The Internet of Things (IoT) is experiencing a massive boom, as the IoT market expected to double by 2021. The predicted growth in the IoT data market is no mystery; it directly correlates to the increasing volume of data and the number of connected devices. By 2020, we’ll have 30 billion connected devices around the world. Also, by 2020 there will be approximately 8 billion people in the world connected to those 30 billion devices. There’s no turning back now: the explosion of real-time IoT data is upon us, requiring enterprises to leverage modern data management solutions to handle it all.
Factors driving the IoT data growth
Why is the IoT exploding now? Why not five years ago? It’s due to the convergence of three key technologies have heated things up in the IoT world:
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- Data analytics: For the past five or 10 years, we’ve seen a shift in the way we do our analytics and the way we approach data; it’s been essentially a “big bang” change in the analytics world. We used to ask questions and look at a subset of the data to get the answer. Now we look at the data and find insights by exploring it. We finally have the technology to ingest and store massive amounts of the incoming IoT data using open source framework and can run jobs in batch or real-time, enabling the data to be explored quickly.
- Cloud computing: The cloud has evolved and matured into a full infrastructure and platform. We’re now just a few clicks away from creating an entire IT architecture and infrastructure, or a business cloud solution. There are many ways to do this, besides the most commonly known software as a service (SaaS), there is infrastructure as a service (IaaS); platform as a service (PaaS); container as a service, now; and function as a service. On top of that, you can pick the flavor of the cloud you want. A lot of software companies offer a public cloud to access their SaaS, or they expose access to components and capabilities. Other vendors sell private clouds – rather than be on a public cloud, it will be on your own private VPN. There are also hybrid cloud solutions. Talend Integration Cloud is an example, as it can connect cloud applications and your own on-premises applications.
- Electronics: Technology is getting cheaper and networks are growing fast. IoT sensors can be built inexpensively and there are more networks dedicated to IoT, such as SigFox, LoRa, or the Narrowband IoT. Today’s sensors are capturing new sorts of IoT data around our movement or our environment. It’s even possible to do a robotic project with your kids using cheap and accessible software.
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These factors have all contributed to making IoT data more pervasive and present in our world than ever. There are a lot of examples of how IoT has been deployed int he industrial world. From the smart cities to healthcare, to the smart operation, connected car, smart farming, or smart retail – IoT data is everywhere. Which brings us to the question…if all these devices are collecting data, how do we manage the data and make sense of it all?
IoT Data Considerations
The first challenge with IoT data is to integrate all this data and make sense of it – which is the primary reason it is collected it in the first place. How are you going to get analytics that will transform your organization for the better?
Once the IoT data is captured from the sensors, a messaging protocol is used to connect the devices and dump the information into a “sink”. From there, the data mediation and processing will happen, and the integration is done. It’s time to apply machine learning. There are three flavors of machine learning that really fit the IoT solutions:
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- Anomaly detection – This is probably the most widely used. It identifies anomalies within the stream of IoT data from the sensors.
- Predictive modeling – This involves using historical data to make predictions. If we have the target, file or field to predict and send in a reply, machine learning will then predict the next value.
- Classifications – This involves grouping a set of events together, so it fits to IoT.
There is a standard methodology that applies to machine learning. It’s a cycle – a loop that starts with the business understanding and goals then the analytic approach that will be taken to get the desired results. The IoT data is then collected and checked to make sure it’s right data and you understand it, then the data is prepared. When the data preparation is done, you finally get the model, then is evaluated (checking accuracy, for example) and then the model is deployed. Any feedback that comes after deployment may require adjustments to the model, which are completed until the loop is accurate.
IoT data and artificial intelligence
Machine learning is one discipline of data science. Another is artificial intelligence (AI). The relationship between AI and IoT much like the relationship between the human brain and body. Our bodies are collecting sensory inputs, such as sight, sound, and touch. And our brain takes that data and makes sense of it. Turning light into recognizable object or turning sound into understandable speech. Our brain makes the decisions and sends a signal back to our body as a command, for instance, to make a movement, or pick up an object, or to speak. All the connected sensors that make the IoT are really like our bodies, providing the raw data of what’s going on around us.
The value and promise of AI and the IoT are just really being realized because of each other. The use of AI for development and discovery is just now beginning to gain traction, but over the next decade, as the scope of IoT data grows, AI will become an area of significant investment and development for many enterprises.