My search for meaningful Industrial IoT knowledge
January 30, 2017
As product managers, technologists, and business development professionals look for ways to customize solutions, products, and business models for the...
As product managers, technologists, and business development professionals look for ways to customize solutions, products, and business models for the Industrial Internet of Things (IoT), we’re increasingly hungry for quality information. Most of what we read today are great forecasts of billions of connected products and how information will lead us through a “fourth industrial revolution.” Secondarily, we read of how algorithms and models that lead to better predictive techniques, big data, machine learning, and artificial intelligence as a panacea for growth.
While I agree with those statements, there’s a lack of domain-specific information for driving this incremental value with connectivity and data science. Thus, I’m reading every book, white paper, article, product release, podcast, and YouTube video on the subject that span the technology stack looking to glean insights about where the value chain is and the gaps that present opportunity.
To further my understanding, last year I decided to pursue a Master’s of Data Science to truly bridge between business needs and what the data science can provide. It’s been a great journey and I feel that I’m in the center of exponential technologies and changes and able to grip the nodes of what’s possible while being customer obsessed and changing the way value is delivered in an “industrial SaaS” model with domain-rich information to solve issues.
I argue data analytics will not be utilized by a very lean manufacturing staff; it must be integrated into their workflow by human intervention. Hence, we must be customer-obsessed with solving their problem, not just identifying it. In other words, going from a monthly blood pressure check to continuous blood pressure monitoring and telling the patient he’s having a heart attack is not enough. We must schedule and perform the surgery as well.
I feel like we’re close to cracking the code, and my journey for valuable information lead me to a book by Dr. Timothy Chou called Precision: Principles, Practices and Solutions for the Internet of Things. Dr. Chou is a lecturer at Stanford and former President of Oracle on Demand.
Dr. Chou’s book is divided into two parts, the first being principles and practices and second being solutions in action today. Part 1, Principles and Practices, speaks of the technology stack, where it came from in a format of Things, Connect, Collect, Learn, and Do. Part 2 provides 14 examples of the technology in action. If you want a sample check out the Precision Race Car story, which explains the technology in enough depth to provide credibility but keep business readers engaged. The included examples provide breadth that somewhere in the mix one will find relevancy to get your thoughts stirring on how you can hone, calibrate, or develop your product and service offering.
I liked the book so much I wrote a review and recommended it to the Dean of Software Engineering at St. Thomas, where I am pursing a Master’s degree. I felt if the data science students, coders, modelers, and others could see relevance into the business issues, it would create an entrepreneurial view for this group and drive insight. The Dean and I had the opportunity to speak with Dr. Chou about the program and the various gaps that engineering educational programs could bridge to realize IoT promise’s and forecasts previously mentioned.
Dr. Chou’s insight and experience should be leveraged; it’s very meaningful in this time of exponential technologies in human history. If you’re interested, his work is being turned into an online, video-enabled, mobile class.
Dan Yarmoluk is the business and market development lead for ATEK’s IoT and analytical products which include TankScan and AssetScan. Dan has been involved in analytics, embedded design and components of mobile products for over a decade. He has an MBA from Loyola University Chicago and a Master’s of Data Science from the University of St. Thomas.