Data in the hands of many creates a competitive advantage!

Chemical companies continuously strive to optimize process performance and improve overall equipment effectiveness. Some are already using big data and are embracing the concepts of Industry 4.0. But what makes these companies outperform their competition?

Making information out of “big data" often requires data scientist creating a data model to come to conclusions on how to improve operational performance. A form of big data is sensor generated process data, typically captured in the historian. But still process engineers often need to rely on data scientists for analyzing process performance. Wouldn’t it be great if they can leverage their expertise and avoid to build complex analytics models to find root causes for process deviations?

In this webinar, Ruchika Tawani, Data Analytics Engineer, TrendMiner will discuss how process engineers and other operational experts can analyze and improve.


  • Doing more with data
  • What is self-service analytics?
  • What is TrendMiner?
  • Who is using TrendMiner?  (references)
  • Use case/Demo about
    • Batch Analytics
    • Continuous Anaytics
    • Asset Analytics
    • Predictive Analytics
  • Benefits for the Chemical Industry

The webinar is specifically of interest to plant managers and process engineers who want to know how they can improve operational performance. The practical use cases shown in the webinar help you better understand why democratization of analytics can help accelerate your organizational goals, such as reducing downtime, increasing yield, control quality, improve asset reliability and increase overall profitability.

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Ms. Ruchika Tawani Data Analytics Engineer TrendMiner

Having studied Masters in Chemical Engineering, Ruchika brings a strong technical background to the team in order to understand the needs of our customers.
Joining TrendMiner in 2018 after graduation from TU Dortmund, as part of the Customer Success team she has been working with customers hand in hand to unlock the full potential of their process data.