From data to actionable intelligent data

Data have become indispensable. Both at home and at work, we are flooded with bits and bytes. As such, the importance of data in an industrial environment can no longer be ignored or underestimated. Cheaper sensors and more powerful computers have resulted into an exponential accumulation of data. But how can companies benefit the most from all these data?

An increasingly efficient production is one of the most commonly used methods to lower costs and stay ahead of the competition. Lately, we’ve noticed that the use of data has become ever more important for this. Think of smart, predictive maintenance of production lines in which the decision when a particular part should be replaced is based on data. We are also able to manufacture ever better products and, as these products remain connected, we can even upgrade them during their lifecycle. Also products generate data throughout their lifecycle. By analysing these data, companies can gain important insights on how their products are used. When, subsequently, these companies improve their products on the basis of these analyses, they will have completed the circle and obtain a not inconsiderable competitive edge. In addition, companies will be able to develop and implement new, innovative business models

Still, to be able to properly process such huge amounts of data, a number of conditions must be fulfilled, including:

1. A reliable and fast internet connection (5G networks will play a major part in this)
2. Sufficient storage capacity (this storage capacity is necessary for storing and distributing data and may be situated in the Cloud, in the Edge or on the device itself)

You can convert data into intelligent data using statistical and artificial intelligence algorithms. Intelligent data allow to identify opportunities for improvement.

The adjustment of model parameters through neural networks will enable you to better determine and understand the origin of your data. An example of this is a screen on which product or production parameters can be read. In this way, the operator receives feedback on the condition of the machine(s) and make adjustments where necessary. A problem here, however, is that many neural networks operate in an industrial context through the black box principle. The system is not able to explain why it gives a specific output. This explains the demand for explainable AI. By applying reverse modelling filtering, we can now separate part of the black box and make data understandable and richer.

When you understand data, you can very rapidly assess the risks of certain decisions. Only by creating a maximally transparent data flow, the need for change and improvement will become clear and you will be able to evaluate your decisions as objectively as possible. Besides, it is also a tool to identify opportunities for improvement.