Summary

This chapter covered the importance of analytics in the context of IoT. This process converts raw data that’s received from diverse sources (static data sources such as enterprise systems, as well as dynamic data sources – for example, data received in real time from sensors) into meaningful insights, which is a prerequisite for effective/efficient decision making. We covered specific considerations that you need to be aware of while tailoring generic analytics for IoT applications. Then, we covered the steps that are normally followed in any analytics data pipeline. We also looked at the benefits and nuances of edge analytics, where data is processed close to the data source.

After that, we introduced edge analytics; we will cover this in more detail in Chapter 12, Exploring Synergies with Emerging Technologies. We looked at the importance of data quality and how it can be ensured at the different layers of the IoT stack, as well as the importance of IoT visualizations...