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IoT Data’s Human Component: A Q&A with Tim Butler

“…for companies who are grappling with IoT integration … edge computing is becoming a prime opportunity for enhanced employee performance and contribution to the whole.”

Thus begins Tim Butler’s Q&A session with the incomparable David Marshall of VM Blog, who knows quite a bit about the important technology trends and what they mean to business practitioners.

Pegged to the recent AWS launch of Greengrass, an initiative that takes on many of the same issues that Tego’s been working for years to solve, Tim provides ranging perspective on the value that edge computing stands to unlock. Highlights from the session include:

Why did AWS choose to launch on-premises compute solution?

Greengrass seems to explicitly acknowledge that constant data connectivity on edge devices isn’t easy, and it isn’t cheap. Instead, an approach that focuses on the “T” in the IoT (i.e. the “Thing”), which turns parts, components and other objects into smart conveyors of information, does not require constant connectivity or complex software integrations. The trick is in finding an easier way for these things to share their data, which is where AWS appears to be devoting its attention.

Does Greengrass give the IoT a boost to “cross the chasm” into large-scale digitization initiatives?

Wider recognition of the value of placing intelligence on the “T” in the IoT is analogous to companies realizing in the 1980s and 90s that moving away from mainframe computing architectures to desktop PCs could empower their companies to accomplish more. Instead of being locked into singular work streams from their “dumb green” digital terminals, employees could now read, write and store data locally, further their knowledge and understanding, broaden their work context, and produce more powerful daily outcomes.

How do you see the human dynamic changing as edge computing gains steam?

In today’s work environment, it is not uncommon for an employee at the edge to start to feel disconnected, or to feel like they’ve been made into an automaton. However, when data travels with an object, and that object becomes progressively more informed each time it interacts with a human, a funny thing happens. Humans can suddenly absorb and contribute to the organization’s intelligence in ways that add more meaning and context to their roles.

We believe edge computing will yield a better sense of engagement for employees at the edge, empower them to more personally contribute more often to a final outcome. It may just become the perfect expression of man and machine working together.

Read the full article here.

To learn about Tego’s asset intelligence platform for high-value edge computing, visit this page.

To schedule a demo for how Tego can improve your local data strategy, contact us here.


With Greengrass launch, Amazon validates market readiness for IoT data processing and enhanced edge computing

By Timothy Butler, CEO

 

With its introduction last week of Greengrass, AWS paid a huge favor to anyone seeking to extract more value from IoT data. In his blog, AWS’s CTO Werner Vogels describes three “laws” that define why localized data processing (a.k.a., edge computing) is important:

Physics. It takes time to send data to the cloud, and networks don’t have 100% availability. Customers in physically remote environments, such as mining and agriculture, cannot afford to let these issues affect their operations.

Economics. The IoT creates a lot of data, much of it low-value. Businesses need to be able to keep and conduct analysis only the high-value data.

Regulations. Legal requirements often call for data to be isolated, duplicated or handled in a very specific way. Some governments even impose restrictions on where data may be stored or processed. (I.e., data cannot be transported physically or electronically at all).

On several fronts, Amazon sees the same opportunity as Tego. Both organizations are truly endeavoring to break down barriers for IoT adoption that stem from a need for always-on connectivity. The company also reinforces much of what Tego has understood for years, that there is tremendous power in getting data closer to assets, so that decision-making can be made by workers who are in the fray. Sometimes, the most important decisions can only be informed from short-lived data, and at a precise moment in time. Once that moment’s passed, the data loses its value, and the opportunity is lost.

Even still, Amazon seems to lack real understanding about where the transformative effects of edge computing lie. Where does the data originate? Is it able to share a unique historical context with employees? Does it allow on-site personnel to improve downstream outcomes? This is where putting data onto assets becomes the missing link. When data travels with an object, and is able to become progressively more detailed at every point of human interaction, humans can absorb and contribute to the organization’s intelligence in ways that add meaning to their roles. Edge computing is not just about creating faster, quicker, data. It’s about finding better ways to use data, and that requires active employees who are enabled to own the process.

Greengrass appears destined to help with decisions at the point of read, but another question lingers: can it likewise make human assets more valuable? Big, enterprise value has to start with a small, specific improvements in job roles and performance. That’s what will drive positive, enterprise-level outcomes. An airline empowers its ground crews to reduce time on the tarmac and thus improve overall profitability; medicines prove their authenticity to an aftermarket caregiver through an embedded, digital signature with NSA-level encryption, and expand trust for a brand; line workers in an aseptic pharmaceutical plant use data to protect the company against batch-level contamination and the specter of a vast recall. Focus on solving small issues, and they will add up to BIG!

Amazon, please show us the path for Greengrass to “go big.”

To learn more about why data at an asset’s physical layer matters, schedule a demo here.


Resource Engineering & Maintenance – Achieving Next-generation MRO through Smart Asset Performance Management

“… rugged high-memory passive RF data chips (and sensors) enable next generation MRO by allowing product data and its entire, granular lifecycle history to easily and inexpensively become part of the product itself.”

Tego Executive Director Bill Stevenson writes for Resource Engineering & Maintenance magazine to describe what asset intelligence can do to bring about a next generation of asset performance management and MRO operations.

From the article:

The term digital thread is sometimes used to describe an integrated view of an asset’s data throughout its lifecycle. The digital thread is intended to deliver “the right information to the right place at the right time.” In a distributed asset intelligence scenario, a given asset’s “as-maintained” data resides on the asset itself, thus providing an information platform for improving asset performance management and MRO. “Smart assets” make detailed specification and configuration data available at the point of use to facilitate maintenance, precisely track compliance of life-limited parts, ensure the authenticity of parts and avoid counterfeits, confirm part performance, and even incorporate maintenance instructions into the part itself. The part tells the employee what needs to be done!

More importantly, data on each part can be updated with each service or inspection activity, and a permanent record of all entries gets maintained locally. Multiple data partitions requiring security credentials allow certain data to be selectively available to users based on their rights and role. A local smartphone reader can connect right back to the enterprise systems of the operator, OEM, or third-party maintenance provider, providing thorough visibility into how a part is used and its performance over time, and fueling better decision making for management.

In addition to storing lifecycle history data on the product itself, RF data chips can be configured to provide power to a sensor, and then record its data whenever the chip is interrogated with an RF signal. Typically, this activity would not be carried out to collect continuous real-time data, but rather to provide periodic trend data that could inform further maintenance decisions.

Read the full article here (Go to Page 16).

To learn about Tego’s platform solutions for product lifecycle management and MRO and how Tego enables smarter asset performance management, contact us here.


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