IoT Evolution Business Impact Awards given to businesses who deployed successful IoT Solutions.
Do you know which vendor can actually provide analytics for your particular IoT deployment? The data analytics industry is huge, and there are many subcategories. The most familiar to most people are web analytics and mobile app analytics, but there are companies serving practically every vertical where there is measurable and actionable data. Financial services, social media, server infrastructure and shop floor automation all have nuances that require a highly specialized approach to analyzing data.
We humans like to think about time in eras. We define styles and trends of the past neatly into decades. We talk about the “Industrial Revolution,” the “turn of the century,” the “21st Century” and the “Information Age.” With the Internet of Things finally becoming a reality, I recently looked back at one description of our next big thing as the “Post-PC Era.”
I met with a company last week that expressed interest in hearing more about how our IoT Analytics solution could help them. This is a very large, household-name consumer electronics manufacturer, and you likely own or have owned several of their products. When I got to the meeting at their office, the room was full; there were a bunch of technologists, marketing people, product people, and even some customer service managers.
It was explained that they had been thinking about whether to deploy analytics on their connected products portfolio at all, and they wanted an explanation of how analytics might benefit them. When you walk into this sort of big meeting at a big company, there is always a ranking member (RM) who drives the discussion. Sometimes that ranking member is cool and helpful. Other times, not.
Most people are familiar with the concept of analytics: At it's most basic, analytics is about finding meaningful patterns in data. That sounds like a smart thing to do, and it is, because the meaningful patterns in your data generally represent some kind of opportunity.
For instance, you might be trying to figure out whether the button on your website should be green or red. Make the button green for a few days, then make it red for a few days and compare the patterns in your data to see which one gets clicked on more. (It turns out it’s red.) This sort of A/B testing is among the simplest of analytics exercises, and looking at a relatively small amount of data can yield astounding insights that can lead to impressive results if acted upon.