Solve historical challenges and improve plant profitability through analytics-based insights.
by Edwin van Dijk
September 27, 2017

By sharing analytics insights with users, they are able to take immediate action when a trend appears and directly contribute to improving overall plant performance at all levels of production.

Enabling a Modern Engineering Analytics Organization

Just as technology has evolved to create connected plants, so engineers must be empowered to manage these facilities. This is a critical shift in business culture, since the entire organization must understand the potential of analytics as it applies to their role.

Instead of relying solely on a central analytics team that owns all the analytics expertise, subject matter experts, such as process engineers, are empowered to answer their own day-to-day questions. Not only will this spread the benefits to all engineers involved in process management, it will also free the data scientists to focus on other critical business issues.

Enabling engineers does not mean asking them to become data scientists. It means providing them access to the benefits of process data analytics. Process engineers will not become data scientists because their education background is different (computer science versus chemical engineering). However, they can become analytics aware and enabled.

This process is sometimes referred to as “the rise of the citizen data scientist,” a growing trend in which experts in their own disciplines (such as engineering) add analytics capabilities to their core competencies rather than splitting the analysis from the data.

Involving engineers in analytics allows them to solve more day-to-day questions independently and increase their own effectiveness. They in turn provide their organizations with new insights based on their specific expertise in engineering. This delivers value to the owner-operator at all levels of the organization and leverages (human) resources more efficiently.


Many people wonder if these analytics is worth the time needed to get started, and the answer usually yes. With a self-service industrial analytics tool, benefits may be great but the time investment is small­—especially for a company that’s not ready to invest in on-site data scientists. With a self-service industrial analytics solution, users don’t have to wait while a data model is selected and built. Immediately after deployment, they can begin analyzing the historic and live performance data from assets and processes. Additionally, the software is plug-and-play, often implemented by in-house Information Technology (IT) teams within an hour. Likewise, involved training is not required because the simple interface is easy to operate and quick to learn.