The Internet of Things (IoT) is quickly becoming a way of life, making its way from consumer environments in homes and retail environments – to now becoming an integral part of the modern enterprise.
Before the IoT revolution, data was limited. It existed, and people tracked it – often manually – but limited data was being produced because humans had fewer devices, and even less data was actually accessible by humans. With IoT, the flood gates have opened – with every device pushing, pulling and producing data. And, with IoT comes convenience.
Having all (or most) devices connected makes for better collaboration, ease-of-use, insights and connectivity at the click of a button anywhere, anytime. The flow of information seems to be endless and we as consumers, business or otherwise, bask in the convenience and efficiency afforded to us by technology.
The IoT is about the connectivity and communications that takes place between devices, not just humans. Every connected device pushes data out, is consumed by the next, and so on. With the average number of devices expected to hit 4.3 per person by 2020, more connected devices means more data flow between our devices.
To businesses users, this translates into an opportunity for more key performance indicators (KPIs) than ever before. It means thousands of KPIs, in fact – which is useless to humans who cannot possibly analyze or track these KPIs. More commonly, this argument is that it equates to data overload.
Many industry analysts and influencers analyze this common problem among the enterprise and come to the conclusion that there is too much data, and most of it has a lifespan and becomes irrelevant – begging the question of how do humans analyze and digest all this data?
The fact is, humans don’t need all these data sets – but our machines do. There are certain data that the consumer does see, such as messages and other forms of communication, but the majority of data consumed by a connected device, is actually meant just for that device.
Let’s go deeper. IoT produces mass amounts of data, and artificial intelligence (AI) listens to a request, mines for data, and either performs a specific task or delivers cohesive, well-informed results for the human. In this instance, AI is the proxy between people and IoT to produce meaningful business insights and ultimately smarter business decisions or processes.
By acting as the proxy, AI is making BI more flexible and accessible to humans. If we limit the amount of data given to these devices – going with the premise of data overload and data purging – the device will have less information to work with and won’t be able to serve its purpose.
In the BI world, AI is creating opportunities for nontechnical users to interact with business insights by enabling access to data in more humanized, digestible formats through chatbots and natural language devices and applications. In other words, AI is simplifying analytics, connecting humans with complex data in a comprehensible – and even conversational – format.
Just as AI is making BI more flexible and accessible to all users, IoT is doing the opposite – making it more autonomous and enabling machines to work with minimal human guidance. Take Tesla, for example. The amount of data and IoT that goes into making a Tesla work is impossible for humans to comprehend on their own.
As drivers, we do not need all of this information, and it would actually result in information overload and make us worse drivers. For Tesla, it cannot run without unrestricted access to data. In this example, IoT is enabling the autonomy of the machine (Tesla).
The misunderstanding of data overload could ultimately deplete the knowledge and efficacy of smart Business Intelligence (BI). Historically, (or better yet, primitively) humans would pull from memory recall and lessons learned to make business decisions. Now, with the emergence of machine learning, AI and IoT, humans have legacy knowledge and attested answers right at their fingertips. This enables them and ultimately their business to extract more accurate and meaningful intelligence to be analyzed by humans.
It’s easy to get caught up in all this talk of data, machines and autonomy, and forget how it relates to the human experience – but that’s exactly what all the data and machines are about. If we become dependent on the connected devices we use today (remember back to 4.3 per person by 2020), we cannot remove or limit the data they use to be helpful and efficient simply out of fear of data overload.
Technology is usually created to solve a problem or bridge a gap – much like how the cell phone was created to solve connectivity issues from remote or people in transit. So, if we limit the data that devices use to remain helpful, the positive human experience will vastly decrease because the technology will no longer serve its purpose, or bridge the gap.
Ultimately, we as humans need help from machines as the IoT pushes them to be more strategic, useful and predictive assets in our BI stack. The IoT is the bottom line that will lead to the least human-intensive BI process. Still, this isn’t about removing humans from the BI process – in fact, the IoT and AI are opening BI up to even more humans, by simplifying the process and making it accessible to all business users. As this happens, technology and humans will truly begin to work together, as IoT-driven insights inspire human action and business outcomes.
(About the author: Eldad Farkash is co-founder and chief technology officer at Sisense)