To give you an idea of scale, IDC expects global IoT spending to reach nearly $1.4 trillion by 2021, up from $800 billion in 2017. The IoT is all around us, in many cases fading into the backgrounds of our homes and lifestyles, all the while generating massive amounts of data. The trick is driving value from that data.
The Balance of Data is Shifting
Over the past decade, we've witnessed several shifts in enterprises' ability to deal with data. While different companies and industries are at different stages of maturity, we've seen and continue to see analytics evolving, whether it's adding unstructured analytics capabilities to structured analytics, third-party data sources to our own, or IoT data to enterprise data. Slowly but surely, we've been seeing the balance of data shift from internal data to external data, particularly as more IoT devices emerge.
Edge analytics helps separate meaningful data from all the noise, which usually means identifying, and perhaps reacting to, exceptions and outliers. For example, if the temperature of a piece of industrial equipment rises beyond a threshold, maintenance crews may be alerted, or the equipment might be shut down.
Organizations attempting to manage IoT data using their traditional data centers are fighting a losing battle. In fact, Gartner noted that the IoT is causing businesses to move to the cloud faster than they might move otherwise. In other words, when so many things are happening in the cloud, it makes sense to analyze them in the cloud.
Data and Analytics Strategies: Top-down and Bottom-up
The sheer amount of data organizations must deal with increases greatly with the IoT, and there are still philosophical debates about how much data should be kept and how much data should discarded. Gartner strongly advises its clients to be smart about IoT data, meaning that one should not save all the data hoping to drive value from it in the future, but instead focus on strategic goals and how IoT data fits into that.
We often hear how important it is to align analytics efforts with business goals. At the same time, we also hear how important it is to uncover unknown opportunities and risks simply by allowing the data to speak for itself. Some of the most sophisticated companies I've talked to over the last several years are doing both, with machine learning identifying that which was not obvious previously. In Gartner's view, "data and analytics must drive business operations, not reflect them."
One major challenge organizations face, practically speaking, is operationalizing analytics -- with or without the IoT. The core problem is moving from insights to action, which can't be solved completely with prescriptive analytics. It's a larger problem that has to do with company culture, stubborn attitudes and the very real challenges of integrating data sources.
Meanwhile, some organizations are pondering how they can use the IoT to improve customer experience, whether that's minimizing transportation delays, improving environmental safety or otherwise eliminating friction points that tend to irritate humans. Humans have become fickle customers after all, and each touch point can affect a brand positively or negatively.
For example, Walmart placed kiosks in some of its stores that retrieve online orders, scan receipts and trigger the conveyor belt delivery of the items a customer purchased. The kiosks address a customer pain point which is walking all the way to the back of the store and waiting several minutes for someone to show up only to be told the order can't be located.
Now think about what Walmart gets from the kiosk: trend data about customer use and experiences that may impact staffing, inventory management, marketing, supply chain. Clearly, the data will also indicate whether the kiosk idea is ultimately a good idea or a bad idea.
In the pharmaceutical industry, GSK has been working with partners to develop smart inhalers that track prescription compliance and dosing. The data helps inform research, and it also has value to doctors and pharmacies.
Similarly, enterprises can use IoT data to develop predictive models that help improve business operations, logistics, supply chain and more, depending on the nature of the sensors and the device.
Is Your Organization Integrating IoT Data?
What kinds of IoT devices does your company use? Has the data been integrated with other data so it can be analyzed in some sort of departmental context? What challenges have you faced? What advice do you have for others? We'd love to hear about your experiences in the comments section.