Data Analytics in IoT

What is Data Analytics?

By networking devices and collecting valuable telemetry information from the environment, analysis is becoming increasingly important in IoT and IIoT-based systems. Data analytics comes into play when data-driven decisions are required and diagnostics are to be carried out using IoT device data. Put simply, data analytics is the process of systematically analysing data in order to gain valuable insights and encompasses a wide range of different techniques and methods, from simple statistics to data science methods.

Apects of data analytics:

1. Data acquisition
   • e.g. databases with sensor values (time series data).
2. Data preparation
   • Raw data must be cleaned, filtered and organised before analysis.
3. Data analysis
   • Descriptive analysis: Describes what has happened in the past.
   • Diagnostic analysis: Analyses the reasons for a particular result.
   • Predictive analysis: Uses past data to predict future events.
   • Prescriptive analysis: Gives recommendations for action based on the knowledge gained.
4. Data visualization
   • Visualisation through diagrams, graphics or dashboards.
5. Interpretation und application
   • Analysis results must be interpreted correctly in order to make decisions.

Data generation, data collection, data storage, data transformation, data analysis, insights and actions are the most important steps in IoT data analysis. The advantage of a data pipeline is the management of data from the edge to the user. It covers the entire data pipeline flow of IoT data, starting with IoT sensors and ending with a data lake, an analytical layer and a visualisation layer.

Why is data analytics important for diagnostics in the IoT sector?

It plays a central role in the IoT (Internet of Things) and IIoT (Industrial Internet of Things) sector, as it enables the immense amount of data from networked devices and sensors to be analysed and valuable insights to be gained.

• Predictive Maintenance:Enables early detection of machine failure, allowing companies to avoid costly breakdowns and extend machine life.
• Fault diagnosis and rectification: Real-time analyses can be used, for example, to quickly identify where an error has occurred and are particularly valuable when several devices and systems are networked together.
• Optimising efficiency: Targeted analyses can be used to optimise the energy consumption and operating time of devices, for example.
  Improved quality assurance: In production lines, analysing IoT data can help to identify weak points in quality assurance and thus improve product quality.

Conclusion: Data analytics in the IoT and IIoT area enables detailed and predictive diagnostics

Diagnostic data forms the basis for process optimisation throughout the entire IoT lifecycle of end devices. A company should therefore always draw up an efficient data analytics plan in order to gain a competitive edge.

How do I find out what data analytics plan my company needs and how can ithinx help?

The first step is to take an initial inventory of your system and data and draw up a customised data analytics plan. An end-to-end flow for your company’s internal data analysis and intelligent IoT diagnostics can then be professionally set up.

If you’re not sure but would like to find out, talk to us. As part of smaller consulting projects, we can help you with the assessment and create a plan on how to achieve better data maturity.