The Power of Data via Storytelling

A recent social function I went to… 

Party Host – “So, what line of work are you in Alistair?”

Me – “Ummm Data, insights and helping our clients find profitable new customers…that sort of thing.”

Party Host (Colour draining from face) – “Oh data, how interesting…”

Me – “It is actually!!! It still amazes me that since man walked the Earth, data and it’s interpretation has been the foundation of every decision ever made… good or bad!

I’m not sure I totally convinced him on the marvellousness of data but no doubt my enthusiasm on the subject lightened the topic for him!

The Power of Data

Every day, each one of us analyses the information (or data) we receive  and then makes a decision based on that data. Some decisions are small, some are big.

I recently watched a wonderful movie - The Big Short - which follows the story of hedge fund manager, Michael Burry, and acknowledges his foresight of the GFC. By using data, and correctly interpreting it, Michael convinced many of Wall Street’s largest and well known finance houses to sell him credit default swaps against what he knew to be ‘vulnerable’ sub prime deals.

In layman’s terms, Michael Burry strolled into Wall Street with $1.3 billion and put it all on Red…the only difference being that in Wall Street’s history, Red had never come up as a winner. As far as Wall Street was concerned, this was easy money!

By understanding and correctly interpreting the data he had uncovered, Michael Burry realised a net return of $1 Billion (AUD) on top of his original $1.3 billion wager.

Now how cool is that story!

For some data can be as dull as watching paint dry. So when you are using data as part of your marketing conversations try to delivering it in the form of a story instead. Giving them a start , a middle and one hell of an ending – as in The Big Short – and you’ll no doubt shine a little light on the beauty and importance of data.

For 5 tips on how to tell a great story with data here’s a great article by Jim Stikeleather that may help you out in that next marketing meeting (or party conversation if you’re as passionate as me on the topic ;). 

How to Tell a Story with Data

An excellent visualisation, according to Edward Tufte, expresses “complex ideas communicated with clarity, precision and efficiency.” I would add that an excellent visualisation also tells a story through the graphical depiction of statistical information. As I discussed in an earlier post, visualisation in its educational or confirmational role is really a dynamic form of persuasion. Few forms of communication are as persuasive as a compelling narrative. To this end, the visualisation needs to tell a story to the audience. Storytelling helps the viewer gain insight from the data. (For a great example, how much do you think steroids have influenced baseball?)

So how does a visual designer tell a story with a visualisation? The analysis has to find the story that the data supports. Traditional journalism does this all the time, and journalists have become very good at storytelling with visualisation via infographics. In that vein, here are some journalistic strategies on telling a good story that apply to data visualisations as well.

1. Find the compelling narrative. Along with giving an account of the facts and establishing the connections between them, don’t be boring. You are competing for the viewer’s time and attention, so make sure the narrative has a hook, momentum, or a captivating purpose. Finding the narrative structure will help you decide whether you actually have a story to tell. If you don’t, then perhaps this visualisation should support exploratory data analysis (EDA) rather than convey information. However, for the designer of an exploratory visualisation it is still important to spark the viewers’ imagination to encourage examining relationships among and facilitate interacting with the data – think gameification.

2. Think about your audience. What does the audience know about the topic? Is it meant for decision makers, general interested parties, or others? The visualisation needs to be framed around the level of information the audience already has, correct and incorrect:

• Novice: first exposure to the subject, but doesn’t want oversimplification

• Generalist: aware of the topic, but looking for an overview understanding and major themes

• Managerial: in-depth, actionable understanding of intricacies and interrelationships with access to detail

• Expert: more exploration and discovery and less storytelling with great detail

• Executive: only has time to glean the significance and conclusions of weighted probabilities

3. Be objective and offer balance. A visualisation should be devoid of bias. Even if it is arguing to influence, it should be based upon what the data says–not what you want it to say. Tufte found numerous charts that misled viewers about the underlying data, and created a formula to quantify such a misleading graphic called the “Lie Factor.” The Lie Factor is equivalent to the size of the effect shown in the graphic, divided by the size of the effect in the data. Sometimes it is unintentional-a number that is three times bigger than another will be perceived nine times bigger if represented in 3D. There are simple ways to encourage objectivity: labelling to avoid ambiguity, have graphic dimensions match data dimensions, using standardised units, and keeping design elements from compromising the data. Balance can come from alternative representations (multiple clustering’s; confidence intervals instead of lines; changing timelines; alternative colour palettes and assignments; variable scaling) of the data in the same visualisation. Maintaining objectivity and balance is not a trivial effort and is easily unintentionally violated. Viewers and decision makers will eventually sniff out inconsistencies  which in turn will cause the designer to lose trust and credibility, no matter how good the story.

4. Don’t Censor. Don’t be selective about the data you include or exclude, unless you’re confident you’re giving your audience the best representation of what the data “says”. This selectivity includes using discrete values when the data is continuous; how you deal with missing, outlier and out of range values; arbitrary temporal ranges; capped values, volumes, ranges, and intervals. Viewers will eventually figure that out and lose trust in the visualisation (and any others you might produce).

5. Finally, Edit, Edit, Edit. Also, take care to really try to explain the data, not just decorate it. Don’t fall into “it looks cool” trap, when it might not be the best way to explain the data. As journalists and writers know, if you are spending more time editing and improving your visualisation than creating it, you are probably doing something right.

By Alistair Malloy, Account Director, ContactAbility

Source: Harvard Business Review

ContactAbility specialises in customer acquisition and can reach more than 12 million prospects. To find out how we can help you reach new segments, why not drop me line at alistair.malloy@contactability.com.au today.