“If we have a look at the data usage and service in the industry, we have a first group of people who are engineers and operators. These people know their processes very well and are experts who know about their working and also usually know where the problems are. We call them ‘Analytics Aware Group’ because they perform the scripted diagnostics and analytics. So, they are able to examine their historical data and interpret it well. They generally use the trends from historians and spreadsheets such as MS Excel which has its limitations while dealing with huge data sets. On the other hand we have 'Express Group' who have knowledge in scripting and are using tools such as Python and are able to do predictive and prospective analytics. They can use programming languages to develop models, do scripting through simulations and other processes. But they usually don’t have the practical experience of engineers and operators,” says Sabine Pietruch, Data Analytics Engineer, TrendMiner.

“Therefore, these two groups must work closely to achieve success. The knowledge of these two groups is complementary and there should be flawless communication for efficient outcomes,” adds Sabine who shared her presentation titled, ‘Closing the Gap between Data Science & Process Engineering’ at the NextGen Chemicals & Petrochemicals Summit 2021 organized by Indian Chemical News on 7-8 October, 2021. 45 percent of the largest chemical companies globally are using TrendMiner, says Sabine.

"The purpose of self-service tools such as TrendMiner is to democratize advanced analytics. It is an advanced self service analytics platform for operational specialists in the process manufacturing industries. It is based on sensor generated data to analyze, monitor and predict production performance within its operational context. This easy to use tool allows engineers to perform data analytics and the engineers and operators don’t rely on excel anymore because, with this, they can handle huge data sets,” added Sabine.

“On the other hand, for an expert group, they can have a deeper understanding to contextualize their data. It has power tools for time series data, not only the data from historians but we are also able to connect data analysts to IoT platforms and so on. It gives a better understanding of data for people without much expertise can use the tool and it is easily accessible besides we also provide help to the customers. Summing up, Trendminer is bridging data science and process engineering with the help of a self -service analytical tool,” Sabine says in conclusion.