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Business developers and analysts don't care about IT - they want "Invisible IT"


When I was a teenager, I owned a Ford Escort (a medium-size car that I don’t think was ever sold in the US. It was extremely popular in its day). So did my girlfriend and so did my sister. Once every couple of weeks, I would have to spend at least a few hours on a Sunday tinkering with one or more of these Escorts. Perhaps it was adjusting the brakes. Or cleaning out the carburetor. Or adjusting the timing.

Now, thirty-odd years on, my weekends are my own because my car just goes. (This is not quite true. I’ve just had a service done - the first for three years.)  

The workings of my car has become largely invisible to me. And that’s the way that I, and 99.9999% of car owners want it to be. (One of the promises of electric cars is that they are even more invisible because they require no oil or coolant changes.)

Business IT wants invisible IT platforms

As I’ve commented a number of times in the past, applications and data analytics is increasingly being created by business IT. By people whose objectives don’t include “tinker with the IT platform required to create apps and analytics”. In fact, their objectives don’t even include anything about creating apps and analytics - these are just things that they do in order to meet their business objectives.

In other words, there is increasing pressure to do for IT platforms what has been done for cars. There is pressure for “invisible IT”.  This invisibility is not just required at the server or storage level.  What business IT wants is “invisible” platforms that allow them to create Blockchain-based applications, do machine learning, take action with an IoT-based system or create mobile apps. 

I think that there are three ways to achieve invisible IT - the invisible platforms that business IT wants.

invisible IT.png

1. Invisibility on premises

How can on premises IT be made more invisible?

Invisibleness step 1 - automation

Our first step towards invisibleness was thru automation. Automation allows us to abstract away the detail of IT-related tasks. For example, rather than worrying about the detail of how to add more CPUs, automation allows us to just ask for more CPU. HPE’s Synergy and Simplivity platforms, as well as OneView, contain a lot of automation. As do products like Ansible, Puppet and Chef. 

Invisibleness step 2 - machine learning

The next evolution of invisibleness is the use of artificial intelligence. Machine learning, specifically. Please see my previous post on the types of artificial intelligence which explains what machine learning is. 

Machine learning is the key technology to provide invisible IT platforms

It’s early days for the use of machine learning in IT management, but I believe that this technology is key to providing the invisibleness that business IT demands.

What does machine learning allow us to do?

Machine learning allows us to autonomously work out what’s going on

Machine learning can autonomously (by itself) work out what it going on. The machine learning system will learn what different problems’ digital fingerprints look like. These digital fingerprints will typically span a number of different systems - this is why IT management has always been non-trivial. 

So, rather than employing a first or second-level support person to rummage around in logs and event streams, the machine learning system can do this for us. 

HPE Nimble InfoSight product does just this. It uses machine learning to look for problems’ digital footprints. It then presents the problem, with its cause and its consequences, to third level support. 

The user and entity (thing) behaviour analysis product from HPE’s Aruba division works similarly. It looks for the digital footprint of security attacks. 

Machine learning allows us to predict that a problem is going to occur

Once a machine learning system can be taught to look out for certain digital fingerprints, it can the be asked not just say, “you have problem X”. It can start to predict. It can start to notice patterns that lead to problems and alert (or take autonomous action) to stop the occurrence of a problem. 

Going back to the car analogy, my car has an oil quality sensor and it predicts when I will need to change the oil - it predicts failure of the oil’s viscosity or additives and takes proactive action (screaming at me with a red warning light) to stop me running my car with bad oil. 

Machine learning allows us to predict capacity needs

I special, but very important, form of prediction concerns the capacity planning. We can use machine learning to predict ahead of time when we will need more capacity. Nimble’s InfoSight product does this. It can predict when you will need more disk storage.

Predictive capacity provisioning is a great component for our “invisible IT” quest. It means that we never get the situation where we have poor performance and we are given the excuse, “the XYZ part is on order”. 

Matching finance to capacity needs - pay-as-you-grow

Our discussions to date have been around technology. But another aspect of invisibility is finance (well, probably not invisibility - the only invisible finance option is that everything is free. Perhaps “frictionless” is a better way of putting it).  We need to be able to pay as we grow, rather than having to pay for everything up front.

2. Invisibility on premises, but “as-a-service”

The Blockchain, devops, machine learning and IoT platforms that the digitally-transforming warriors in business IT require are leading, and often bleeding-, edge They are typically composed of new open source or offerings from startup software companies. 

A typical IT department may not have the spare bandwidth to keep up with all these goings on, and even if they do, they probably don’t have the time to figure out how all this new stuff will scale-up in production.

For this reason, HPE’s service organization, Pointnext, will be offering “platforms as on prem services”. The idea is that you get an on premises platform that business IT will want, but that that platform is managed, remotely, by Pointnext. While an IT department may only have experience of installing, say, one Blockchain server, Pointnext will have experience of installing, running and scaling up many hundreds or thousands of Blockchain servers. So, the experience that Pointnext gets across the world in each of the digital transformation platform areas (Blockchain, IoT, machine learning, devops) can be used for each customer who chooses to use on premises as-a-service offering.

It’s early days for this concept, but to me, it makes sense. It allows IT to offer the platforms that business IT needs, but with the control that on premises brings. 

3. Invisibility by using the cloud

Cloud vendors are moving from providing server and storage services to providing machine learning and IoT services so that business IT developers can focus on the apps and analytics they need to produce.


We need to make the platforms that business IT needs “invisible” so that they can focus entirely on the job of creating apps and analytics.

The heart of the required invisibleness will, I believe, be delivered by the use of machine learning. Machine learning will autonomously diagnose problems for us. It will predict that problems are going to occur so that we can proactively fix them. And it will predict extra capacity needs ahead of time so that we never need to suffer performance problems due to upgrades being on order.

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Mike Shaw
Director Strategic Marketing
Hewlett Packard Enterprise

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Mike Shaw
Director Strategic Marketing

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About the Author


Mike has been with HPE for 30 years. Half of that time was in research and development, mainly as an architect. The other 15 years has been spent in product management, product marketing, and now, strategic marketing. .

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