1. How Not to Data Visualize

At the most commonly found level, a data visualization project consist of having some data, having a vague idea of the user’s actual needs, and a strong conviction that what you’re doing is perfect for the problem. Well, in 99.99% of the cases it’s not.

This process could be visualized kind of like this.

For the sake of completeness, here is the real-time / streaming analytics version.

It’s very easy to take data, mix it with some sort of “black magic” (read Javascript) and have everyone look at it in awe. But how about after the presentation? How about after first 10 uses? How about after a year? How about when something needs to be rapidly changed (because NOBODY gets the spec right at first go > analytics dashboards are a moving target because user needs are).

It may be straightforward to build things with various libraries, but that’s the wrong problem.

The number one reason for frustration (user) and failure (business) regarding data visualization is “data puking”. Don’t just puke, tell a story that helps people to take action (change behavior).

The basic principle is that generally you’ll understand the value in your work better than anyone else does. Be very mindful of that.

2. Make it Goal Based

Around 2008 I led a team of young turks that shared a goal; start a new era in web analytics. At that point, Omniture had not been acquired yet, and Google Analytics was really bad. I mean really bad. There were three of us trying to change things — STATSIT, Nuconomy and KissMetrics — and show the big boys how to do it. I think to some extent we did show them, at least Google. Below is a screenshot from the keynote speech Avinash Kaushik, then Google’s head evangelist for analytics, gave in the Google Analytics summit in 2009. In it you see the goal setting view of STATSIT web analytics.

As the screen shows, almost 10 years ago, we had built-in goal setting for ‘behaviors’ and ‘long term’ goals, in addition to what were more commonly referred to as conversions. While ‘behaviors’ have to some extent become part of the analytics toolkit today, there is still much room for improvements in terms of ‘long term’ goal tracking. This is just one example of how analytics is nowhere as nearly goal-driven as it could/should be.

Related with the goal aspect, you could follow these 6 steps to avoid headaches later:

Identify what the current goals of the user are Make sure that the visualizations correlate with that Create a simple prototype first After some use figure out what the goals now are Make changes rapidly (this will make users want it badly) Keep iterating

Here is an example of a simple user feedback loop:

Now go do it. Forget bells and whistles and ‘wow factors’ until you figure out what the user actually needs. Note that ‘needs’ and ‘wants’ are often entirely different.

Going back to the point about starting simple, you might ask, how do you know it’s simple enough?