Data Science is an increasingly common term in the chemical industry. And even though it is a fairly new area within our industry, its added value is growing. The Sitech Asset Health Center has its own Data and Technology department, consisting of Data Scientists and Data Engineers. How do we use Data Science in the chemical industry?
It’s a fact: (big) data is becoming more and more important. It is said that more data equals more knowledge. However, it is important to know exactly which data is valuable for the processes within an organization. Our Data Scientists can help with this. While our Data Engineers make it possible to collect large amounts of data, Data Scientists look to discover hidden patterns in that data. And that leads to valuable information. For example, these insights can be used to identify possible factory installation failures in good time.
Data Science starts with cleaning and preparing raw data. This task takes the most time, because it is often necessary to fill in missing data and/or convert it to the correct format. ‘Machine learning’ algorithms and models are then applied in order to understand the data, extract knowledge from it and formulate actions. The way we clean data and the algorithms we use for training determine the quality. The methodology is thus a collection of knowledge from statistics, computer science and research methodology.
Within the Sitech Asset Health Center, we work closely with Engineers and Technicians in the factory. Thanks to their many years of experience in the field of maintenance, we understand the challenges of the industry like no other. We combine this knowledge with the expertise of our Data Scientists. And it is precisely this combination that enables us to predict the health of assets accurately and at an early stage, in a clear and reliable manner.