from The operational brain: A new paradigm for intelligent data management in the industrial IoT
by Anasia D’mello
The Industrial Internet of Thing (IIoT) promises to allow organisations to deploy ever-increasing volumes of machine and sensor data to optimise myriad production processes, enhance security, and improve the worker experience (whether that employee is on the factory floor or in the office).
Industrial businesses, says Christian Lutz, CEO, Crate.io, are finding out that old paradigms of data processing don’t help their teams keep up with the speed of data, don’t match new analytic algorithms and, perhaps most critically, don’t enable the competitive need for real-time data queries.
An approach for solving this problem is combining modern distributed (open source) database architectures with Machine Learning/Artificial Intelligence, and IIoT networks. Together, these technologies form a rather new data management paradigm – what I’d call the operational brain – that goes beyond the traditional notions of databases and solves increasing data issues acute to industrial and manufacturing businesses.
Defining the operational brain
Traditional relational databases (such as Microsoft, SQL Server and Oracle) are technically incapable, usually, of processing the massive volume of data that must be handled for IIoT applications to be successful. These databases really weren’t designed to create the kind of backbone required to develop smart factories, smart cities, or driverless vehicles; use cases like these demand faster and more intelligent data processing. A comprehensive database management strategy is ultimately measured by the added business value of its use – not by its amount of memory or the hard disk’s speed.
I call this type of comprehensive IIoT data management “the operational brain.” The brain is the organ that can receive, structure, and make decisions based on this data. The data management system of the future will invariably function like our central nervous system, connecting directly to sensory impressions and using artificial intelligence to monitor, predict, and control systems in real time.
Data acquisition and enrichment
The modern, networked factory incorporates a diverse array of machines from different manufacturers. The challenge is thus to capture dissimilar data structures, analyse them in the cloud, and derive actions from them. Modern data management systems already start here. This simplifies the implementation and reduces error rates, since the machine and the database do not communicate via a third instance.
Without context, the collected data is useless for further processing. A recorded value is initially just a number: 108. Is that a temperature? If so, is it Celsius or Fahrenheit? Is it a product count? If so, when was the counter reset and what does it actually count? Data needs to be enriched to be meaningful. This enrichment requires three components: a database, a runtime that executes certain rules, and knowledge about the data’s meaning.
The operational brain combines all of these necessary steps into a single model. It saves industrial organisations from writing algorithms in order to make the data available for further processing. Instead, rules can be set up to interpret the stream of processed information. The operational brain is essentially the machine responsible for executing rules that automate processes, improving overall equipment effectiveness (OEE) in factories. It uses real-time […]
The post The operational brain: A new paradigm for intelligent data management in the industrial IoT appeared first on IoT Now – How to run an IoT enabled business.