Data Readiness Blogs

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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The Evolution of Digital Twins for Asset Operators

In Part I of this series on Digital Twins (DT) Andy crafted a great explanation of what a DT is, why it's need and the guiding principles on building DTs for Asset Operators. In Part II, I’ll address is the evolution of how we, as industrial asset operators will go through in adopting Digital Twins.

To refresh, a digital twin is a dynamic digital representation of the physical environment. We’re mostly familiar with DTs from the consumer world as the apps on our phones that manage your Nest Thermostats, the Philips Hue Light System’s color of the lights, the information about a Fitbit, or to summon a Tesla.

Unfortunately, for asset operators, these app-based consumer DTs don’t scale to the hundreds of thousands, even  millions, of measurement data streams required to operate modern industrial equipment and assets, whether it’s a discrete manufacturing line, oil refinery, or a large scale film production studio.

Two major trends, while creating a lot of marketing buzz, are pressuring asset operators and their supporting IT organization to act:

  1. Greater connectivity of our equipment with more sensing, typically referred to as “IIoT” or the Industrial Internet of Things
  2. Cheaper compute and storage allowing for more powerful analysis and operational improvement, a trend typically referred to as “Industry 4.0” or “Digital Transformation”

To connect those two trends you need digital twins. But which digital twin should asset operators pursue and how should they get started? We see DT’s evolving in stages, and the good news is that through past investments many companies already have ingredients in place to begin (and many have already started and not realized) their journey.


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