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Time-Series Data Blogs

We all Need a Pair of Blue Jeans and a Shovel

Who Actually Struck it Rich?

The most memorable stories from the California Gold Rush focus on the lucky souls who struck it rich. Perhaps just as interesting were those who supplied the tools that the miners needed to be successful -- the picks, shovels, and pans. Some of these suppliers were amazingly innovative and entrepreneurial: Levi Strauss (along with partner Jacob Davis who patented the copper rivet reinforcement in the heavy-duty pants) famously supplied denim jeans. More than 150 years later, Strauss’ company is still successfully supplying jeans, and more, to people around the world.

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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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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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4 Strategies for Getting Industrial Sensor Data onto the Cloud

I talk to a lot of IT leaders from industrial organizations who are working to get more value from the mountains of industrial data they collect by turning it into advanced analytics. It’s especially hard for them to determine a path forward, to find signals amongst the noisy bombardment from Industrial Internet of Things vendors. Pressure increases when the CEO returns from Davos wanting to know “When will Industry 4.0 be ready?”

Getting started quickly while containing risk is top of mind for these industrial IT leaders. Time and again we find ourselves discussing the same topics, especially why and how they should move analytical workloads to the cloud. The cost and performance advantages of moving analytics to the cloud are well understood, but IT leaders have concerns around how this move can be done in a scalable and non-disruptive way.

Because the cloud can unlock the greatest value from time-series sensor data and enable advanced analytics, we talk to IT leaders about these 4 strategies that we’ve adopted to help them on their way...

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