Technological Convergence Enabling Real-Time Facility Analytics

In “The Sociology of Science”, Robert K. Merton concludes that, “discoveries become virtually inevitable when prerequisite kinds of knowledge and tools accumulate in man’s cultural store and when the attention of an appreciable number of investigators becomes focused on a problem, by emerging social needs, by developments internal to the science, or by both.”  Merton shared this insight in 1973, summarized now as the “Multiple Discoveries” theory, with no knowledge of how technology would evolve.  However, this idea, that the convergence in demand for a technology and our own ability to implement it explains many of our fastest growing industries today.  Chief among those is Energy and Facilities Analytics.

A long time, ago, yesterday…

At first blush, “multiple discoveries” seems simple enough; more than one person is aware of a need, so more than one person attempts to build a solution.  However, what is remarkable is the multiplicative effects that one need and its solution has on our ability to identify and satisfy other needs.  Some say the first control systems were found in Egypt over 2000 years ago in the form of water clocks (  Throughout the industrial revolution, the capabilities of control systems were further improved by the convergence of enabling developments in science and mathematics. The demand and advantages of such systems were as obvious then as it is now, improved control, more efficient processes, better profits, increased reliability, and user acceptance.  However, after the advent of pneumatic controls, technology remained relatively stagnate as demand, largely driven by the success of such systems, increased.  It wasn’t until the advent of the transistor, integrated circuits, and ultimately, direct digital controls (DDC) that we saw another huge leap in capabilities.  Of note, is that the transistor and its subsequent implementations were not designed for the explicit purpose of building automation or energy management.  It was the convergence of demand and the availability of the necessary technologies that brought about the DDC revolution.  Fast forward to the early 1990’s and we see the demand for remote management and 24/7 awareness converge with the enabling technologies of the internet.  Today, remote connectivity, or at least the IP-based technologies that enable it, are considered standard components in a modern building automation system.  What’s more, is despite its many varied forms (tenant leases, federal energy mandates, energy ratings and standards organizations, etc…), the original demand to implement more efficient processes, drive profits, increase reliability, and user acceptance hasn’t changed in the last 2000 years.

Today, and hopefully tomorrow…

So, we all know where this is going…I’m going to say we are at another amazing convergence of Big Data, IoT, Open Protocol BAS, the Internet and Mobile Applications, Machine Learning, driving applications that we have yet to even imagine!  (Oh and by the way, Datakwip is the one who can do it all!  Our marketing team refers to that as a “call to action” and they get a little sour if I fail to include it.  Box checked.  Scowl averted.)  Here’s the thing, all that is true, but there are a couple of demands and enabling factors that are both critical and overlooked.

But first, a note on cost.  Developing new technologies is expensive.  Making sure they work right is even more expensive.  Making sure they work right even when everything else is going wrong, is REALLY expensive.  The next great convergence is not just a convergence of possibilities, but a convergence of feasibilities.  The technologies we see today not only enable us to do things we couldn’t do before, but by lowering the financial barrier to entry we are enabling more people to participate. Borrowing from Merton, “an [even greater] number of investigators [will] become focused on [this] problem”.

Serverless architectures

Services like AWS Lambda, AWS Kinesis, AWS Redshift, AWS S3, Azure Streams, Azure Blob, and Google Dataflow all have two things in common.  They are data processing and storage tools, and they allow people to write applications as opposed to starting and maintaining server farms.  This is a really big deal.  OK, it’s 2017, so you’re not going to go out and buy a server and put it on a rack in the back of your office.  You’re going to go with an offsite infrastructure, maybe it’s a private data center, maybe it’s a public cloud like AWS or Azure, but more than likely you’re still just turning a Windows or Linux machine on over the internet and logging into it just like it was sitting right behind you.  The problem here is you still, for the most part, must manage it.  This means updating the operating system, configuring and maintaining security patches, upgrading hardware, not to mention monitoring the actual application you brought online to serve your customers.  However, in a serverless architecture, you write your code once, and then (literally) turn up the volume as needed.  This means reduced costs in maintaining the deployment and a lower time to market.  Leaving more time for solution providers to talk to domain experts about the products they’re building.  Even better, on many of these services, developers are charged based on what they use.  Imagine you want to simply check a data stream for temperatures above 75 degrees every five minutes.  You only pay pieces of pennies every 5 minutes.  No multi-thousand-dollar upfront investment for onsite hardware or paying for unused compute time in traditional cloud deployments.  If that wasn’t enough, these services have built in mechanisms to increase the security and resiliency of your applications, no extra coding required.  Imagine building the latest generation of energy analytics applications without having to seek major corporate funding, without having to hire 24/7 devops staff, and being able to leave your office more than once a month and interact with your end users.

(Good) Open Source Software

Software licenses suck almost as bad as multi-thousand dollar servers.  There’s nothing worse than visiting a website, getting excited about how a new piece of software is going to help you or your customers, only to find out that you are looking at a six-figure investment just to kick the tires.  Enter open-source software.  The open source movement has been around for a long time, but over the last 5 to 10 years, we have a seen a deluge of extremely high-quality big data and IoT-oriented technologies become available.  What’s even better is that many of the technologies, while open source, are also backed by full-service software companies.  So, after you play around with these solutions, if you need to talk to the experts, you can sign up for a support agreement at a lower rate than having to “buy” a traditional software license from a major software company.

The IoT Movement

While I referred to the IoT movement somewhat sarcastically above, it is helping with a multiple discoveries convergence, just not in the way some people think.  IoT represents the idea that devices will provide real-time data updates over the internet to various services that will then derive additional value from that data.  This is a critical concept; however, it’s not that much different than an internet based DDC system.  In fact, I may have used the phrase “I was doing IoT before it was cool” at least one time too many.  Personal biases aside, the IoT movement and the corresponding media frenzy, industry hype, customer hoopla serves a critical purpose.  It raises awareness and generates need.  As our friend Mr. Merton stated, we don’t just need technology, we need problems to solve with it.  The IoT revolution, while in some ways a marketing revolution more so than a technology revolution, makes people aware of our technological capabilities and gives them a context in which they can share the problems they believe technology may solve.

Wondering what technologies will converge and help your organization operate more efficiently?  Talk to Datakwip about our real-time machine learning powered energy and facility analytics today (scowl averted, check).