Storytelling is one of the most important skills for a data scientist. It doesn’t matter how ingenious your models are, if you can’t build a story that other people can understand then your data isn’t doing anything to change the world.
Having said that, the burden of understanding how data works shouldn’t fall on the data scientist alone. Yes, you have the responsibility to build stories and to choose the right data visualization strategy. Still, one can only go so far when other people don’t understand even the basics of data analysis.
Beyond The Indicator
Did you know that there is a mathematical correlation between the rise of temperature in the Caribbean seas and the reduction of country wise email marketing list piracy in the area? It may sound like a joke, but it’s not, the correlation is there.
That’s what it’s traditionally called a spurious correlation. It’s what happens when you can establish a mathematical correlation between 2 or more indicators, but that correlation doesn’t make sense in the real world.
Linking pirates and climate change, funny as it sounds, is an extreme example of something that happens all too often—taking numbers at face value. Long gone are the days when an executive summary was a single-page report with a single indicator and a recommendation. We live in a data culture, and you have to adapt.
A (Data) Cultural Revolution
Studies have shown that companies boosting sales with pipeline velocity that embrace a data-driven culture tend to show growth in both output and productivity between 5% and 7% in the short term. This translates to annual growth of up to 30%.
Despite its many benefits, embracing data culture has been an uphill battle. In a recent survey, over 74% of managers reported that they haven’t been able to implement a data-driven culture in their businesses. Ironically, the same number of managers reported that they fear business disruption by data-driven competition.
In other words, we understand the importance of data, but we haven’t managed to find the right strategy to push towards widespread data-driven adoption.
Introducing Data Literacy
Data literacy is the ability to read, write, and communicate data. This includes an understanding of data sources, analytical brazil data methods, constructs, data cleaning, and statistical models.
To take it one step beyond, it also encompasses the ability to describe use cases and applications, as well as to build stories from data.
Data literacy, like any skill, can be learned, and it comes in different levels of expertise. Not everyone in your company needs a Ph.D. in statistics to be data literate. It’s enough to understand the basics, to know how to ask the right questions, and to know where to look for answers.