The cloud changes everything – if you’ll let it…

In my experience, technologies are rarely adopted by corporations as rapidly as expected – or maybe it’s just as rapidly as vendors would like them to be…

Often, the challenge of organizational culture is overlooked.  Few people enthusiastically embrace change, and we’ve surely all experienced new releases or upgrades that really were detrimental.  Change will surely be more difficult for some companies than others when it comes to the Industrial Internet of Things and the adoption of the cloud that will come with that.

But, change is inevitable, like it or not.  At this point, I’m thinking there are really only two types of companies when it comes to cloud adoption:

  1. Companies that have officially blessed putting some data and applications in the cloud, and created policies around that.
  2. Companies that have policies explicitly forbidding use of the cloud – but whose employees are secretly using the cloud anyway!

In the second case, why would those employees commit what, in many cases, is technically a dismissible offense?  It’s usually because some cloud service makes their job much easier to do, whether it’s mere cloud storage or a more sophisticated Software-as-a-Service application.  It’s that simple.

The more I learn and think about the cloud, the more convinced I am that it’s a game changer:

  • A year ago I wrote about how solutions like Amazon’s Redshift had the potential to completely change how business analysts, data warehouse engineers, and even progressive CIOs conceive, design, and execute business intelligence and analytics projects (The Disposable Data Warehouse:  How Will You Use Yours?)
  • At SAP SAPPHIRE NOW in May this year I learned how the cloud helped T-Mobile to complete a proof-of-concept in two weeks, instead of waiting 4 months just to procure the hardware to run the same proof-of-concept on-premise.  In this example, the cloud fosters agility and can help to cut the time needed to bring new products and services to market.
  • In one of my current research projects I’m taking a deeper look at the red-hot world of machine learning.  (So red-hot there are more than 700 startups apparently…).  In this instance, I’m realizing how the cloud can completely change the way enterprises choose software solutions.  Many of the machine learning startups are cloud-based.  That is, users develop, test and deploy their machine learning applications in the cloud.  These solutions typically provide a robust framework to help users get started with their applications quickly.  In this way, the cloud can make the evaluation cycle so much faster for potential buyers:  Pick a cloud-based solution, and try it out for a couple of days.  If you like it, move towards a production application (or a more fully-fledged prototype).  If you don’t like it, just move on – pick another cloud-based machine learning tool and start over…

(Originally published on industrial-iot.com, a blog by ARC Advisory Group analysts)

Oh puleeze, let’s make the Industrial IoT better than this…

The consumer internet of things largely lives in something of a parallel universe to industrial IoT.  Sure, there is going to be overlap in the supporting infrastructure – networks, IoT platforms etc. but the applications are shaping up to be very different.  A big chunk of consumer IoT is focused on wearables – gadgets that we wear that enhance our life in some way, such as fitness trackers or health monitors.  Nevertheless, I was amused to read this article over the weekend.  “The wearable you’ll actually wear, because it doesn’t need charging“.  Wow!  Imagine buying a device that adds so little value that charging it every day actually becomes a chore.  Instead of enhancing your health, it becomes a pain in the <insert body part of your choice>.

I’ll tell you right now, if our first efforts at Industrial IoT are so inept, we’ll kill the opportunity to drive a new wave of efficiency, introduce new business models and revenue streams, stone dead.  For at least 5 years, maybe 10.  Fortunately, it looks like Industrial IoT is fairing better than consumer IoT, with early examples of success from KAESER KOMPRESSOREN SEBP, the trains in Olso, and other examples from the ARC forum in February.  Ralph Rio also writes more about the predictive maintenance opportunity here.

IIoT projects share many similarities with any other IT project.  It’s early days, but so far I have 6 simple guidelines for anyone contemplating an Industrial IoT project:

  1. Start small – your first project is really a proof-of-concept.
  2. Focus on a real, living, breathing, business problem.
  3. Use a multi-disciplinary team – you won’t get very far without one…
  4. Think about what data you have, right now, that you can leverage.  Or, what data can you get, easily…?
  5. Which potential projects promise quick and easy value?  Pick one of those.
  6. Make sure you measure ROI so that you have fuel for future projects.

Motherhood and apple pie in many ways, but important to remember anyway.  What additional guidelines do you have?  Add a comment and share please.

(Originally published on industrial-iot.com, a blog by ARC Advisory Group analysts)