When beginning to use your IoT data, a good starting point is to study differences between IoT platforms and IoT infrastructure, to understand the importance of a data platform, and to define business needs so that IoT data can be turned into a useful form that can be easily visualized. In addition, data science offers many other opportunities; only the sky is the limit when machine learning algorithms are applied to the field of IoT. I will cover examples of potential applications from three different points of view: predicting, optimising and identifying special events with the help of data.
Change your way of thinking from reactive to predictive
By using traditional IoT data analytics, combining data sources and reporting results you can monitor how devices operate, and maybe even find regularities and trends from the gathered data. If alerts are implemented correctly, you can react very fast to exceptions occurring in the data.
For example, if a part of a production machine breaks down, the measurement from the sensor connected to it will change and trigger an alert. Then it is possible to take necessary action to check the condition of the machine and to repair it. But even if the alert was received fast, damage has already happened, and in the worst-case scenario, the whole machine or the production plant may be out of use for the time it takes to check the fault and fix it. What if the breakdown could have been predicted?
Data scientist is your superhero – she changes your attitude towards IoT data from reactive to predictive.
If the collected data happens to include failures, a data scientist can find regularities related to the breakdown on the basis of equipment history data. With data science, even regularities difficult to notice based on reported data solely, can be identified. Especially by enriching collected data with other data sources, such as temperature, pressure or other relevant variables, new factors influencing the forecast can be found. In addition to predicting maintenance operations, forecasts can be generated, for instance, to predict the capacity or load of equipment, or any other key performance indicator.
However, data scientists can not perform miracles with your data on their own. Well-defined business problems and domain knowledge combined with data science skills are of primary importance.
Increase efficiency with optimisation
Predictive maintenance is only one example of how to use data in the field of IoT – although it has huge potential. However, the range of potential applications is immense. For instance, when you can predict the capacity or load of a machine or production reliably enough, the next reasonable step is to use these predictions and optimise production to enhance processes. If you know how to predict production downtimes, you can implement needed measures when they occur. For instance, maintenance windows can be scheduled to the predicted quiet times, and hence maximise the device availability. Optimisation can also be used to fine-tune production processes to improve quality and customer satisfaction.
Data science can create the competitive advantage that might be crucial for your production process.
Optimisation can be utilized in a variety of IoT applications outside production plants, too. For instance, a person’s real-time location in a building can be determined by combining data coming from multiple sensors. Furthermore, the way production plants optimise their use of quiet and busy times, can also be applied to traffic data to determine the best travel times to avoid traffic jams. In addition, forecasts can help to optimise the times when buildings are heated or even fuel consumption of vehicles. The sky is the limit!
Identify special events in your data
It can be difficult to find exceptions related to device breakdowns by only viewing your data. In practice, this kind of events occurring indirectly in the data can be found by applying pattern recognition in the data collected by one or more sensors. With the help of data, you can observe and predict when devices function abnormally.
Data science makes it possible to find regularities for abnormal events.
At best, the discovered regularities help predict maintenance needs, but they can be useful in many other ways, too. Typically, IoT devices send time series data that normally follows certain regularities. However, depending on connections and transfer methods, time series data can be partly inadequate, and hence difficult to analyse. Regularities can help to predict missing values to this kind of inadequate data set.
In addition to traditional sensors that concentrate on specific variables, also video streams are more and more used as IoT data sources. With the help of image recognition, you can identify chosen or abnormal events in video stream, too. For instance, you can check the number of available parking lots in real time on a video transmitted by a surveillance camera, and report it to drivers nearby. Machine learning can also be used to classify and recognize different kinds of sounds from recordings. As an example, a dog’s bark or a baby’s cry can be programmed to trigger an alert in the phone application of the dog owner or the baby’s parents.
There is so much potential in your IoT data – only a fraction of all the possible applications were discussed in this blog. Next, it is time to harness your data in your use.
