data analysis and decision-makers who may or may not be experts or probably not well versed with skill of data interpretation.
While working with data and analyzing its outcomes and insights, a lot of challenges can overhaul the process including the pre-established conclusion, cognitive bias, oversimplification of output, and convenience sampling. While pitching the most understandable notes of a data, interpretation is important for better decision-making, as oversimplification can cause misinformed decision.
“How organizations deliver business analytics insights are evolving, notably in the rising use of what is called data storytelling,” says James Richardson, Senior Director Analyst at Gartner. “Data and analytics teams have always created dashboards and visualizations, but many are unfamiliar with wrapping those artifacts into a narrative.”
Data stories explore and explain how and why data changes over time, usually through a series of linked visualizations. Although visualization is almost always a key element in data stories, it is only one piece of a three-part strategy.
As formulated by Gartner, ‘Storytelling = visualization + narrative + context’
Self-service BI and analytics platform users now have access to a range of capabilities to help them create compelling data stories. They use an array of data visualization forms, ranging from chart types to geographic mapping, and more varied and sophisticated charts such as heat maps and candlestick charts.
It is important to note that no one visualization works for all situations. Data and analytics storytellers must choose a fitting visualization based on the kind of data they want to present and the audience to which they want to present it. Arranged into a time or conceptual sequence, these visualizations can be shaped into a narrative to help reveal findings, trends, or underlying patterns.