AI, IoT, M2M, Big Data – The Alphabet Soup of Technology Jargon You Need to Understand (Part 2 of 2)
This week’s post is a continuation of an introduction to AI, IoT and Big Data with the help of the 1984 movie The Terminator. Re-read that post here.
Let’s rearrange the AI, IoT, and big data alphabet soup of technology jargon to come up with a simple question that helps you cut through the hype and delivers some focus for your technology strategy. I am going to drop a few words from artificial intelligence, Internet of things, and big data to make my point. Here we go:
“How can you use the Internet to collect data about customer equipment so that you and your customer can make intelligent decisions about services that will minimize risk, expense, and business disruptions caused by suboptimal equipment performance or equipment failure?”
I really don’t care if the data is “big” or “small.” I don’t care if the information that comes over the Internet is generated by a “thing” or by a person holding a smartphone taking photos of an impaired piece of equipment (although “things” are often cheaper than people as collection devices). I also don’t care if the “intelligence” is artificial or natural so long as it is smart and not dumb. The overall direction of technology, of course, is toward bigger data, more things connected to the Internet, and more intelligence that is artificial versus natural as computing gets cheaper and people get more expensive.
Now that we have generated a simple test to cut through the hype and focus our innovation lens on practical and actionable solutions, what are some examples that illustrate the potential value of this strategy? How are you currently and in the future going to collect data over the Internet to make more intelligent decisions regarding equipment services that should be delivered to optimize performance? You don’t have to wait for the day that the terminator is a reality.
Real World Example: AI at Work
The favorite workflow of ServiceTrade customers is the recording of equipment deficiencies by technicians using the mobile app and the subsequent online review of a quote by the customer to approve a related repair. Let’s see if this workflow meets the test of our strategy.
- Are we collecting information about equipment via the Internet? Yes.
In this case it is photo, video, audio, and text captured by the technician that illustrates the problem to the sales person in the office and ultimately to the customer.
- Are we using intelligence? Yes.
The technician knows this situation can lead to a failure, otherwise why record it? The sales person also recognizes the problem because of the detailed data set, and she applies the correct quote template for repair based upon prior experiences – how much time, which tools, which parts, etc.
- Finally, the customer can trust the information to make a good decision because he sees and hears the problem. Just like the “look through” scenes in The Terminator, we know that intelligence is being applied because we can see it happening. The customer sees what the technician sees.
Real World Example: How IoT Reduces Chaos
Sensors are getting super cheap and the power requirements are getting so small that battery life is often measured in years. Consider fire sprinkler customers that have risk of pipes bursting due to freezing in certain areas of their facility that are not temperature controlled. Setting up a temperature sensor that generates an alert below freezing temperature could easily trigger a response to turn on some space heaters. If the heaters are connected to some sort of “smart” electrical circuit, perhaps they deploy on the signal without any other intervention. This seems like a small thing, but during cold snaps in normally moderate climates, it is amazing how many sprinkler pipes freeze. OK, does it pass the test?
- Did we collect data on the Internet? Yes, through an ad hoc temperature sensor.
- Are we using intelligence? Yes, intelligent people know that water freezes at thirty two degrees, and we know that plugging in a space heater will keep the temperature above that threshold.
- Did our decision and action avoid disruption and maintain optimal facility performance? Yes. Great, we are off to a terrific start with our AI, IoT, and big data strategy!
Real World Example: Big Data Brings About Better Decisions
Big data is simply a buzzword for datasets that are generally so large that a simple tool like Excel with a human interface might struggle to parse any intelligence from the data. All of the data in ServiceTrade is automatically ported over to Amazon’s Redshift/QuickSight big data analytics platform. A simple analysis will show customers spending habits related to emergency service versus planned services (preventative maintenance and planned retrofits and repairs). During an annual review with a challenging customer that insists on minimal preventative maintenance, you might be able to demonstrate that a similar customer that opts for maximum preventative maintenance is spending significantly less overall during the course of the past 3 years. No one could parse that amount of data in Excel, but QuickSight handles it easily with just a few clicks. OK, does it pass the technology strategy test?
- Did we collect data on the Internet? Yes, all data in ServiceTrade is collected over the Internet because ServiceTrade is a SaaS application.
- Did we make an intelligent decision to lower expense, lower risk, and optimize performance? Hopefully the answer is yes because ideally the customer buys into your premium program based upon the analysis that indicates lower total costs.
Let’s quickly contrast these straightforward examples of effective and simple technology deployment for achieving a mission with a “technology solution trying to find a problem.” Google, Snapchat, Intel, and a host of other technology heavyweights have spent years and hundreds of millions of dollars on “smart glasses” that combine cameras, heads up displays, natural language recognition, cellular networking, etc. Some vendors in the contracting space latched onto this science experiment and began selling it as the productivity solution for all of your problems. It enabled customer collaboration, technician training, remote diagnosis, and a host of other benefits (according to the vendors). It didn’t work.
Taken in pieces, elements of the technology make sense. A small, bluetooth camera clipped to the bill of a ball cap with a similar bluetooth earpiece all tethered to the mobile phone will enable the technician to fire up a FaceTime call with a colleague. The two can then collaborate via shared images and a real time conversation to diagnose a problem. The challenge with jamming everything into a new form factor like glasses is that it is a laboratory exercise instead of a solution to a problem. It is Frankenstein as compared to the terminator. Frankenstein was great science, but yielded only chaos and misery when deployed beyond the lab. The terminator, by contrast, was built for accomplishing the mission in the field.
Your objective is to assemble the terminator and avoid Frankenstein. The examples above clearly indicate that AI, IoT, and big data are already a part of the arsenal of technology you are using for the benefit of your customer. It really is not rocket science, and you really can embrace new innovations if you are willing to explore and set aside the intimidating jargon in favor of a elegant strategy. Your strategy should simply be a trend of collecting more data via the Internet so that you can intelligently make service decisions that optimize the performance of your customers’ important equipment. Any innovation that meets this simple test is putting you on a good path for adding more value. Stay focused on the mission, and the right solutions will present themselves as obvious candidates for your premium service program.
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