A responsive interface, by its nature, supports many form factors. The carefully chosen scale for your line chart in landscape mode can look deceptive if the user simply turns their device on its side. A legible and useful visualization on a desktop or tablet may turn into an unusable, eye-straining puzzle on a phone. A widget designed for the phone can look absurdly simplistic enlarged on a tablet.
You can solve this with painstaking design and coding, but you shouldn't need to. Your BI platform, if well-designed for mobile, ought to handle these scenarios generically. No dashboard designer should be forced to handle the complexities of multiformat design.
For example, a mobile BI platform should intelligently scale charts appropriately at any size. A scatterplot may show many data points at a high resolution. At a low resolution, the platform could algorithmically determine the optimal key points to show, still revealing the pattern and range of the data. However, at any size, the user should be able to zoom and select and interact with the chart to see their information from a different "viewpoint" to gain a yet more complete understanding.
Working with a responsive and exploratory design, old certainties about information design start to change. Personally, I find it painful to see a traditional pie chart with many segments and complex labels. What a horrible way to show useful data! However, if you can enlarge or shrink the pie to suit your format, and spin the chart around its center to bring different segments to the top, all the while showing only the labels most relevant to the current view ... now the pie chart feels more usable and interesting and informative.
3. Speedy discovery
That's why a mobile BI platform demands great performance on the client. At least use CSS media queries to restrict the visualization and design features that will be appropriate and available for the client in use. And look to complement that responsive approach by building with a framework such as AngularJS. The two-way data binding makes it easy to listen for screen resizes to determine what kind of elements you would like to load efficiently. If you find that this complicates working with the code, use a CSS pre-compressor (such as LESS.js) and compile everything to single file.
There's another facet of user behavior that may help to squeeze performance from an analytic client. Users following "information scent" mostly make selections that narrow the scope of the data they are looking at, only occasionally moving to a new or broader scope. Looking at U.S. sales, you most likely "drill down" to consider sales in, say, Texas rather than jumping to look at European sales. So caching data on the client can be quite practical, especially if you use JSONPatch to update the array when needed.
The same tendency to start from a higher level and drill down can speed up visualizations using paging. You rarely need to plot 1 million data points, even when rendering a complex data set. The algorithmic approach that enables you to scale visualizations can be used to optimize performance. You may show only key points at first and page in more data as needed when the user selects to explore further.
A natural approach to analytics
As you can see, mobile BI requires that you think beyond mobility. A good mobile analytic platform is fast and facilitates an interactive experience that is responsive and touch-enabled.
These practices not only enable, but encourage exploration. We are compelled by natural curiosity. As we follow it, we make discoveries and form new insights and new understanding.
This is only natural. In business we can only spend so much time at our desks. That's why we get out into the real world, we go on the road, we walk round the store or warehouse or factory floor in order to see what's new. Mobile analytics should go with us -- and work with us the way we need it to work.
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