Eric Swayne on the Fundamentals of Good Data Narration

Leading up to SXSW, we’ll be doing Data Stories with SXSW presenters starting with Director of Product of MutualMind, Eric Swayne, who is speaking on “Scientist to Storyteller: How to Narrate Data.” (Gnip is hosting a SXSW event for those involved in social data, email for an invite that goes out in a month or so.) 

Eric Swayne

1. In your SXSW session, you’re going to talk about being more than a “data janitor.” What do you mean by this?

Data Janitor is a term that resonates with many Analysts currently, as they’re basically being used for maintaining the facilities: scrubbing data sources, pushing data into prescribed buckets, rolling out the same reports because they’re the reports “we’ve always done.”  These are still important, but we have to aspire to more. People that live in data analysis have the crucial opportunity for extracting meaning that transforms businesses through data-driven decisions, and it takes much more than just pushing out the monthly graphs and charts.

2. How important is visualization to good data narration?

Great data visualizations put incredibly powerful tools in the hands of Data Narrators that enable them to tell better stories, as well as extract insights from incomprehensible data sets.  However, it’s critical not to confuse the visualization WITH the insight – they are distinctly separate, and not necessarily dependent upon each other.  A simple pie chart that tells a CEO exactly what they need to know to make a good decision isn’t any visual tour de force, but it clearly gets the job done.  In all cases, visualizations should serve the story: the string of insights that lead to data-driven decisions.  When a good picture makes a good idea stick, that’s when you know the Data Narrator has done their job.

3. What are the trademarks of good data narration?
You’ll often see three key hallmarks:

1. True Insights – An insight tells me something I don’t know, that I need to know, and that I can do something about.  If the data story doesn’t include these three elements, it’s factual or irrelevant, not insightful.

2. User-Centric Approach – Human Interface Design isn’t just for UX professionals – Analysts need to become more adept at its principles as well.  Everything we say in a report or dashboard through form, color, size or spatial relationships carries meaning – whether we intended it to or not.  Data Narrators not only understand their story but also their audience: what they’re used to seeing, how they might be biased against certain ideas, and what assumptions they’re making based on what they see and hear.

3. Idea Inception – I would call this “stickiness”, but we have a much better term for it now, thanks to Christopher Nolan and Leo DeCaprio! Work from great Data Narrators shows an intent to focus the audience on an idea, and make sure they remember it. Data Narrators often focus not on the meeting where they present their work, but the NEXT meeting their audience is having, and whether they remember what was said and use it.

4. When marketers misinterpret data, what are the ramifications that you see?
Of course the ultimate impact of misinterpreted data is bad decisions, but it usually starts by creating bad stories. Urban legends pervade businesses just like any other culture, and they often sound enough like real data that they’re not questioned.  “Our site visitors click on blue more often,” “We’ve never had a good Q4 for product X,” “Twitter hasn’t driven sales for us like Facebook,” and on and on. These “data-ish” stories are particularly insidious because they’re often unquestioned assumptions, and voices that seek to pick them apart are often quashed as trying to “rock the boat.” Storytelling isn’t just a tool to be used for good or ill – it’s the default processing protocol for human brains. Where good, data-driven stories are NOT created, it leaves a vacuum that others will fill with whatever they remember.

5. What are your data pet peeves? What is the data equivalent of driving slow in the fast lane?

  • Confusing correlation with causation. I know, I know, we say this maxim so many times it should be the Data Scientist’s Golden Rule. But the fact is that it’s tremendously hard for humans to avoid this trap, particularly when correlations appear to validate the opinions we already have. This is why it’s incredibly important for us to question each others’ assumptions, and to be open to ours being questioned.
  • “Perfect” data. When charts show a straight line, or scatterplots neatly cluster, or r^2 results are incredibly high, I get suspicious. Nothing in nature is perfect, particularly when humans are involved – the reality is that while many of our behaviors can be consistent, they aren’t absolute.  It’s incredibly important that we use analytics and statistics to tell the story that the data tells us, not the one we want to say.
  • Trophy numbers.  When I start a new client engagement, I like to ask them what their Trophy Numbers are.  These are the stats and figures that are used to report upwards (and often justify jobs), but that we know have no inherent value.  Pageviews, Hits, Impressions, Potential Reach, and Asset Views are all often found in this category.  While these may be good symptoms of success, they almost always aren’t the way your business wins in the world.  Data Narrators don’t ignore these, but rather they lead clients on a journey from here to better KPIs that indicate real business success.

If you’re interested in more Data Stories, check out Gnip’s collection of 25 Data Stories.