The Internet of Things (IoT) has arrived, and the promise of a connected-device world is taking shape as innovative companies bring improvements to market in the form of automated water meters, connected industrial machinery, and useful consumer products, to name just a few categories. With the current estimated number of connected IoT devices reaching well into the billions, there is still tremendous room for growth to the projected 50 billion devices expected to be connected to the IoT by around 2020.
There is a somewhat infamous 1995 Newsweek article about how the hype and the reality of the Internet were so far apart. Here is a choice excerpt complaining about the promise of e-commerce:
“Stores will become obselete. So how come my local mall does more business in an afternoon than the entire Internet handles in a month? Even if there were a trustworthy way to send money over the Internet—which there isn't—the network is missing a most essential ingredient of capitalism: salespeople.”
At some point, you have to cost-justify your analytics project. “We know we need this, but we have to make the case to the accountants.” Sound familiar? You’re not alone.
You are making a decision to make better decisions with data, and those decisions need to make financial sense, otherwise you shouldn’t make the decision. While you cannot know your full ROI until you implement, making data-based decisions is better than whatever you are doing now.
If you are a TL;DR kind of person, here you go: It is less expensive than you think and it is measurably more valuable than you imagined. So if you just Googled “IoT analytics ROI Analysis” for your project, this post is for you.
A study released by Deloitte Canada a few months ago showed that 82% of more than 300 CMOs they surveyed have been asked to interpret consumer analytics data and admitted that they felt unqualified to do so based on previous experience. This is a problem made bigger by the fact that 70% of respondents in the study said their companies expect marketing decisions to be based on analytics and that they are expected to treat data as the “voice of the customer.” It is no wonder, then, that almost 70% of the same respondents reported that they expect agencies to play a key role helping them analyze and interpret data.
I recently wrote about how IoT Analytics is the new CRM. The point was that if a CRM system exists to give you insight into your customers who are using your products, then IoT Analytics gives you insight into the products themselves.
For product companies looking for an edge, IoT Analytics is an important component, and this phase of the IoT evolution will continue for at least a decade, but IoT Analytics is at the heart of a revolution that extends far beyond that. Here is what is going to happen:
Hello, product-based company of some sort! Whatever you are building is going to be awesome. One of the main reasons it is going to be awesome is that its connectedness and its ability to provide real-time analytics data is going to give you and your customers a new dimension of insight.
If you are embarking on an IoT project and you are reading this, you already have a sense that the data generated by your devices is valuable and must therefore be captured, analyzed and leveraged for your (and your customers’) benefit. This is true. You are also likely to see a conflation of the following concepts: IoT, Big Data, and Analytics. I guess that makes sense to some degree; billions of connected devices are going to generate big amounts of data, so clearly a big data solution is needed, yes? No.
If the Internet of Things is comprised of smart, connected objects, then wearable technology is certainly a visible and exciting category that enables people to understand the promise of the IoT. At the intersection of analytics and wearable technology is the quantified-self, the result of the output of data derived from sensors you wear that measure what you do.
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.”