Understanding IoT Data

In the fourth and penultimate post in our blog series about the A - Z of Data Science for IoT, we’re going to discuss IoT data and how it’s understood and analysed. IoT analytics can help companies understand the IoT data at their disposal, in order to reduce maintenance costs, avoid equipment failures and improve business operations. Furthermore, retailers, restaurant chains and goods manufacturers can use data from connected devices to launch targeted marketing and promotional campaigns.

So, let’s take a look at the key processes to extracting insight from this data:

  • Sending the data: The sensor or smart device creates data from each and every event and sends it over the Internet using an IoT protocol like MQTT, Mosquitto or RabbitMQ, to the central system. Depending on the device, the network and power consumption restraints, data can be sent in real time, or in batches at any time.

  • Storing the data: Data is collected, stored and, sometimes, organised, in a database such as Hadoop or Cassandra.

  • Analysing the data: The next step in the process uses tools such as Spark, an open-source cluster-computing framework, to look for trends over time and start making predictions based on behaviour patterns.

  • Making smart decisions: Companies take action based on these predictions and trends and see improvement in their business processes.

So, in order to understand IoT data, companies need to implement data integration and analytics tools, so that their data scientists can collect and analyse both the structured and unstructured data collected from sensors. Hadoop, for example, is a an open-source software which is used for storing big data and processing across commodity hardware. Implementing these technologies, however, usually involves updating the IT architecture of the company, since a traditional architecture is not sophisticated enough to support these tools or even collect such huge amounts of data. Companies are now deploying cloud analytics solutions as they offer a faster route to insight-driven decision-making and business outcomes or using fog computing, which connects sensors to cloud computing resources to enable rapid, actionable decisions.