The first Research Spotlight in our biennial research series, Analytics That Matter, co-created with LNS Research, digs into the emerging space of Advanced Industrial Analytics, providing need to know best practices and insights on proven strategies to scale transformation during today's uncertain times.
Today's industrial companies claim to be using Advanced Analytics in many forms. However, not all of them are advanced, and consequently, many do not meet LNS Research's definition of Advanced Industrial Analytics.
Advanced Industrial Analytics goes beyond traditional statistical, rules-based, or first principles-based analysis and blends this with machine learning/artificial intelligence models, ranging from Descriptive to Prescriptive, and Prognostic.
According to LNS' research, Advanced Industrial Analytics must:
- Address multiple industrial use cases out of the box to speed time to value. Manufacturers don't want to have to deploy a new vendor every time they want to pursue a new use case.
- Leverage sophisticated mathematical models. More advanced companies are taking it a step further (past blending traditional statistical rules with ML and AI models by including these models as part of a self-learning system.
- Be digitally native and create value from a modern operational architecture
- Target specific layers and roles
- Analyze large heterogeneous datasets