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:
- Sophisticated product companies will make it a top priority to extract usage, event and contextual data from their connected products to apply IoT Analytics to achieve an edge.
- At first, humans in these product companies will look at their IoT Analytics data and use it to make smarter decisions.
- Then, the companies will get smarter and allow the data to flow algorithmically through processes where software will replace the humans looking at the data to make smarter decisions faster.
- Meanwhile, a growing mesh of connected products will be further enabled and the already digital processes will extend to the products themselves, wringing even more value out of them. This is when your refrigerator will reorder milk from Amazon and a drone will deliver it.
- Next, the value of analytics applied to a company’s internal data will hit the top of the S-curve and the focus will be on combining it with external data, too, which will provide a greater level of transparency and contextual insight.
And then, something significant is going to happen: Autonomity. There will be an end to human intervention.
We are going to make connected devices smart enough to make themselves smarter, and those devices are going to go out on the interwebs to look for the most relevant information to make themselves smarter. The most useful information they will find will be historical and real-time IoT Analytics data from a cohort of other devices.
Driving your car from California to Colorado? It will look at available weather data to determine the temperature. It will also know altitude, pressure and humidity. It will make adjustments to the engine to optimize performance by looking at other vehicles within and outside its manufacturer in a geospatially global cross-section based on similar circumstances. Then it will add what it learned from other cars’ experiences to its own actual experience and put that all back in the connected and distributed cloud of useful data for other cars to use in real time.
Every selection of a cohort and application of data will become a case study indexed and findable by other machines capable of applying and adapting or discarding best practices at nanosecond scale. The result will be continuous improvement through the application of shared experiences and learning that will enable devices to evolve at a pace and to an extent that human programmers could not achieve.
At the heart of all of it is IoT Analytics: We are taking what a device is doing and turning it into knowledge and insight so that it can then be combined with other data to gain further knowledge and insight so that it can be provided to a machine to incorporate the knowledge and insight so that the machine can then return additional knowledge and insight to the collective knowledge base for all to benefit from.
This future is going to happen. The companies that stand to benefit the most are the ones that embrace the vision and build their product strategy around it the soonest to reap the greatest reward.