Wednesday, January 23, 2019

A discussion with IT analyst Martin Hingley on the culmination of 30 years of IT management maturity

The next BriefingsDirect hybrid IT strategies interview explores how new maturity in the management and composition of multiple facets of IT -- from cloud to bare-metal, from serverless to legacy systems -- amount to a culmination of 30 years of IT evolution.

We’ll hear now from an IT industry analyst about why – for perhaps the first time -- we’re able to gain an uber-view over all of IT operations. And we’ll explore how increased automation over complexity such as hybrid and multicloud deployments sets the stage for artificial intelligence (AI) in IT operations, or AIOps.

It may mean finally mastering IT heterogeneity and giving businesses the means to truly manage how they govern and sustain all of their digital business assets.

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy. 

Here to help us define the new state of total IT management is Martin Hingley, President and Market Analyst at ITCandor Limited, based in Oxford, UK. The discussion is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Looking back at IT operations, it seems that we have added a lot of disparate and hard-to-manage systems – separately and in combination -- over the past 30 years. Now, with infrastructure delivered as services and via hybrid deployment models, we might need to actually conquer the IT heterogeneity complexity beast – or at least master it, if not completely slay it.

Do you agree that we’re entering a new era in the evolution of IT operations and approaching the need to solve management comprehensively, over all of IT?

Hingley: I have been an IT industry analyst for 35 years, and it’s always been the same. Each generation of systems comes in and takes over from the last, which has always left operators with the problem of trying to manage the new with the old.

Hingley
A big shift was the client/server model in the late 1980s and early 1990s, with the influx of PC servers and the wonderful joy of having all these new systems. The problem was that you couldn’t manage them under the same regime. And we have seen a continuous development of that problem over time.

It’s also a different problem depending on the size of organization. Small- to medium-sized (SMB) companies can at least get by with bundled systems that work fine and use Microsoft operating systems. But the larger organizations generate a huge mixture of resources.

Cloud hasn’t helped. Cloud is very different from your internal IT stuff -- the way you program it, the way you develop applications. It has a wonderful cost proposition; at least initially. It has a scalability proposition. But now, of course, these companies have to deal with all of this [heterogeneity].

Now, it would be wonderful if we get to a place where we can look at all of these resources. A starting point is to think about things as a service catalog, at the center of your corporate apps. And people are beginning that as a theory, even if it doesn’t sit in everybody’s brain.

So, you start to be able to compose all of this stuff. I like what Hewlett Packard Enterprise (HPE) is doing [with composable infrastructure]. … We are now getting to the point where you can do it, if you are clever. Some people will, but it’s a difficult, complex subject.

Gardner: The idea of everything-as-a-service gives you the opportunity to bring in new tools. Because organizations are trying to transform themselves digitally -- and the cloud has forced them to think about operations and development in tandem -- they must identify the most efficient mix of cloud and on-premises deployments.

They also have to adjust to a lack of skills by automating and trying to boil out the complexity. So, as you say, it’s difficult.

But if 25 percent of companies master this, doesn’t that put them in a position of being dominant? Don’t they gain an advantage over the people who don’t?

Hingley: Yes, but my warning from history is this. With mainframes, we thought we had it all sorted out. We didn’t. We soon had client/server, and then mini-computers with those UNIX systems, all with their own virtualizations and all that wonderful stuff. You could isolate the data in one partition from application data from a different application. We had all of that, and then along comes the x86 server.
How to Remove Complexity
From Multi-cloud
And Hybrid IT

It’s an architectural issue rather than a technology issue. Now we have cloud, which is very different from the on-premises stuff. My warning is let’s not try and lock things down with technology. Let’s think about it as architecture. If we can do that, maybe we can accommodate neuromorphic and photonic and quantum computing within this regime in the future. Remember, the people who really thought they had it worked out in previous generations found out that they really hadn’t. Things moved on.

Gardner: And these technology and architectural transitions have occurred more frequently and accelerated in impact, right?

Beyond the cloud, IT is life

Hingley: I have been thinking about this quite a lot. It’s a weird thing to say, but I don’t think “cloud” is a good name anymore. I mean, if you are a software company, you’d be an idiot if you didn’t make your products available as a service.

Every company in the world uses the cloud at some level. Basically there is no longer choice about whether we use a cloud. All those companies that thought they didn’t, when people actually looked, found they were using the cloud a lot in different departments across the organization. So it’s a challenge, yet things constantly change.

If you look 20 years in the future, every single physical device we use will have some level of compute built into it. I don’t think people like you and I are going to be paid lots of money for talking about IT as if it were a separate issue.

