Misunderstood? Try Data Storytelling


Data visualizations help explain complex data, although individuals can and do come to different conclusions nevertheless. It's a significant problem for data scientists and data analysts, especially when they're trying to explain something important to business people.


Visualizations alone may be confusing.
Credit: Pixabay
Visualizations alone may be confusing.

Credit: Pixabay

Part of the problem is one's ability to communicate. Another problem is expecting too much from data visualizations -- specifically, the clear communication of an analytical result.

Data storytelling can help, because it goes beyond data visualizations. It also helps individuals think a bit harder about what the data is saying and why.

Are data visualizations dead?

Clearly not. They remain an extremely important part of turning data into insights, but they do have their limitations. The first limitation is that data visualizations don't always explain the details of what the data is saying and why. Another limitation, as I mentioned earlier, is the possibility of diverse interpretations and therefore diverse conclusions, which, in a business context, can lead to some rather heated and unpleasant debates.

A simple form of data storytelling is adding text to data visualizations to promote a common understanding. Like PowerPoint, however, it's entirely possible to add so much text or so many bullets to a data visualization that the outcome is even more confusing than it was without the "improvement."

The same observation goes for infographics. Bright colors, geometric shapes, and "bleeds" (the absence of a border) do little to aid communication when used ineffectively. It's important to avoid clutter if you want others to understand an important point quickly.

One complaint I hear about using data visualizations alone is that they lack context. Data storytelling helps provide that context.

How to tell a good data story

Humans tend to be storytellers naturally, whether they're explaining how a car accident happened or why they weren't home at 7:00, again. However, when it comes to telling data stories, it's easy to forget what an effective story entails.

An effective story has a beginning, a middle, and an end like a book or a movie. A data story should have those elements, but beware of telling linear stories that are passively consumed. Interactive stories tend to be more effective in this day and age because people have become accustomed to interacting with data at work and at home. In addition, work styles have become more collaborative over time. Allowing audience members to do some of their own exploration enables them ask more informed, if not challenging, questions. In addition, unlike storytelling generally, data story endings tend not to be definite (e.g., " triumphant at last, they rode off into the sunset") but rather possibilities.

Data stories are also vulnerable to the same kinds of flaws that detract from blogs, articles, presentations, and books: typos. Make sure to proof your work. Otherwise, you may lose credibility. Also avoid jargon, not only because it's considered a bad practice, but because it may confuse at least part of the audience, which brings me to another important point: consider the audience,

Data scientists often are criticized for failing to understand their audiences -- namely, business people. It's fine to talk about linear regressions and sample variances among people who understand what they are, how they work, and why they're important. A business person's concern is business impact, not particular forms of statistics.

While you're at it, be careful about language use generally. The word, "product" can mean different things to different people who work at the same company. Bottom line, it's all about making it easier for other people to understand what you intend to communicate.

There's more to this story

I am unable to write a book about the subject here, which means there are more points to discuss than I have addressed. What do you think is important? What experiences have you had? What advice do you have? What questions do you have? Let's discuss it together in the comments section.

Lisa Morgan, Freelance Writer

Lisa Morgan is a freelance writer who covers big data and BI for InformationWeek. She has contributed articles, reports, and other types of content to various publications and sites ranging from SD Times to the Economist Intelligent Unit. Frequent areas of coverage include big data, mobility, enterprise software, the cloud, software development, and emerging cultural issues affecting the C-suite.

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Data storytelling in presentations
  • 6/2/2017 12:44:37 PM
NO RATINGS

..

This is a useful blog post with some valuable tips. I dunno why I didn't get involved in the discussion when Lisa first posted it in the fall of 2016.

I guess my experience in giving presentations of my own data analyses at public transportation conferences can be considered a form of data storytelling.

Lisa writes

An effective story has a beginning, a middle, and an end like a book or a movie. A data story should have those elements, but beware of telling linear stories that are passively consumed. Interactive stories tend to be more effective in this day and age because people have become accustomed to interacting with data at work and at home. In addition, work styles have become more collaborative over time. Allowing audience members to do some of their own exploration enables them ask more informed, if not challenging, questions. 

My own style tends to follow this "beginning-middle-end" pattern, with some specific approaches that I've found effective:

• Beginning — Why did I do this study? What's the purpose of this analysis? What problem or issues was I trying to resolve?

• Middle — How did I do the analysis – What's the methodology? What problems did I encounter, and how did I overcome them? What are the results?

• End — What conclusions can be inferred? What recommendations can be made for going forward?

I also like Lisa's point about encouraging questions from the audience. Often I insert questions in the presentation, and pause for just a moment to get people to think about the issue. Also I like to stimulate followup questions from the audience in the Q&A session afterward.

Bottom line: Lisa's summary is a good guide for presenting data in a way that will get your audience engaged,

..

Re: Conveying Message and The Dead End Ahead
  • 10/8/2016 12:55:44 PM
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Just the facts make for an easy to follow progression of the story and maybe a little razmatazz to keep the interest going in our short attention span world? But the story is the important part to keep in mind of course.

Re: Conveying Message and The Dead End Ahead
  • 10/6/2016 9:21:24 AM
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@Silvon. Great point about how the story has to accurately reflect the data. One thing about any storytelling is that it really should be based on facts. Exceptions can be made for stories told by grandfathers and crazy uncles.

 

Re: Conveying Message and The Dead End Ahead
  • 10/5/2016 6:40:37 PM
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@Silvon  Couldn't agree more and Welcome to A2 !

Re: What Conveying Message and Staying on it - Has to Do With It ?
  • 10/5/2016 6:39:13 PM
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Good point Pierre.  Though I am having trouble with the term Storytelling.  I think what is provided in the end ( or at least should be IMO ) is contextual background.  I remember trying to explain an issue to a former boss and he took it as though I was being to wordy about the entire issue - just get to the point.   

Well sometimes when you go straight to the point,  your audience will not understand where you are coming from because you have not provided contextual background prior to releasing the actual point.

So this manager probably thought I was providing a wordy story when in fact I was laying out the long contextual background so when the main issue was finally revealed he would understand how we got there.

 

Thankfully he is no longer with us so I don't have to provide either to him anymore.

Re: Conveying Message and The Dead End Ahead
  • 10/5/2016 2:34:57 PM
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Data visualization may not be enough. Data storytelling gives the visualizations a context that makes the information even easier to understand. However, it is important to remember that the "story" needs to accurately reflect the information, otherwise it can cause more harm than good. 

Re: What Conveying Message and Staying on it - Has to Do With It ?
  • 10/1/2016 3:56:44 PM
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Simplifying the story will be an ongoing challenge as industries face integrating data from different sources and as the audience decides what it wants to hear most.

Re: Good research design
  • 9/30/2016 12:15:26 PM
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What about the possibility of confirmation bias?

Re: Good research design
  • 9/30/2016 11:53:57 AM
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Part of the challenge is understanding what story you want the data to tell before you even look at it. I often would develop titles for my power point slides before I read the analysis then see if my story would hold up with the analytic or how the analytics would change the story. It often worked because it gave a baseline story that the analytics then embellished to create depth and greater understanding.

Re: Good research design
  • 9/30/2016 11:45:01 AM
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Yes, industry analysts tend to write their own presentations, sometimes at midnight!  

It is very interestinng to see how such a story takes shape, from crafting the surveys and analyzing the results, to writing all that up and presenting it.  By the time you've done all that, you're in a great position to tell a story.

Within the typical company, though, you may have to combine talents to tell a good story.

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