An Overview: Technical Terms Used in Digital Transformation
Are you bombarded with different terms in digital transformation you don’t understand? This article defines some of the terms we use in heavy-asset industries.
As we’ve talked about earlier, industrial companies now have the ability to interact with physical equipment like never before. You’ve probably also heard that there’s an urgency for you to get started with your digital transformation. However, you might be bombarded with different terms you don’t understand. Here I’ll give you definitions of some terms we often use in relation to digital transformation in heavy-asset industries.
Advanced analytics is the collection of sophisticated modeling techniques used to investigate data or content to gain deeper insights, predict future events, discover patterns and generate recommendations. Some of the different methods or techniques within advanced analytics include:
- data/text mining
- descriptive modeling
- statistical/quantitative analysis
- machine learning
- pattern matching
- semantic analysis
- sentiment analysis
- network and cluster analysis
- multivariate statistics graph analysis
- simulation and optimization
- neural networks
Anomaly detection (a.k.a. outlier detection) is a class of problems, where the goal is to determine behavior that’s sufficiently different from the nominal (or normal) behavior patterns. In the context of industrial settings, anomalies are unexpected or undesired behavior. Typically, anomalies occur rarely in well-behaved systems. Anomaly detection is one of the core techniques in data-driven maintenance.
ARTIFICIAL INTELLIGENCE (AI)
Artificial intelligence is the ability of a machine to perform tasks that would otherwise require human-like intelligence in some form. Common approaches to artificially intelligent systems are machine learning-based or rules-based systems, with machine learning being the currently favored approach.
Computer vision is how we can enable computers to "see" and gain understanding of digital images in the same way humans do (or even surpass humans). The goal is to extract meaning from pixels. Computer vision is acquiring, processing, analyzing and understanding these digital images and extract high-dimensional data from the real world to produce numerical or symbolic information, e.g. in the form of decisions.
Deep learning refers to a type of machine learning that uses artificial neural networks. Deep neural networks are "deep" because they are composed of many layers, allowing them to extract patterns in the data in a hierarchical way. Deep learning has excelled at tasks related to images and language, among others.
Edge analytics is an approach where you collect and analyze data at a non-central point, such as the sensor, device or touchpoint itself. Analyzing data as it’s generated lowers companies’ costs and increases the ability to drive timely, valuable business insights from all of the data to inform actions and decisions at remote sites.