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The Link Between Asset Management and Skills Gap Mastery: A Q&A with Tim Butler

“… there really is a sociological aspect to all of this, where some companies will spend tens of millions of dollars on a new system that ultimately fails because the workers on the edge won’t use it.”

In a ranging conversation with Ian Wright at Engineering.com, Tego’s CEO Tim Butler touches a litany of vital considerations when it comes to building an APM strategy that drives business metrics that matter. From quality assurance, to productivity, to workforce engagement, it is not just about connecting assets together. It is about making them smart.

In other words, enabling things to be smart means putting data about a given asset on the asset itself. This can be its birth records, maintenance history, operating instructions, or any other information about how, when and why employees have interacted with it. The asset becomes a repository for information, not just an endpoint that’s flowing information to the cloud. Instead, information can be read from and written to the asset throughout its life, without requiring any cloud connectivity.

That is what makes an asset smart.

Highlights from the conversation include:

The real change with the introduction of PCs was an increase in flexibility. Does that apply in this case as well?

Yes. Think about what we learned in the last 30 years: suddenly, we’re walking around with tablets and phones that have levels of connectivity that were unheard of a few decades ago. Now, I can walk up to an asset and interact with it using my phone or tablet. I can query it and get information from it or write information to it; I’m using the functional and analytic capabilities of those tools to actually do analytics right there at the edge.

Cybersecurity is a major concern associated with the IoT. Does the kind of pervasive asset intelligence you’re talking about introduce new security risks?

From our perspective, it actually provides a new layer of security because all these assets are off the grid. We can enable up to 16 different types of access, where some employees have read/write access, others can only read some types of information and still others can only access  a different set of information. So, you can partition the information in ways you couldn’t before. The technologies we’re familiar with—in terms of password protection, encryption and authentication—can all be applied here, now that we have the storage to be able to do it.

Do you believe this technology can help address the skills gap in manufacturing? If so, how?

If you’re wondering how manufacturers are going to get the next generation of workers—who typically don’t use pen and paper—to actually use digital information, one of the critical elements is having that information at the source, or the edge.

It’s all about automating the process so that people can do it more easily and efficiently—which brings training costs down—making the information as accessible as possible, and it’s about making the information transferable and usable across the organization.

If you’re giving your workers something that basically turns them into automatons, you’re going to have trouble. The flip side is that you still need to replace them in the next ten years. But, if you enable real data on the edge, you’re starting that digitalization, but the knowledge and experience of your technicians feeds that. Now the older workers are saying, ‘Oh, this isn’t going to replace me. I’m actually going to be able to show people how smart I am, because now it’s much easier for me to broadcast that across the company.’

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 about Tego’s role in local data strategy, contact us here.

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.

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