Archive for category Data Visualization
On Friday, the HBR Daily Stat posted a story about the link between high school club memberships and a person’s propensity to be a manager later in life. Later that day I ran across a tweet from Dave Zinczenko of Men’s Health Magazine, claiming that 7+ hours of sleep a night leads to a lower risk of heart disease.
The thing that strikes me about both of these articles isn’t necessarily the information that they convey – I think many would agree that getting more sleep is better and that the editor of their high school yearbook will make a good manager. What strikes me about both of these articles so completely confuse the concepts of correlation and causality.
Is seven the magic number of hours of sleep to ensure you’ll live a long and prosperous life? Or is the kind of person who sleeps at least 7 hours a night the kind of person who takes care of themselves, watches what they eat, exercises, and tries to limit the amount of stress in their life? Does being in a certain school club raise the likelihood of being a manager, or do people who gravitate towards certain school clubs (a la Freaks and Geeks) tend towards a certain kind of success?
President Bartlet: CJ, on your tombstone it’s gonna read ‘Post hoc ergo propter hoc.’
CJ: Okay, but none of my visitors are going to be able to understand my tombstone.
President Bartlet: Twenty-seven lawyers in the room, anybody know ‘post hoc, ergo propter hoc’? Josh?
Josh: Ah, post, after hoc, ergo, therefore… After hoc, therefore something else hoc.
President Bartlet: Thank you. Next? Leo.
Leo: ‘After it, therefore because of it’.
If you think I’m nitpicking over very fine distinctions, consider this: how many times has a finance manager asked you to quantify, in dollars, the benefits of your marketing activities? How many times have you been asked to predict – and later to show – causality between what you’ve done and some form of business result, no matter how multi-variate and subtly complex the interdependencies really are.
Did sales go up because of the advertising campaign you recently launched, or did they go up because the economy got better and people feel more comfortable spending money? Did market share go up because of they change you made to your product or the change that your competitor made to their product? Is your product not selling because people simply aren’t aware of your product, or is it not selling because people don’t see a need for your product. Or are there many more factors that contribute to your success or lack thereof?
While I think its important to set measurable goals and work towards attaining them, I also think its important to know when your thing did something and when you did something while something else happened at the same time. And I think its important for all of us to be honest about when it’s the former and when its the latter.
It comes down to asking the “Why, what, who, and how” of your business, arraying it across one page in a way that makes it extremely useful as an alignment tool amongst management or board members. This is hardly a novel concept, but it falls into that category of common sense that is not so commonly done.
Tjan goes on to outline 4 questions he thinks helps frame up the gestalt of a company. Those questions include:
- What’s the big idea? Why do you exist?
- What is your value proposition?
- Who are you trying to serve?
- How do you know you are winning?
Not sure I agree on the order of the questions, but the intent, I think, is sound: if you can’t clearly and succinctly describe your company to yourself, how will you ever be able to describe it to anyone else.
Put it another way: if you’re asked what you do at a family get-together, how are you going to explain yourself?
One potential pitfall of the above questions is the temptation to answer them too broadly. Often I have heard people say “Well, my product is really a global product, and my positioning is really global positioning, so I’m really serving everyone everywhere.”
It seems that the most successful companies are those that are able to answer the above questions as narrowly as possible. Take 37signals for example: they have a very narrowly defined user base that in turn informs a very narrowly defined product. If a customer asks for a product modification, more often than not their answer is “No, we’re not going to do that. We’d hate to lose you as a customer, but we understand if your needs have outgrown our product.”
Compare that to Microsoft and their almost pathological need to have “Windows Everywhere for Everyone.”
The most important of the four questions above, I think, is #4 … how do you know you are winning? It’s easy to focus on doing things, but as my friend John likes to say “Activity does not equal accomplishment” (not sure from where he borrowed that). You need to know what success looks like, you need to know what and how to change mid-stream, and you need to have enough data to make an informed decision.
How do you think through a business strategy?
Admitting uncertainty means facing reality — and our own needs for security. But admitting uncertainty is not enough. We must learn to actively embrace uncertainty and work with ambiguity.
As I sit in a two day meeting about drawing insights from data quantification, this article is very timely. Obviously there’s merit in looking at your data and trying to draw as many insights as possible, but we as marketers can sometimes end up hiding behind the data, using them as an excuse to take no action in favor of gathering and analyzing more data.
There comes a time, however, where you have to accept whatever level of ambiguity you’re willing to accept, make a decision, and take action. The question then becomes: When is the right time? A better question might be: What do I get by delaying and gathering more data? Am I falling prey to the law of diminishing returns?
How do you overcome “Analysis Paralysis”?
As someone who spends a decent amount of time creating and updating Powerpoint slides, I sometimes struggle to find the most effective way to present data. In one circumstance, a simple table of numbers might be sufficient to get the point across in the proper amount of detail. In another, a 2×2 matrix might do a better job conveying the interplay between 3 or 4 different variables.
There’s a great TED Talk given by David McCandless about visualization of very large datasets. Its an interesting look at how to “[squeeze] an enormous amount of information and understanding into a small space.”
As we continue to compile and sift larger and larger datasets, its up to us as marketers not only to distill the key messages from those data, but also present those key messages in an effective and compelling way.
McCandless goes through a few short examples in the talk and details even more in his books. I highly encourage you to check them out.
[NB: YouTube version embedded. You can also check out the original on TED.com]