5 tips to manage 3 important issues

1. When do infographics add value?
2. How to confirm that an infographic’s information can be trusted.
3. What are the ropes to skip when preparing or sharing an infographic?

This post builds on an earlier one I wrote in August 2011 where I asked, what makes a great infographic? There, I noted:

“The question is, can viewers see the overall shape of the data more easily and quickly with infographics than any other visual aid? Most infographics fail this acid test.”

Things sure have changed since then. One major challenge has been big data, of which have increasingly more. Unfortunately, we are therefore also more vulnerable to misinterpreting information, which in turn leads us to make the wrong decisions.

These days, running a poll on Twitter or a survey with an online tool is quick and easy. Nevertheless, how one frames the question – see Scottish referendum – can affect responses. The British competition authorities have a 39-page guide on framing questions correctly. They are concerned about ‘acquiescence bias’.

Here respondents fail to challenge assumptions implicit in a question, such as, Did you do some physical exercises today?, which implies that you should have done so. Instead they recommend adding something like, Did you do some physical exercises today or did you not do any? Such clunky questions require giving the subject two response options:

1. I did not do physical exercises today, or
2. Yes, I did physical exercises today.

These type of questions lead to more robust results. Without robust data, answers needed for our questions are on shaky ground. In turn, making smart decisions could be a sheer impossibility (see Guest Poster Karen Dietz’ Storied infographics: Why do they fail?).

Here are five tips to reduce the risk of wasting ink and time by producing a rather useless infographic.

Tip 1: Actions speak louder than words

People often say one thing but do another. For instance, Jeff Bullas re-posted an infographic on his blog. In the post, one is advised to always give credit to the original poster
(see below).

But does Jeff Bullas follow his own rule? Nope. Nowhere can a hyperlink be found to the original post with this infographic. Bullas is in great company, though. Most blogs that re-posted this infographic failed to give credit where credit is due. But you have to, especially if you consider yourself an authority on your subject.

Where is the added value of this infographic compared to a table?

Where is the added value of this infographic? Would a table have done a better job?

Tip 2: Make it easy for your readers

As the above example illustrates, sometimes graphics are hard to find. Instead, a string of words is added to some graphic elements.
The question we always need to ask:

Is the information we have best suited to an infographic?

CLICK - The turnover rate in the control group was 47.2 percent higher than that of the individual identity group, and 16.2 percent higher than that of the organizational identity group. And turnover was 26.7 percent higher in the organizational identity condition than in the individual identity condition.

The MIT Sloan Management Review did not think so about the above information, but the table also illustrates that, if done well, infographics can do much to clarify the keypoints of a study for the uninitiated.

Tip 3: Trust but verify

President Ronald Reagan told President Mikhail Gorbachev that the SALT Treaty they both signed in Washington, DC would need to be verified. Always verify that the links posted in the bottom of an infographic work before republishing it. For instance:

The above link (listed under sources in the infographic above) did not work for me. To minimise such errors:

a) use a URL shortener, or
b) give a short link to where you found the data.

After some digging, I found the post that the above misspelled URL should have led to:

The lesson here? Long URLs increase the chances of a typing error if a reader wants to go check things out. Something a reader should do, before believing or re-posting the data.

Tip 4: Check your numbers

Based on the above link issues, I wanted to read the 12 pointers about how to use Pinterest as a business from Jackie Raiford.

“Jackie is a graduate student studying Conservation Biology at Antioch University in Keene. She is the Social Media Specialist at ParagonDigital.”

She tells us what she thinks works or not on Pinterest. It sounds very useful and sensible, but I cannot tell whether it is based on data or just her opinion. Do you want to invest several thousand dollars each month in your social media officer?

Because, as Jackie rightfully suggests, that is what it means if this is properly managed. Your expert will spend several hours each week posting the right things and engaging with fans and clients on Pinterest. That does not come cheap, does it?

Another interesting issue in the same vein comes up when the graphic below is studied a bit more carefully. For instance, do the city character and real estate values correlate? What is city character, exactly? How do you measure it? Are commuting times in London or Mumbai a factor?

Sure, Savills got great PR out of this graphic – we will definitely get a repeat performance in 2015, and the FT managed to fill more than a page based on this graphic. Singapore and Moscow have few high rises and high real estate values, while New York has many skyscrapers and also has high real estate values. So what have we learned? That there does not seem to be a trend!

This is an example of an infographic – produced by a reputable firm and published by a highly esteemed newspaper – whose merit should be questioned by readers and editors alike.

