The data transformed in the previous step becomes the input for the analytics step. Depending on the problem at hand, there are different types of analytics that can be performed on transformed data.
The following are the different types of analytics that can be performed:
- Descriptive Analytics: This type of analytics helps in finding patterns and details about the statuses of IoT devices and their overall health. This stage identifies and summarizes the data for further consumption by more advanced stages of analytics. It will help in summarization, finding statistics related to probability, identifying deviation, and other statistical tasks.
- Diagnostic Analytics: This type of analytics is more advanced than descriptive analytics. It builds on top of descriptive analytics and tries to answer queries about why things happened. It tries to find the root causes of events. It tries to find answers using advanced concepts, such as hypothesis and correlation.
- Predictive Analytics: This type of analytics tries to predict things that have a high probability of happening in future. It's about prediction based on past data. Regression is one of the examples based on past data. Predictive analytics could, for example, predict the price of a car, behavior of a stock in the stock market, when a car tire will burst, and so on.
- Prescriptive Analytics: This type of analytics is the most advanced. This stage helps in identifying the action that should be executed to ensure that the health of devices and solutions do not degrade, and identifying proactive measures to undertake. The results of this stage of analytics help in avoiding future issues and eliminating the problems at their root causes.