Why storytelling is needed in data

Gillis and Laskowski provide an excellent definition of data storytelling and why it is important. Often we use data to drive decisions, create action or influence opinions and positions. This may be a decision about a performance marketing strategy, about a financial investment or transaction, about how much raw material to buy or how fast to run the engines. As data professionals we all know and hope that decisions are based on data and enabled by data, if not actually data driven. One problem that we have, however, is that not all decision makers are sufficiently data literate to understand or interpret the raw data that is put in front of them, either in the dreaded spreadsheet or on the equally dreaded ‘dashboard’. It is an even greater leap to expect business decision-makers to understand the output from a ML model or indeed the model itself or how it was trained or how it is calibrated. To get around these issues data leaders have increasingly realised that they need to be interpreters or narrators so as to make the data understandable and to enable engagement with insights. Scott Taylor, The Data Whisperer, is one of the leading practitioners and evangelists for data storytelling. It is all about connecting the dots, creating the narrative that brings the data alive to impact on decisions and actions. We often talk about ‘actionable insights’, but if the insights don't create action then they are of little value.

What we are talking about here is using the power of data storytelling to influence operational decisions for the course of the business.

That, however, is only one aspect of the storytelling. Often data leaders need to create the narrative that builds the connection between a data activity and a business outcome.