Analytical results are often interpreted differently by different people. Sometimes the conclusions presented don't align with intuition. Differences in experience and expertise can also come into play. An effective way to align thinking is through data storytelling, although there are better and worse ways to do it.
Data storytelling typically includes text, visualizations and sometimes tables to illustrate a developing trend or issue that requires attention if not action. Data storytelling can make the results more memorable and impactful for those who hear it, assuming the presentation is done effectively. Following are a few things to consider
Data scientists are often considered poor data storytellers because they struggle to align a story with the needs and knowledge level of the audience. Sometimes others are brought in to translate all the technical jargon into something that that is meaningful to business leaders.
Similarly, different parts of a business may require a slightly different focus that uses different language and maybe even different types of data visualizations to have the desired effect, which is understanding analytical results in context.
Effective stories have a beginning, a middle, and an end. The beginning of a story provides context, setting the stage for the story itself. The middle tells the story, and the end usually includes a set of possibilities. Getting the end right is important because insights without action have little value. Are there actionable insights from the data? How can the results be used to drive strategy? In a business context, is there a significant revenue opportunity or an opportunity for cost savings? How much more likely is it that one course of action will succeed versus another? If you provide curated data points and visualizations that support the key points, you can often pre-emptively address the most likely questions and objections.
Effective storytelling also address issues beyond the "what." Take a sales situation for example. Heads of sales are constantly monitoring progress against sales targets. Let's say sales fell short or exceeded expectations last quarter. That leads to other questions such as why were sales better or worse than we expected? How could we use those insights to turn the situation around or increase sales even further? How well do we understand our customer base and their requirements? What levers work well and which don't?
With some solid analytics and effective data storytelling, everyone in the room -- the head of sales along with the C-suite or her team can have a common understanding of what impacted the sales results, why, how things are changing and what that means going forward, for the sales team, products, marketing, etc.
Data storytelling should also explain why the analysis was performed, how the analysis was performed, whether hypotheses were proven or disproven in addition to the important findings and what those findings mean for the audience. Some people make the mistake of showing the many steps required for an analysis to demonstrate how challenging the exercise was, which adds little, if any, value.
Great stories can be derailed by simple mistakes, such as misspellings, a lack of focus and a propensity to demonstrate the mastery of a software program to the point of distracting the audience.
Misspellings and grammatical errors tend to be addressed by modern software; however, they don't always catch everything. Some of them have default settings that limit the amount of text that can be included; however, that's usually configurable. Sadly, it' possible to overload stories with so much noise that the audience has trouble staying focused. The point is not clear, in other words. Similarly, trying to get too creative with the colors used in data visualizations can detract the audience's attention away from the point.
Also consider the presentation of the data in relation to the data itself. On a scale of one to two, a move from one to two reflects a 100% increase. On an actual scale of 25, 50, 100, or 1000, a single-digit increase would appear differently.
One of the reasons businesses have placed greater emphasis on analytics versus traditional reporting is the ability to interact with data versus passively consuming it. There is a parallel with data storytelling which is a move away from the traditional and static business presentation format that tends to reserve questions for the end to interactive storytelling in which questions or alternate points of view can be explored live.
Generally speaking, data storytellers should be prepared for questions and challenges, regardless. Why wasn't something else explored? If a particular variable were added or subtracted, what would the effect be? Of the X possibilities, which is the most likely to see and why?