Home Internet of Things Aerospace Apparel Energy Defense Health Care Logistics Manufacturing Retail

Are Data Silos Holding Your IoT Strategy Back?

Rigid silos are preventing businesses from improving their productivity and quality by up to 30 percent by using new techniques in data processing and machine learning.
By Will Ochandarena
May 06, 2018

This is an exciting time for anyone working in the industrial space, whether in the manufacturing, oil and gas, mining or automotive sectors. Several companies with whom I've spoken believe that they can improve both productivity and quality by up to 30 percent during the next five years by using new techniques in data processing and machine learning, which is an order of magnitude higher than they would have even dreamed of five years ago. What has been holding them back? Rigid data silos.

The issue of data silos actually impacts companies across nearly all verticals, but I would argue it affects the industrial sector the most. Why? Because the data silos that exist in factories and industrial sites, usually called historians, are smaller in size and more limited in capability than those of other industries. Their limited size forces down-sampling of data, like collecting data points every minute instead of every second, as well as sprawl, since it may take multiple historian systems to capture data from all machines and sensors within a single factory.

This is, of course, made worse when you consider the "edge" problem: remote wells, refineries and mines that have their own ecosystem of machinery and sensors, all dangling off of spotty satellite or rural broadband connections. It's no wonder analysts and data scientists in this space have been struggling to show value.

Modern, scale-out data systems are finally making it easy to overcome these challenges. Such systems typically combine general-purpose data storage with file and database semantics, the ability to handle real-time streaming data, and the ability to perform analytics through both traditional SQL and programmatic machine learning. Some of these systems shrink down well into an edge-appropriate form factor, with built-in data replication for coordination with the mothership.

Most companies take their optimization journey in steps. An easy first step to take is to export data out of the various historian systems into the new data platform, and to use business intelligence dashboarding tools to look for patterns or correlations in the data that provide insight. This, however, doesn't really help the data-fidelity issue, so the next step is bypassing the historians and connecting machinery (PLC, DCS and the like) and sensors directly to the data platform, and cranking up the rate of data collection to once per second or higher.

Login and post your comment!

Not a member?

Signup for an account now to access all of the features of RFIDJournal.com!

Case Studies Features Best Practices How-Tos
Live Events Virtual Events Webinars
Simply enter a question for our experts.
RFID Journal LIVE! RFID in Health Care LIVE! LatAm LIVE! Brasil LIVE! Europe RFID Connect Virtual Events RFID Journal Awards Webinars Presentations