Why Analytics Need to Be Embedded and Contextualized

Remember your first experience with analytics? Many of us became acquainted with the concept by logging into a separate analytics tool outside of whatever program or application we were using. Once we were in that tool, we could take a look at whatever images or a graphics were available, then navigate back to the program or application we used to do our work. After that came cloud-based tools that ran an analytic application inside an iframe in your program or application, so the analytics were housed in a separate “box” in your program—similar to how you see a banner ad on a web page.

We’ve come a long way since then, but I find that many companies are still using the kind of analytics that they had originally used: analytics in a separate tool or tab. However, that means that the analytics are completely isolated from the rest of your workflow—and that’s a problem for two key reasons.

1. Analytics need to talk to your other applications

When you have analytics in an iframe or a tool, the analytics can’t “talk” to the other applications you’re using. Because the analytics are kept separate, they have no knowledge of what’s going on within your primary application. Not only does this mean that the analytics aren’t getting the full story, but it also severely limits what you’re able to do with the analytics.

For example, say you work at a shipping company, and your analytics are currently in an iframe in the form of a chart showing you how many deliveries you have left for the day. But, because your analytics are in an iframe, they have no knowledge of what other information exists on the page, and they can’t make adjustments based on other developments. If you see that a weather event has disrupted deliveries and rerouted packages, that information wouldn’t be taken into account. The analytics wouldn’t be able to “see” that information and show you how your vehicles or deliveries are affected.

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2. Analytics need to be presented in context

In the example above, it would obviously be of enormous benefit to you if you were able to see the number of orders you still have left to deliver that day, or use filters to see how many deliveries you have left by region or truck type or some other metric. Or maybe you’d like to see a map of all routes next to how many deliveries you have left, so you can see where the remaining deliveries still need to be.

If analytics live in a separate tab or tool, then you’re missing some of the value of the analytics, because it’s not presented in context. Seeing this information in context, embedded right alongside the rest of your workflow, helps you to draw connections faster and more easily. Though having this information living elsewhere is better than not having it at all, you’re still not able to use analytics to its full potential.

By ensuring that analytics are embedded and contextualized, you’re opening up a world of opportunity—not only in terms of visualizing and understanding your work, but in creating a true workflow. Analytics no longer has to be a separate mode; it’s just a part of your routine. You get to the point where analytics are embedded so deeply that you may forget you’re even using them.

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