Thursday, March 19, 2015

Axeda's machine cloud produces on-demand IoT analysis services

This BriefingsDirect big data innovation discussion examines how Axeda, based in Foxboro, Mass., has created a machine-to-machine (M2M) capability for analysis -- in other words, an Axeda Machine Cloud for the Internet of Things (IoT).

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

To learn more about how Axeda produces streams of massive data to multiple consumer dashboards that analyze business issues in near-real-time, we're joined by Kevin Holbrook, Senior Director of Advance Development at Axeda. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: We have the whole Internet of Things (IoT) phenomenon. People are accepting more and more devices, end points, sensors, even things within the human body, delivering data out to applications and data pools. What do you do in terms of helping organizations start to come to grip with this M2M and IoT data demand?
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Holbrook: It starts with the connectivity space. Our focus has largely been in OEMs, equipment manufacturers. These are people who have the "M" in the M2M or the "T" in the Internet of Things. They are manufacturing things.

The initial drivers to have a handle on those things are basic questions, such as, "Is this device on?" There are multi-million dollar machines that are currently deployed in the world where that question can’t be answered without a phone call.

Initial driver

That was the initial driver, the seed, if you will. We entered into that space from the remote-service angle. We deployed small-agent software to the edge to get the first measurements from those systems and get them pushed up to the cloud, so that users can interact with it.

Holbrook
That grew into remote accesstelnet sessions or remote desktop being able to physically get down there, debug, tweak, and look at the devices that are operating. From there, we grew into software distribution, or content distribution. That could be anything from firmware updates to physically distributing configuration and calibration files for the instrument. We're recently seeing an uptake in content distribution for things like digital signage or in-situ ads being displayed on consumer goods.

From there, we started aggregating data. We have about 1.5 million assets connected to our cloud now globally, and there is all kinds of data coming in. Some of it's very, very basic from a resource standpoint, looking at CPU consumption, disks space, available memory, things of that nature.

It goes all the way through to usage and diagnostics, so that you can get a very granular impression how this machine is operating. As you begin to aggregate this data, all sorts of challenges come out of it. HP has proven to be a great partner for starting to extract value.

We can certainly get to the data, we can connect the device, and we can aggregate that data to our partners or to the customer directly. Getting value from that data is a completely different proposition. Data for data’s sake is not high value.
From our perspective, Vertica represents an endpoint. We've carried the data, cared for the data, and made sure that the device was online, generating the right information and getting it into Vertica.

Gardner:  What is it that you're using Vertica for to do that? Are we creating applications, are we giving analysis as a service? How is this going to market for you?

Holbrook: From our perspective, Vertica represents an endpoint. We've carried the data, cared for the data, and made sure that the device was online, generating the right information and getting it into Vertica.

When we approach customers, were approaching it from a joint-sale perspective. We're the connectivity layer, the instrumentation, the business automation layer there, and we're getting it into Vertica ,so that can be the seed for applications for business intelligence (BI) and for analytics.

So, we are the lowest component in the stack when we walk into one of these engagements with Vertica. Then, it's up to them, on a customer-by-customer basis, to determine what applications to bring to the table. A lot of that is defined by the group within the organization that actually manages connectivity.

We find that there's a big difference between a service organization, which is focused primarily on keeping things up and running, versus a business unit that’s driving utilization metrics, trying to determine not only how things are used, but how it can influence their billing.

Business use

We've found that that's a place where Vertica has actually been quite a pop for us in talking to customers. They want to know not just the simple metrics of the machines' operation, but how that reflects the business use of it.

The entire market has shifted and continues to shift. I was somewhat taken aback only a couple of weeks ago, when I found out that you can no longer buy a jet engine. I thought this was a piece of hardware you purchased, as opposed to something that you may have rented and paid per use. And so [the model changes to leasing] as the machines get  bigger and bigger. We have GE and the Bureau of Engraving and Printing as customers.

We certainly have some very large machines connected to our cloud and we're finding that these folks are shifting away from the notion that one owns a machine and consumes it until it breaks or dies. Instead, one engages in an ongoing service model, in which you're paying for the use of that machine.

