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Data Readiness 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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What Your Peers Think About Digital Transformation

Industrial companies are becoming increasingly seasoned and sophisticated in their exploration of data analytics. They’re aware of – and intrigued by – the promise of analytics but are increasingly focusing on getting down to business, encountering challenges and asking tough questions.

This means confronting cultural barriers (i.e. “this is how it’s always been done”) and working to ensure the broad availability of data and analytics (versus making them available only to an organization’s most motivated groups). They’re also moving away from addressing narrow challenges with data and instead treating data as a powerful, broadly applicable and dynamic asset. To first-movers in the industrial sector, data is an asset on par with capital, equipment or personnel.

These are some of the findings from Element Analytics’ workshop, “Digital Transformation Readiness” at the recent OSIsoft PI World.

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Top 10 Reasons Why Digital Transformation Strategies Fail

Industrial organizations are always looking for improvements in operations management. Every so often, a big leap forward kicks off a new epoch for industrial companies. For years now, visionaries have recognized the massive potential digital transformation holds to be the next big leap forward. It has the potential to be the next revolution in operational performance improvement, bringing prognostics, months ahead predictions, and root cause analysis. So, where are all the digital transformations then?

There are many different reasons as to why digital transformation attempts have been stymied for industrial organizations. At Element, we’ve heard about them all, from tools that can’t work with the data to teams that don’t understand the challenges involved. We’ve helped our customers overcome many of these through our software, and we wanted to share some of the most common reasons we see for why digital transformation initiatives stumble...

OSIsoft Accelerating Digital Transformation

For those of us who enjoy geeking out on industrial data, the OSIsoft User Conference (UC) is our Woodstock (Coachella for you Millennials), not just an event but a happening. UC’s pack hundreds of industrial data use cases into a few days, and are go-to events for engineers and IT professionals from diverse industries (oil and gas, biotech, power generation, data centers, pulp and paper) who come together to share best practices about how to solve hard problems with industrial data. 
 
While the UC has been around since 1991 in the U.S. (each Spring in San Francisco), its EMEA version is only five years old and the recent London UC had an impressive 1,500+ people attend.  Our Element Analytics team is a regular UC participant.  We like to learn what’s new in OSIsoft’s world, how companies are solving problems with their data and how Element can collaborate with them. 
 
Here are our takeaways from the London EMEA UC...

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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