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Element Platform Blogs

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...

Using Asset Data Models to Empower Your Industrial Organization

When I speak with CIOs and their staff, the topic of digital transformation always leads to a discussion around how time-series data is the starting point, but it’s difficult to work with and organize in a way that represents how equipment and assets exist in the physical world.

Industrial companies have begun to address the problem by adopting Asset Data Models, which represent the physical structures and relationships of industrial equipment and processes. Asset Data Models are crucial for equipment benchmarking, cross-site comparisons, and underpin every kind of analytics. 

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For Data Quality, Sensor Trust Is Key

In 2009, I led R&D and Technology Operations at Piramal Sarvajal, whose mission is to provide affordable access to safe drinking water for Indians in underserved areas. The large-scale, centralized water utility model had failed to get clean water to those most in need, so we set out to create a distributed network of water purification units and water ATMs, which today provide clean drinking water to 300,000+ Indians across 12 states.

To deploy those systems in a cost-efficient way, we developed new technology to manage maintenance, ensure water quality, and prevent theft. We began by deploying new control systems: a GSM modem-connected programmatic logic controller (PLC). All PLC data was stored in our cloud-based Data Historian. We were one of the first to centralize operational time-series data, aggregating sensor data from 200+ geographically dispersed assets.

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