While we can generate that data and provide some degree of visibility and insight into that data, it takes a massive analytics platform to really get the granular patterns that would drive business decisions.

Gardner: It sounds like many of your customers have used this for some basic blocking and tackling about inventory and access and control, then moved up to a business metrics of how is it being used, how we're billing, audit trails, and that sort of thing. Now, we're starting to look at a whole new type of economy. It's a services economy, based on cloud interactivity, where we can give granular insights, and they can manage their business very, very tightly.
There's not only a ton of data being generated, but the regulatory and compliance requirements which dictate where you can even leave that data at rest.

Any thoughts about what's going to be required of your organization to maintain scale? The more use cases and the more success, of course, the more demand for larger data and even better analytics. How do you make sure that you don't run out of runway on this?

Holbrook: There are a couple of strategies we've taken, but before I dive into that, I'll say that the issue is further complicated by the issue of data homing. There's not only a ton of data being generated, but the regulatory and compliance requirements which dictate where you can even leave that data at rest. Just moving it around is one problem, and where it sits on a disk is a totally different problem. So we're trying to tackle all of these.

The first way to address the scale for us from an architectural perspective was to try to distribute the connectivity. In order for you to know that something's running, you need to hear from it. You might be able to reach out, what we call contactability, to say, "Tell me if you're still running." But, by and large, you know of a machine's existence and its operation by virtue of it telling you something. So even if a message is nothing more than "Hello, I'm here," you need to hear from this device.

From the connectivity standpoint, our goal is not to try to funnel all of this into a single pipe, but rather to find where to get a point of presence that is closest and that is reasonable. We’ve been doing this on our remote-access technology for years, trying to find the appropriate geographically distributed location to route data through, to provide as easy and seamless an experience as possible.

So that’s the first, as opposed to just ruthlessly federating all incoming data, distributing the connectivity infrastructure, as well as trying to get that data routed to its end consumer as quickly as possible.

We break down data from our perspective into three basic temporal categories. There's the current data, which is the value you would see reading a dial on the machine. There's recent data, which would tell you whether something is trending in a negative direction, say pressure going up. Then, there is the longer-term historical data. While we focus on the first two, we’d deliberately, to handle the scale problem, don't focus on the long-term historical data.

Recent data

I'll treat recent data as being anywhere from 7 to 120 days and beyond, depending on the data aggregation rates. We focus primarily on that. When you start to scale beyond that, where the real long tail of this is, we try to make sure that we have our partner in place to receive the data.

We don't want to be diving into two years of data to determine seasonal trending when we're attempting to collect data from 1.5 million assets and acting as quickly as possible to respond to error conditions at the edge.

Gardner: Kevin, what about the issue of latency? I imagine some of your customers have a very dire need to get analysis very rapidly on an ongoing streamed basis. Others might be more willing to wait and do it in a batch approach in terms of their analytics. How do you manage that, and what are some of the speeds and feeds about the best latency outcomes?

Holbrook: That’s a fantastic question. Everybody comes in and says we need a zero-latency solution. Of course, it took them about two-and-a-half seconds to say that.
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There's no such thing as real-time, certainly on the Internet. Just negotiating up the TCP stack and tearing it down to send one byte is going to take you time. Then, we send it over wires under the ocean, bounce it off a satellite, you name it. That's going to take time.

There are two components to it. One is accepting that near-real-time, which is effectively the transport latency, is the smallest amount of time it can take to physically go from point A to point B, absent having a dedicated fiber line from one location to the other. We can assume that on the Internet that's domestically somewhere in the one- to two-second range. Internationally, it's in the two- to three-second or beyond range, depending on the connectivity of the destination.

What we provide is an ability to produce real-time streams of data outbound. You could take from one asset, break up the information it generates, and stream it to multiple consumers in near-real-time in order to get the dashboard in the control center to properly reflect the state of the business. Or you can push it to a data warehouse in the back end, where it then can be chunked and ETLd into some other analytics tool.

