Asset Engineers have been analysing asset data in the pursuit of high value outcomes for only about 2500 years. With the advent of machine learning and cloud computing infrastructure, our industry became convinced that a completely novel approach was required, and the direction should be provided by the banking industry, because this industry is leading the world in modern data analytics capabilities. Our experience indicates that nothing could be further from the truth and the formula that works best for Assets is about evolving traditional Asset Engineering capabilities rather than revolutionising the entire sector.

In this presentation we will explore two vastly different approaches to building a modern Engineering Analytics capability specifically geared for an Asset Intensive company. We will look at the positives and negatives of each model and deep dive how each model works. We will try to debunk a few myths including AI, Big Data and cloud infrastructure requirements and clear the air in terms that an Asset Engineering team can understand and use.

We’ll also present a case study from our adopted Engineering Analytics model such that we can see the value of success and understand the reduced cost to achieve these outcomes sustainably.