I love spending time with analysts / data scientists / quants. It takes me back to my days as a grad student when gnuplot was just becoming a thing. It’s still cool, and I’m still amazed at the rich tools that are freely available that enable data owners to quickly get a handle on its meaning.
I have a hypothesis: a brain twice as massive as a human brain would only be marginally more intelligent. And a single brain with 7 billion times the mass of a human brain would be orders of magnitude less capable than all of humanity. This feels analogous to Haldane’s famous 1928 piece “On Being The Right Size”, mapped into the domain of learning.
What’s cloud computing, and what’s the edge? We could describe boundaries based on the points of demarcation between providers and customers, but that quickly gets messy: AT&T serves my phone but it also serves my company; moreover AT&T is more familiar as a communications provider than as a cloud provider. Let’s try again: In an enterprise context Edge means “on prem”, and Cloud means “not on prem”. Tricky again: Silver Spring Networks is one of the largest providers of Smart Metering Infrastructure, with over 25M endpoints under management (like your house). They operate low bandwidth networks city-wide, and give their utility provider customers accurate information about energy consumption. What’s “on prem” in this story? What’s “on prem” to a vendor of self-driving cars, or to Uber, which eschews on-prem IT infrastructure but receives masses of data from drivers and riders to its cloud hosted apps world wide?
Often when I visit enterprise users of industrial automation – for example in manufacturing or retail – I find tremendous excitement on the part of ops teams at the potential for IOT solutions to deliver value. But when I dig deeper into their requirements or ask how they would measure the value of an IOT solution, the room falls silent. Ops teams are too busy meeting delivery schedules or production targets, and they struggle to imagine the possibilities of an environment transformed by machine learning and real-time insights and control. They can’t begin to imagine how new technology could transform their organization’s productivity and make their lives easier.
In my previous post I made the case that Industrial IOT (IIOT) is not an App nor DevOps problem; It’s a data problem. There aren’t enough app and ops folk who know the language of big-data and cloud to get the job done. And there’s no way to deliver these new stacks in orgs with today’s Information Technology (IT) and Operational Technology (OT) skill-sets.
Too many brave pioneers seeking to transform the efficiency of their industrial infrastructure with new Industrial IOT (IIOT) applications face such daunting odds that their projects are doomed to failure before they even get off the ground. In this post I don’t want to focus on the reason why most projects fail. Instead, I want to give you a glimpse of a powerful new approach that has the potential to transform success rates and catalyze the adoption of new IIOT solutions.
There are lots of IOT startups out there. Too many. Silicon Valley hasn’t changed – if you throw enough mud at the wall, some has to stick, right? For example, there are over 400 IoT software platform vendors competing for developers to adopt their frameworks to develop cool new IOT apps. Good luck with that, folks. Just ask Pivotal (the Cloud Foundry folk) how hard it has been to build a community of developers focused on its platform. Salesforce faced a similar battle when it acquired Heroku. And then there are the big guys, who effectively suck the remaining oxygen from the room: AWS, Microsoft, Google, and perhaps Apple. Fighting for developers is a losing strategy for a startup. It’s a one-way street that ends in the oft-repeated fight to the bottom: “If we open source it, they will come”. Sadly, once the tech has been open sourced, if it is any good it will simply be offered as a service by one of the big clouds, or adopted by competitors with more funding.
The Industrial IoT landscape is littered with the corpses of failed projects that never made it past the PoC phase. What’s wrong? I’ve surveyed the press and talked with Swim customers and partners, and I’m convinced there are two main reasons for failure, and they both have the same root – the lack of human skills needed to deliver success. Swim is committed to transforming business outcomes in the IIoT domain using machine learning at the edge – right where the data originates. Machine learning at the edge is crucial to addressing the skillset challenge.
Announcing the Swim ESP™: Machine Learning at the IOT Edge
Enterprises and public sector organizations are drowning in a flood of sensor data from equipment, assets, suppliers, consumers, city data sources, and energy providers that contain hidden insights that have the potential to optimize production, transform efficiency, streamline flows of goods, vehicles and services, protect the environment and ensure safety. But finding them remains a huge challenge.