For us, we try not to do the batch ETLing. We'd rather make sure that we handle what we're good at. We're fantastic at remote service, at automating responses, at connectivity and at expanding what we do. But we're never going to be a massive ETL, transforming and converting into somebody’s data model or trying to get deep analytics as a result of that.

Gardner: Was it part of this need for latency, familiarity, and agility that led into Vertica? What were some of the decisions that led to picking Vertica as a partner?

Several reasons

Holbrook: There were a few reasons. That was one of them. Also the fact that there's a massive set of offerings already on top of it. A lot of the other people when we considered this -- and I won't mention competitors that we looked at -- were more just a piece of the stack, as opposed to a place where solutions grew out of.

It wasn't just Vertica, but the ecosystem built on top of Vertica. Some of the vendors we looked at are currently in the partner zone, because they're now building their solutions on top of Vertica.

We looked at it as an entry point into an ecosystem and certainly the in-memory component, the fact that you're getting no disk reads for massive datasets was very attractive for us. We don’t want to go through that process. We've dealt with the struggles internally of trying to have a relational data model scale. That’s something that Vertica has absolutely solved.

Gardner: Now your platform includes application services, integration framework, and data management. Let’s hone in on the application services. How are developers interested in getting access to this? What are their demands in terms of being able to use analysis outcomes, outputs, and then bring that into an application environment that they need to fulfill their requirements to their users?
It wasn't just Vertica, but the ecosystem built on top of Vertica. Some of the vendors we looked at are currently in the partner zone, because they're now building their solutions on top of Vertica.

Holbrook: It breaks them down into two basic categories. The first is the aggregation and the collection of data, and the second is physical interaction with the device. So we focus on both about equally. When we look at what developers are doing, almost always it’s transforming the data coming in and reaching out to things like a customer relationship management (CRM) system. It's opening a ticket when a device has thrown a certain error code or integrating with a backend drop-ship distribution system in the event that some consumable has begun to run low.

In terms of interaction, it's been significant. On the data side, we primarily see that they're  extracting subsets of data for deeper analysis. Sometimes, this comes up in discrete data points. Frequently, this comes up in the transfer of files. So there is a certain granularity that you can survive. Coming down the fire-hose is discrete data points that you can react to, and there's a whole other order of magnitude of data that you can handle when it's shipped up in a bulk chunk.

A good example is one of the use cases we have with GE in their oil and gas division  where they have a certain flow of data that's always ongoing and giving key performance indicators (KPIs). But this is nowhere near the level of data that they're actually collecting. They have database servers that are co-resident with these massive gas pipeline generators.

So we provide them the vehicle for that granular data. Then, when a problem is detected automatically, they can say, "Give me far more granular data for the problem area." it could be five minutes before or five minutes since. This is then uploaded, and we hand off to somewhere else.

So when we find developers doing integration around the data in particular, it's usually when they're diving in more deeply based on some sort of threshold or trigger that has been encountered in the field.
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Gardner: And lastly, Kevin, for other organizations that are looking to create data services and something like your Axeda Machine Cloud, are there any lessons learned that you could share when it comes to managing such complexity, scale, and the need for speed? What have you learned at a high level that you could share?

All about strategy

Holbrook: It’s all going to be about the data-collection strategy. You're going to walk into a customer or potential customer, and their default response is going to be, "Collect everything." That’s not inherently valuable. Just because you've collected it, doesn’t mean that you are going to get value from it. We find that, oftentimes, 90-95 percent of the data collected in the initial deployment is not used in any constructive way.

I would say focus on the data collection strategy. Scale of bad data is scale for scale’s sake. It doesn’t drive business value. Make sure that the folks who are actually going to be doing the analytics are in the room when you are doing your data collection strategy definition. when you're talking to the folks who are going to wire up sensors,  and when you're talking to the folks who are building the device.

