How to Apply Data Science Algorithms to IoT Data?

A simple glimpse at the Internet statistics today will reveal the amount of web traffic, number of Facebook posts, Instagram uploads, YouTube videos posted, number of emails sent, tablets and smartphones purchased around the world and more. The best part about the statistics is that it keeps ticking every second. Probably that wouldn’t contribute to even 10% of the amount of data generated every single day. From regular Google Searches to clicks on sponsored content, you are making your device know you better. With the Internet of Things further making its way to our lives, the amount of data produced is just exploding.

One of the primary goals of technology has always been the enrichment of human lifestyle and with the IoT, it is getting closer to achievement. However, to completely realize this goal, IoT needs data to offer enhanced experiences or find newer ways to do this autonomously. That’s where Data Science and Machine Learning come into action. On this post, we will look at applying Data Science techniques and algorithms to IoT data and understand how this could allow devices – from sensors to end devices – to extract data and analyze the data to uncover information.

With the current trend, IoT is one of the forerunners in data generation and this is exactly why Data Science will be required in IoT more than ever. Data Science being interdisciplinary encompasses a range of techniques such as data mining and processing to Machine Learning to draw insights from raw data. For the appropriate application of Data Science techniques, you need to define your data types. A few examples of this would be variety, volume, velocity, data models including clustering methods, classification, neural networks and more. Once the data type is defined, you should apply the right algorithm that falls in line with distinct data characteristics.

Since data is generated from various sources, it is important to characterize them and apply the appropriate algorithms for maximum interpretation. Secondly, there are data sets that are generated at high velocity and scale such as the data generated by the sensors in autonomous cars per second per journey. Applying the right algorithm to this high-velocity data will make sense for the system to take right and better driving decisions. Lastly, for better analysis of the generated data, it is highly essential that data is fit into the most appropriate data model.

Without getting too technical, the best way to apply the right algorithms for IoT data would be to understand the concept of Smart Data. As you know, Big Data is all about high velocity, scale and variety and smart data is overcoming the challenges posed by these three factors and coming out with extensive pieces of information that would aid in decision making.

Besides, to understand algorithm application, it is also essential to understand three fundamental concepts – IoT applications, data characteristics, data-driven visions. To help you understand the intricacies of the subject, we recommend listening to this podcast by Ajit Jaokar.

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