The chances are, your business has already launched Big Data projects and has started collecting all kinds of data. The next elusive step is to extract value from your investments in data projects. Your data may hold tremendous potential, but not an ounce of value can be created unless the data is analyzed, actionable insights extracted and translated into concrete business process action items.

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.

Companies are desperately searching and recruiting data science talents. Recently, LinkedIn reported that data analysis is one of the hottest job categories and it is the only category that consistently ranked in the top 4 across all of the countries they analyzed.

However, much of the current hiring emphasis is centered on the data infrastructure and preparation a.k.a. data engineering, rather than on the skills that convert analysis to insights and actions. From my experience, many data scientists with advanced degrees in economics, mathematics, or statistics find effectively communicating their analyses and insights with other business stakeholders and interpreting the significance of their sophisticated analyses within the business context particularly challenging.

According to McKinsey,

Organizations need specialists, or “translators,” who can analyze, distill, and clearly communicate information of the greatest potential value.

In other words, the need for data storytellers is only going to increase in the future[1].

With more self-service business intelligence tools, such as Tableau and Microsoft Power BI, the pool of business users generating insights will grow beyond just data analysts and data scientists. Correspondingly, we will witness an unprecedented growth in the number and significance of insights being generated than ever before.

Unless businesses improve the communication and collaboration frameworks around these insights, they will experience a poorer insight-to-value conversion rate. If an insight isn’t properly understood, is not compelling or actionable, no one will act on it[2].

Often times, data storytelling is equated to building compelling visualizations. However, data storytelling requires a structured approach for organizing and communicating the insights from data[3]. It requires balance among three core components – data analysis, visualization, and narrative.

Storytelling Venn Diagram

Storytelling Venn Diagram

It’s important to understand how these different elements combine and work together in data storytelling. When you combine the right visuals and narrative with the right data, you have a data story that can influence and drive change.

Think Insights (August 12, 2022) Data Storytelling. Retrieved from https://thinkinsights.net/data-literacy/data-storytelling/.
"Data Storytelling." Think Insights - August 12, 2022, https://thinkinsights.net/data-literacy/data-storytelling/
Think Insights November 17, 2017 Data Storytelling., viewed August 12, 2022,<https://thinkinsights.net/data-literacy/data-storytelling/>
Think Insights - Data Storytelling. [Internet]. [Accessed August 12, 2022]. Available from: https://thinkinsights.net/data-literacy/data-storytelling/
"Data Storytelling." Think Insights - Accessed August 12, 2022. https://thinkinsights.net/data-literacy/data-storytelling/
"Data Storytelling." Think Insights [Online]. Available: https://thinkinsights.net/data-literacy/data-storytelling/. [Accessed: August 12, 2022]
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