Unfortunately, these are frequently within a larger business ,in particular, completely different groups of people that might report to completely different vice presidents. So you go to one group, and they have the connectivity guys. You talk about it and you wire everything up.
We find that, oftentimes, 90-95 percent of the data collected in the initial deployment is not used in any constructive way.

Then, six to eight months later, you walk into another room. They’ll say "What the heck is this? I can’t do anything with this. All I ever needed to know was the following metric." It wasn’t collected because the two hadn't stayed in touch. The success of deployed solutions and the reaction to scale challenges is going to be driven directly by that data-collection strategy. Invest the time upfront and then you'll have a much better experience in the back.

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

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Tuesday, March 17, 2015

Health Shared Services BC harnesses a healthcare ecosystem using IT asset management

The next BriefingsDirect innovation panel discussion examines how Health Shared Services BC in Vancouver improves process efficiency and standardization through better integration across health authorities in British Columbia, Canada.

We'll explore how HSSBC has successfully implemented one of the healthcare industry’s first Service Asset and Configuration Management Systems to help them optimize performance of their IT systems and applications.

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

To learn more about how HSSBC gains up-to-date single views of IT assets across a shared-services environment, please join me in welcoming our guests, Daniel Lamb, Project Manager for the ITSM Program, and Cam Haley, Program Manager for the ITSM Program, both at HSSBC. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Gentlemen, tell me first about the context of your challenge. You're an organization that's trying to bring efficiency and process improvements across health authorities in British Columbia. What is it about that task that made better IT service management (ITSM) an imperative?

Haley: If you look at the healthcare space, where it is right now within British Columbia, we have the opportunity to look at using our healthcare funding more efficiently and specifically focus on delivering more clinical outcomes for consumers of the services.

Haley
That was one of the main drivers behind the formation of HSSBC, to consolidate some of the key supporting and enabling services into an organization that could deliver a standardized set of service offerings across our health authority clients, so that they can focus on clinical delivery.

That was the key business driver around why we're here and why we are doing some of those things. For us to effectively deliver on that mandate, we need the tools and the process capabilities to be able to effectively deliver more consistent service outcomes, all those things that we want to deliver there, and to look at reducing cost a little long-term so that those cost could be again shifted into clinical delivery and to really enable those outcomes.

Necessary system

Gardner: Daniel, why was a Service Asset and Configuration Management System something that was important to accomplish this?
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Lamb: We have been in the process of a large data center migration project over the past three years, moving a lot of the assets out of Vancouver and into a new data center. We standardized on HP infrastructure up in Kamloops and we have -- when we put in all our Health Authorities assets, it's going to be upwards of around probably 6,500-7,000 servers to manage.
Lamb
As we merged to the super organization, the manual processes just don’t exist anymore. To keep those assets up-to-date we needed an automated system. The reason we went for those products, which included the asset side and the configuration service management, is that’s really our business. We're going to be managing all these assets for the organization and all the configuration items, and we are providing these services. So this is where the toolset really fitted our goals.

Gardner: So other than scale, size, and the migration, were there any other requirements or problems that you needed to solve that moving into this more modern ITSM capability delivered?

Haley: Just to build on what Daniel said, one of the key drivers in terms of identifying the toolset and the capabilities was to support the migration of infrastructure into the data center.

But along with that, we provide a set of services that go beyond data center. The tool capability that has been delivered in supporting that outcome enables us to focus on optimizing our processes, getting a better view into what's happening in our own environment. So having the configuration items (CIs) in the configuration management data base (CMDB), having the relationships develop both at the infrastructure level, but all the way up to the application or the business service level.

Now we have a view up and down the stack of what's going on. We get better analytics and better data, and we can make some better decisions as well around where we want to focus. What are the pain points that we need to target? We 're able to mine that stuff and really look at opportunities to optimize.

The tool allows us to standardize our processes and roll out the capabilities. Automation is built into the tool, which is fantastic for us in terms of taking that manual overhead out of that and really just allowing us to focus on other things. So it's been great.

Gardner: Any unexpected benefits, ancillary benefits, that come from the standardization with this visibility, knowing your organization better that maybe you didn't anticipate?

