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Nathan Yau over at Flowing Data has some wonderful posts on visualizing, presenting and interpreting data which are a must read for anyone interesting in or working within the analytics field. A while back he published a handy little guide on general formatting rules for common charts which includes tons of great tips and advice for anyone whose day job includes visualizing data in any way. I also found it pretty amusing that many of the examples of what not to do were pulled straight from Fox News.
As someone who works in the private sector, or more specifically in the field of marketing, I’ve seen data applied in some pretty manipulative ways to make a point or validate a hypothesis that wasn't actually true. Marketing isn'tthe only domain where this happens, as even career academics can either make mistakes or manipulate data to serve their own interests. Megan McArdle just published an interesting article on this very topic earlier today which is worth a read. For a more comprehensive reading on the topic check out Statistics Done Wrong by Alex Reinhart or Wrong by David Freedman.
However, in the world of data and statistics it seems that marketers tend to be a little more guilty of using data to validate a belief that simply isn’t true. Make no mistake, if you work in the field of marketing analytics and you care about being truthful, you'll always struggle to find the right balance between time, cost and rigour. But not everyone understands the importance of rigour when it comes to truthful interpretation of data, and worse, some don’t care.
In my experience, there are usually 3 types of people when it comes to leveraging data to communicate a belief or point of view.
The Good (aka the Truthful)
Those who understand the fundamentals of data collection, data cleaning and analysis, who adhere to sensible rules of formatting and presentation, who seek to present data in consistent and logical ways, and those who do not manipulate the presentation or interpretation of data as a means to communicate their own view, bias or hypothesis regardless of the outcome.
The Bad (aka the Deceptive)
Regardless of their understanding of data collection, data cleaning and analysis these types of people knowingly manipulate the presentation, formatting or interpretation of data as a means to communicate their own view or hypothesis even if it's not true.
The Ugly (aka the Ignorant)
Those who have a limited understanding of the fundamentals of data collection, data cleaning, and analysis, and who attempt to present data in a truthful way but through errors in analysis or interpretation they misinterpret or unintentionally communicate an outcome which is not true.
Believe it or not, it is possible to be in marketing and to use data in both meaningful and truthful ways (i.e. the good). Unfortunately, this means that you need to be prepared to accept and present results that don’t necessarily tell the story you want it to. Whether it’s poor or underwhelming performance or an outcome that doesn't validate your hypothesis, having the bravery to present these results to your leadership, your team or your client can be the difference between facing a hard truth but making a good decision and wasting time and effort on producing fancy tables and charts.
Yau’s posts on chart formatting are a great resource for being an effective storyteller and which can help you avoid common mistakes when it comes to the presentation of data. But beyond the fundamentals and basic rules, before you submit that next report you should ask yourself, "are these conclusions truthful, ignorant or deceptive?"
Header Image Credit - Jeff Victor. To see more of his awesome work check out jeffvictor.com