Big Data shouldn’t be too big to manage
Fulfilling Big Data's promise starts with making sense of the information.
With the massive amount of information currently residing in existing enterprise asset management (EAM) systems, real-time systems, historians, devices, and condition monitoring systems, the promise of Big Data has yet to be fulfilled: using it to help your company stay competitive, profitable, and innovative.
The challenge with Big Data is making sense of all the information and putting it in the proper context: using it for actionable analysis and predictive modeling to determine what, if anything should be done to change the way the asset is managed. Without a reliable and consistent ability to process, analyze, understand, and make decisions on the information contained within these massive unused data sets, expending resources to accumulate that data is questionable.
An effective approach implements an asset performance management (APM) program that analyzes data across the entire organization to understand asset criticality and risk. By automating the Big Data analysis, ranking asset needs by priority, and generating actionable plans, APM programs can help plant operators identify emerging trends and take immediate action to mitigate any risk of failure.
Understanding criticality and risk
All operators understand that asset failure could result in the injury of people, the environment, and business investments. It is therefore crucial to have the technology needed to flag potential risk and proactively avoid it. Today many organizations are managing multiple systems and solving one problem at a time rather than considering long-term costs and strategic implications. In almost any manufacturing plant, you will find a menagerie of technology and processes to collect data on equipment assets. When systems are operating in silos across an organization, they generate disparate data sets and potentially cause hours of lost productivity through rework, overlap, and confusion.
Consolidating all of the data into a single, secured location, making it available across all levels of an organization, and in the desired form, is the first step to wrestling control of your operating and production environment. A unified data set that pulls from every connected device can help businesses benchmark the performance of individual sites against that of others in the organization and even against industry peers around the globe to get a true picture of how well they're operating.
The next step is figuring out how to manage all that data. How do you manage those billions of bits of data from all those devices? How do you know if you are effectively applying smart technology to the right assets? Some assets may not need to be as "smart" as others or outfitted with advanced sensors, and it's critical to understand which assets, data, and symptoms you need to be monitoring. More importantly, are you analyzing the right data to get the key insights to act upon? With all this information, are you bringing it together in context and with a set of analytical tools to make sense of it and drive action from that?
The promise of the Industrial Internet of Things (IIoT) is real, but for all this new, accurate data to have value, industry will require more than OEM-specific M2M platforms or standalone point solutions. Enabling the IIoT requires connecting disparate information sources, creating storage capacity for exploding volumes of Big Data, comprehensive analytics, expert content presented in context, and most importantly, intelligent asset strategies. These strategies are key to the IIoT as they use both analytical and comparative analysis to provide a platform to integrate asset data from existing and new sources. Intelligent asset strategies are also a driver for the critical conversion of that data into actionable information to predict how and when an asset will fail and the recommendation of actions to prevent that failure.
To ensure that organizations are taking full advantage of IIoT, making strategic business decisions, and seeing a return on their investment, they must be able to draw insights from data within the plants across their facilities and against the performance of other organizations in their industry. APM programs can help companies connect their disparate plant systems and use all this data to create and manage the intelligent asset strategies that will predict and prevent failures as well as drive improved asset performance and reliability, reduce operational risk, and optimize costs. As organizations manage ever-growing groups and types of assets, the amount of data created by connected devices will increase exponentially. But all that data will only provide value if integrated with enterprise systems and used to enable more informed business decisions. When accurately managed, Big Data generated by connected assets keeps facilities more efficient and creates a safer environment.
William E. Amos, PhD, is Chief Technology Officer at Meridium.
- Events & Awards
- Magazine Archives
- Oil & Gas Engineering
- Salary Survey
- Digital Reports
- Survey Prize Winners
Annual Salary Survey
Before the calendar turned, 2016 already had the makings of a pivotal year for manufacturing, and for the world.
There were the big events for the year, including the United States as Partner Country at Hannover Messe in April and the 2016 International Manufacturing Technology Show in Chicago in September. There's also the matter of the U.S. presidential elections in November, which promise to shape policy in manufacturing for years to come.
But the year started with global economic turmoil, as a slowdown in Chinese manufacturing triggered a worldwide stock hiccup that sent values plummeting. The continued plunge in world oil prices has resulted in a slowdown in exploration and, by extension, the manufacture of exploration equipment.
Read more: 2015 Salary Survey