It is the world economy, it just is; so, it becomes about how well you manage everything together.

If you look 20 years in the future, every single physical device we use will have some level of compute built into it.  ... It becomes the world economy. It becomes about how well you manage everything together.
As this evolves, there will be genuinely new things … to manage this. It is possible to manage your resources in a coherent way, and to sit over the top of the heterogeneous resources and to manage them.

Gardner: A tandem trend to composability is that more-and-more data becomes available. At the edge, smart homes, smart cities, and also smarter data centers. So, we’re talking about data from every device in the data center through the network to the end devices, and back again. We can even determine how the users consume the services better and better.

We have a plethora of IT ops data that we’re only starting to mine for improving how IT manages itself. And as we gain a better trail of all of that data, we can apply machine learning (ML) capabilities, to see the trends, optimize, and become more intelligent about automation. Perhaps we let the machines run the machines. At least that’s the vision.

Do you think that this data capability has pushed us to a new point of manageability? 

Data’s exploding, now what? 

Hingley: A jetliner flying across the Atlantic creates 5TB of data; each one. And how many fly across the Atlantic every day? Basically you need techniques to pick out the valuable bits of data, and you can’t do it with people. You have to use AI and ML.

The other side is, of course, that data can be dangerous. We see with the European Union (EU) passing the General Data Protection Regulation (GDPR), saying it’s a citizens’ right within the EU to have privacy protected and data associated with them protected. So, we have all sorts of interesting things going on.

The data is exploding. People aren’t filtering it properly. And then we have potential things like autonomous cars, which are going to create massive amounts of data. Think about the security implications, somebody hacking into your system while you are doing 70 miles an hour on a motorway.

I always use the parable of the seeds. Remember that some seeds fall on fallow ground, some fall in the middle of the field. For me, data is like that. You need to work out which bits of it you need to use, you need to filter it in order to get some reasonable stuff out of it, and then you need to make sure that whatever you are doing is legal. I mean, it’s got to be fun.
How to Remove Complexity
From Multi-cloud
And Hybrid IT
Gardner: If businesses are tasked with this massive and growing data management problem, it seems to me they ought to get their IT house in order. That means across a vast heterogeneity of systems, deployments, and data types. That should happen in order to master the data equation for your lines of business applications and services.

How important is it then for AIOps -- applying AI principles to the operations of your data centers – to emerge sooner rather than later?

You can handle the truth 

Hingley: You have to do it. If you look at GDPR or Sarbanes-Oxley before that, the challenge is that you need a single version of the truth. Lots of IT organizations don’t have a single version of the truth.

If they are subpoenaed to supply every email that it has the word “Monte Carlo” in it, they couldn’t do it. There are probably 25 copies of all the emails. There’s no way of organizing it. So data governance is hugely important, it’s not nice to have, it’s essential to have. Under new regulations coming, and it’s not just EU, GDPR is being adopted in lots of countries.


It’s essential to get your own house in order. And there’s so much data in your organization that you are going to have to use AI and ML to be able to manage it. And it has to go into IT Ops. I don’t think it’s a choice, I don’t think many people are there yet. I think it’s nonetheless a must do.

Gardner: We’ve heard recently from HPE about the concept of a Composable Cloud, and that includes elevating software-defined networking (SDN) to a manageability benefit. This helps create a common approach to the deployment of cloud, multi-cloud, and hybrid-cloud.

It’s essential that you get your house in order. And there's so much data in your organization that you are going to have to use AI and ML to be able to manage it. And it has to go into IT Ops.
Is this the right direction to go? Should companies be thinking about a common denominator to help sort through the complexity and build a single, comprehensive approach to management of this vast heterogeneity?

Hingley: I like what HPE is doing, in particular the mixing of the different resources. You also have the HPE GreenLake model underneath, so you can pay for only what you use. By the way, I have been an analyst for 35 years, if every time the industry started talking about the need to move from CAPEX to OPEX had actually shifted, we would have been at 200 percent OPEX by now.

In the bad times, we move toward OPEX. In the good times, we secretly creep back toward CAPEX because it has financial advantages. You have to be able to mix all of these together, as HPE is doing.

Moreover, in terms of the architecture, the network fabric approach, the software-defined approach, the API connections, these are essential to move forward. You have to get beyond point products. I hope that HPE -- and maybe couple of other vendors -- will propose something that’s very useful and that helps people sort this new world out.
How to Remove Complexity
From Multi-cloud
And Hybrid IT
Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy. Sponsor: Hewlett Packard Enterprise.

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