Part 1: 4 reasons why data-based storytelling is key (but we suck at it)

Data and analytics.

An organisation would have to be downright bonkers not to use the insights derived from these to make strategic decisions. Many aim to. But even more fail. Why is this?

Successful analytically-inclined people tell stories with data, with the intention of persuading the audience to take a particular course of action. However, studies have shown prolific data and analytics lovers traditionally have much 'room for improvement' when it comes to their communication and influencing skills.

I'll be dedicating a few posts to this topic. This initial article covers why data-based stories are so important. 

Without further ado. The importance of data story-telling is underscored due to the following:

1. Who doesn't love a good story? I wrote about the effectiveness of stories to provide context, insight and interpretation in a previous post. Stories have always been powerful tools which humanise experiences. Data and analysis-heavy stories are contemporary versions of fables and narratives - they enrich, provide meaning, and outline relevance to the data being presented. In essence, they bring the data to life by allowing the audience to picture it playing out in reality.
   

   "Storytelling is still fundamental to our experiences. Despite the incredible explosion of technology, we all still love stories." - Sir J Hegarty

2. Analysts primarily aim to influence decisions or actions backed by data. The majority of decisions are emotionally-charged, to a lesser extent supported by logic and reasoning. To persuade and compel behavioural change, you must first influence the audience's attitudes and mindset. Maybe it's an infographic, a storyboard or a narrative - whatever its format, a story which engages the audience and communicates what you have done and why is essential. We are emotive creatures. No matter how much we deny it, both emotional and rational perspectives come into play during the decision-making process. 
3. Your average meeting participant won't be well-versed in detailed analytics. Nor will they need to be. What they do need is evidence. The most compelling of stories combine data, analytics, comparable real-life examples and powerful if/then arguments (e.g. IF we do a, THEN b will occur because XYZ company of similar size and industry realised these outcomes. This is supported by findings in graph x and our analysis captured in table y). By including these factors, your audience is better able to connect the dots and follow your thought process. 
4. The devil is not always in the detail. Top-tier analysts spend hours poring over data, dissecting and hypothesizing till the wee hours of the morning. You need to prepare 'cliffs notes' versions for the receivers of this data, taking into account their decision-making style and your objective - is it endorsement? Approval? An FYI or decision? Stories are gold in articulating key findings from an analysis in style. I used a visually compelling infographic to present the concept of a smart city initiative - the objective was for C-suite to 'green light' funding. It was unanimously approved to progress to the next stage. Audiences may bicker over write ups and business cases. A picture really does say a thousand words - agreement is easier to obtain with an unambiguous visual. No more death by powerpoint.  

The majority of quantitative analysts are not very good at creating or telling stories. The implications are significant. Analytical initiatives don't produce the desired decisions or actions that they should. Let's compound the effects of this little nugget. How much time and money spent on acquiring, managing and analysing data are going down the gurgler in your organisation? 

Stories are just data with soul.

I'll outline the reasons behind our dismal track record at data-based story telling in the next post, and provide suggestions for improving the persuasive influence of your stories.

Till then! 

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