Data Stories…Part II “The Experience”
Different strokes for different folks, or so the saying goes. As we discussed in Part I of this series, information can mean different things to different people depending on their perspective. People also may have different interests concerning what they seek from a trusted data source. We called these logical groupings of people by their interests, “Personas”.
And while Personas help us define areas of emphasis, creating the emphasis itself, highlighting it, presenting it in plain view, that is what we call “The Experience.”
Good experiences tend to be somewhat self-explanatory: you know them when you see them… or experience them. This “self-realizing” aspect of good experiences can make them somewhat challenging to define. The most common theme: the less interaction required to generate a relevant result, the better. Put another way, if you don’t have to click, chances are far greater it’s going to be a good. Show people something they know and want. Don’t make them work for it.
Now, consider the challenge of presenting this no-click experience to your consumer. A single page has finite digital real estate, meaning you can only show a finite number of things. Additionally, on average, people can process about 4 items simultaneously, with perhaps 6 discreet items being viable when the subject is familiar. This is a fairly limited number of items for a large population of consumers many possible metrics to consider. A typical organization can have a couple dozen performance metrics. As we overlay our Personas, we can create subsets of 4-6 metrics, with each subset relevant to a specific Persona. Thus, while we may have two dozen corporate metrics for our dashboard, any single group of consumers only sees those 4 to 6 metrics that holds the greatest meaning to them.
From this initial “no touch” experience, we add layers with more detail, each layer specific to just one of those initial metrics. Perhaps you navigate from a given metric to its supporting detail by tapping on the metric itself. This action creates the context for what is about to appear. For instance, if I tapped on “Gross Margin”, I’d expect details related to that gross margin number. This greatly enhances the comprehension because you’ve created an expectation for the consumer around what they are going to see.
Generating intuitive experiences can be the most challenging part of any development effort. To quote Mark Twain, “I didn't have time to write a short letter, so I wrote a long one instead.” Creating an intuitive analytics experience is an exercise in removing data, not including data. Show just what needs to be shown, hide what might need to be shown, and delete the rest. It can be much harder than it sounds - brain processing and recognition aside, the last pillar of the analytics experience must also be considered: How is my target audience going to access this data? Are they all going to have laptops? PC with 30” screens? Macs? iPhones?
We’ll explore this challenge in the final part of this series: “The Mode”.