Industrial Data Blogs

Sameer Kalwani Heathcliff Howland
Dec 3, 2019

Sameer Kalwani

Heathcliff Howland

OSIsoft customers know they need Asset Frameworks. But few are deploying them. Why?

OSIsoft customers across a spectrum of industries see a huge need for Asset Frameworks for their PI System data. Despite this, only a small percentage have deployed these capabilities. Companies that are using asset frameworks generally aren’t happy, complaining about the lack of quality and consistency. And few companies – less than one in five – have taken steps to make their asset frameworks more valuable by adding data related to quality, financials and other metrics.

So, what is the hold up on AF Adoption? What are the challenges? More importantly, what are the solutions?

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The missing link for digital transformation: Mastering the asset data domain

Having spent the most of my career serving industrial clients’ Information Technology needs, I found the week I spent at the 23rd ARC Industry Forum very interesting indeed. The conference was entitled “Driving Digital Transformation in Industry & Cities.” The conference was chock-full of presentations on industrial use cases & their potential value to the enterprise. I spoke with many attendees and there is an excitement I’ve not seen in a while, that the industry is producing truly digital outcomes that deliver on the promise of Industrial Digital Transformation.

On the IT side of the business you don’t hear “Digital Transformation” as frequently. It’s a given that the information domains supported by IT – Financial, Supply Chain, People & Customers - are already digital. Large Industrials have delivered on a technology stack to “master” these domains and as a result – for the most part – this data is widely used throughout the enterprise and is easily delivered via highly reliable, secure, scalable & trusted enterprise systems. IT analytics, whether analyzing marketing campaign effectiveness or product revenue trends, is performed with ease by a cadre of data scientists and analysts.

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Treat the Whole Machine

360 Degree View of the Patient

Lately, my daughter and I have been binge watching doctor shows. In most episodes, patients are wheeled in presenting symptoms that need immediate attention. The doctors and surgeons work the obvious symptoms but sometimes prescribe treatments that don't address or, even worse, even mask more severe, underlying problems. Our heroes, Dr. Sean Murphy in The Good Doctor and Nurse Nic in The Resident, save the day by picking up bits of patient context from a conversation with a family member, an overlooked x-ray, a buried report or some other stray clue that leads to a brilliant moment of clarity and a prescriptive recommendation that saves the patient's life.

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Architecture of a Digital Twin Service

In part one of this blog series, Andy defined digital twins and their importance for asset operators, and in part two, Sameer gave an overview of the path of digital twin evolution. In the final post of this three part series, I'm going to discuss the process for building and managing Digital Twins (DT’s) throughout their evolution in order for them to provide value to asset operators.  I’ll touch on the four main stages for building and managing DT’s: Data Collection, Data Pipelines, Data Egress, and Data Integrity.

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Why Asset Operators Should Have Their Own Digital Twins

Digital Twins are a hot topic, landing on Tech analyst Gartner’s famed Hype Cycle and named a Top Ten Strategic Technology Trend for 2017.  Not a new idea (credit to Dr. Michael Grieves, 2002, Univ Michigan), Digital Twins are now at the forefront of Industrial /Internet of Things zeitgeist because they are essential to unlocking I/IoT value by enabling analysis, simulation and control of physical things and systems.

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