CLICK - Are population size, persons per hectare, number of buildings over 150 metres, and density/building height USEFUL indicators that HELP us measure the city's urban identity? Or even a city's character? I think what we have here is #BigFail, lack of #Usefulness, #SmallData

Tip 5: Spell out your methods

As we have seen, experts often post some tips while suggesting that doing the same will also lead you to success. In other cases (see above), the infographic insinuates a causal relationship where there seems to be none.

These things can be done correctly. For instance, in the research below, weather data was used to learn how it might correlate with restaurant reviews. In combination with thousands of restaurant evaluations from thousands of patrons across the country, the authors concluded that bad weather leads to more negative reviews.

The difference is that this study is not a quick and dirty exercise. It uses a thoughtful definition of the issue being investigated. Second, time, effort and financial resources were spent collecting the data. Such studies are not done in a week or two (see below).


Experiments are best conducted in the lab. Field studies should try to control for certain variables, such as the day and time tweets were posted.

Bottom line

We all use simple visualization techniques for quantitative or qualitative analysis, which does not mean we analyze data for its own sake. Instead, we want to make informed decisions, so we analyze our data to gain an accurate and thorough understanding. Almost all effective visualization of quantitative or qualitative analyses are two-dimensional, X-Y axis type visual aids, such as bar or line graphs, and scatterplots. We want an overview, so we zoom in and filter, then study the details.

However, if the details are not to be trusted, visualization may result in more confusion that clarity. To illustrate, you may start with a table to allow the reader to get an overview (see DrKPI Blog Benchmark).

DrKPI Blog Benchmark shows how to improve content marketing for Barclays Wealth Blog and foster more dialog with target audience

DrKPI Blog Benchmark shows how to improve content marketing for Barclays Wealth Blog, to foster more dialogue with its target audience.

The above table gives you an overview with lots of information in context (e.g., country and same industry). Once you dig deeper, graphics get added.

These must be designed to display content easily, without wasting ink. UNFORTUNATELY, more often than not, infographics are a string of words. Through dazzle and a splash of color we may want to convey more than our data permit. That means we are trying to snowball our audience or at least wasting their time.

If we look again at the images above, the US map is relatively simple. Nonetheless, it conveys a lot of information. Also, the table about onboarding at Wipro (see earlier above) conveys interesting findings in non-technical language to managers. Both images use little if any color but add a lot of insight for the reader.

Better safe than sorry – buyer beware

Infographics may offer much color and splash. Nevertheless, their data may be boring or less valid and reliable in comparison to quality work. Keep that in mind.

So before giving away the farm, use the 5 tips in this blog post to check things out. Is trusting an infographic you found advisable? It could be based on invalid data riddled with bias and therefore make you look stupid if you re-tweet or post it to your blog!

Lots of collor and images - but where are the numbers

Lots of color and images – but where are the numbers?

What is your opinion?

– Have you recemtly found an infographic you liked? Please share the link!
What do you recommend doing FIRST when putting together an infographic?
– What other recommendations would you make?

I love to read your comments below and look forward to answering them. Merci.

Source: Can infographics show you the money? 


Bakhshi, Saeideh, Kanuparthy, Partha, and Gilbert, Eric (April 7, 2014). Demographics, weather and online reviews: A study of restaurant recommendations. Paper presented at the 23rd International World Wide Web Conference, in Seoul, South Korea on April 10. DOI 10.1145/2566486.2568021 Retrieved April 6, 2014, from

Cable, Daniel, M.; Gino, Francesco; and Staats, Bradley, R. (March 2013). Breaking them in or eliciting their best? Reframing socialization around newcomers’ authentic self-expression. Administrative Science Quarterly, Vol. 58(1), 1-36 DOI: 10.1177/0001839213477098. Retrieved May 27, 2014, from

Easier summary article: Cable, Daniel, M.; Gino, Francesco; and Staats, Bradley, R. (March 2013). Reinventing Employee Onboarding. MIT Sloan Management Review, Retrieved May 28, 2014, from

Few, S. (2011). Data visualization for human perception. In Mads Soegaard and Rikke Dam (Eds.), Encyclopedia of human-computer interaction. Retrieved September 30, 2014, from

Few, Steven (2009). Now you see it. Oakland, CA: Analytics Press. See

Gattiker, Urs E. (July 9, 2012). Why do infographics fail? ComMetrics Blog. Retrieved September 30, 2014, from

Schrage, Michael (September 3, 2014). Learn from your analytics failures. Harvard Business Review – Blog Network. Retrieved September 4, 2014, from

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