The internet of things — the concept that anything that can connect will be connected — is captivating consumers and enterprises alike. When billions of devices, applications and sensors can connect and share data, we will experience a technology transformation that will easily rival that of mobile and even the internet itself.
The examples used to describe the world of IoT usually involve refrigerators, thermostats and coffee makers. But the reality is that everything — not just home appliances — can and will be reshaped by IoT. Every industry stands to benefit by surfacing the data hidden within these “things.” But the road to making this dream a reality could be long and arduous. I see this transformation happening in three distinct waves.
Wave 1: Sensor, event and data enablement
This wave is focused on unlocking the data from all the things and processes. We are living in this wave right now. Enterprises have been gathering data for years, and every year more sensors are placed on more devices, and more events occur and more data is created. The challenge of the first wave isn’t just gathering data, it’s accessing and delivering it. Yes, many companies have the data, but if it is trapped in devices or software or systems it serves little purpose. The goal in this wave is to build sophisticated “plumbing” that will simplify and improve how data is extracted and accessed.
This is no easy task. Companies in almost every industry are hiring data scientists and data engineers to solve their data problems. These roles, however, aren’t limited to research or experimental programs. Teams and business units across the organizations are each becoming more and more reliant on data to provide direction on their products, projects and activities. The need for data is becoming ubiquitous and surfacing data is critical for the journey to IoT.
Wave 2: Processing the data
Once you have unlocked the data the next step is to turn it into useful, actionable information. Today we are only experiencing this in an extremely limited way with traditional analytics and big data. Data processing will hit its real stride when advanced machine learning (ML) and artificial intelligence (AI) become more mature and the first wave reaches escape velocity. ML and AI can already identify insights and patterns that were never before humanly possible. Providing deep, rich datasets will only add to the power these technologies can bring to market. The key will be in advanced ML not requiring sophisticated modeling of the data to provide insight into the patterns, causation and correlation of data end events. But none of it will ever come to fruition without trust.
Wave 3: Closing the loop with trust
What good is the data if you can’t trust it? Without trusted data, we cannot act or bring about real change. Our very way of life is becoming more and more dependent on artificial intelligence. If the wrong data or incomplete data or inaccurate data is processed, decisions will be made (by humans and machines) that will have very real impacts. Crops could be destroyed. Self-driving cars could crash. Unnecessary risks will be taken. While it may seem dramatic, it is a very real problem that needs to be addressed before we can embrace a trusted IoT world.
We have to close the loop in order to trust the data, trust the route and trust the information that is sent back. It took a long time to get to the green lock (HTTPS) for the internet, and it will take even longer if we don’t consider trust and security as a prerequisite for IoT rather than an afterthought. The Internet is only 25 years old; IoT needs to move faster to get to a trusted system.
And don’t get me wrong — I am as captivated by the internet of things as anyone. But I do believe that the future requires us to start with trust by default, and not bolt it on like in technologies past. And if given the choice, I think we’d all prefer the internet of trusted things.
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