Up-to-date information

Lamb: We've been able to track down everything that’s out there. That’s one thing. We just didn’t know where everything was or what we had. So in terms of being able to forecast to the health authorities, "This is how much you need to part with for maintenance, that sort of thing," that was always a guess in the past. We now have that up-to-date information available.

This has also laid the platform for us to better take advantage of the new technologies that are coming in. So what HP is talking about at the moment, we can’t really take advantage of that, but they have this base platform. It’s going to allow us to take advantage of a lot of the new stuff that’s coming out.

Gardner: So in order to get the efficiency and cost benefits of new infrastructure and converged systems and data center efficiencies, having your ducks lined up and understood is a crucial first step.
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Lamb: Definitely.

Gardner: Looking down the road, what’s piquing your interest in terms of what HP is doing or new developments, or does this now allow you to then progress into other areas that you are interested in?

Lamb: Personally, I'm looking at obviously the new versions of the product sets we have at the moment. We've also been speaking to other customers on the success that we've had and giving them some lessons learned on how things worked.
One of the things that we have been able to do is enable our staff to be more effective at what they're doing.

Then, we're looking at some of other products we could build on to this -- the PPM, which is the Project Management toolset and the BSM, which is unified monitoring and that sort of thing. Being able to put those products on is where we'll start seeing even more value, like in terms of being able to reduce the amount of tickets and support cost and that sort of thing. So we're looking at that.

Then, just ad-hoc interest are the things around the big data and that sort of thing, just trying to get my head around how that works for us, because we have a lot of data. So some of those new technologies are coming out as well.

Gardner: Cam, given what you've already done, what has it gotten for you? What are some of the benefits and results that you have seen. Are there any metrics of success that you can share with us?

Haley: The first thing is that we're still pretty early in our journey out of the gate, if I just talk about what we've already achieved. One of the things that we have been able to do is enable our staff to be more effective at what they're doing.

We've implemented change management in particular within the toolset, and that’s giving us a more robust set of controls around what's actually happening and what’s actually going into the environment. That's been really important, not only for the staff, although there is bit of a learning curve around that, but in terms of the outcomes for our clients.

Comfort level

They have a higher comfort level that we have more insight or oversight into what’s actually happening in space and we are actually protecting the services that they need to deliver by putting those kinds of capabilities in. So from the process perspective, we've certainly been able to get some benefits in that area in particular.

From a client perspective, it's putting the toolset in it. It helps us develop that level of trust that we really need in order to have an effective partnering relationship with our clients. That’s something that hasn’t always been there in the past.

I'm not saying that we're all the way there yet, but we're starting to show that we can deliver the services that the health authorities expect us to deliver, and we are using the toolset to help enable that. That’s also an important aspect.

The other thing is that through the work we've done in terms of consolidating some of our contracts, maintenance agreements, and so on into our asset management system, we have a better view of what we're paying for. We've already realized some opportunities to consolidate some contracts and show some savings as well.
It helps us develop that level of trust that we really need in order to have an effective partnering relationship with our clients.

That's just a number of areas where we're already seeing some benefits. As we start to roll out more of the capabilities of the tool in the coming year and beyond that, we expect that we will get some of those standard metrics that you would typically get out of it. Of course, we'll continue to drive out the ROI value as well. So we're already a good way down that path, and we'll just continue to do that.

Gardner: Any words of wisdom, based on your journey so far, for other organizations that might be struggling with spreadsheets and tracking all of their assets and all of their devices and even the processes around IT support? What have you learned. What could you share to someone who is just starting out?
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Lamb: We had a few key lessons that we spoke about. One was the guiding principles that you are going to do the implementation by. We were very much of the approach that we would try to keep things as out-of-the-box as possible. HP, as they are doing the new releases, would pick up the functionality that we are looking for. So we didn’t do a lot of tailoring.
And we did the project in a short cycle. These projects can go on for years sometimes, and a lot of money can get sunk and there isn’t value gained sometimes. We said, "Let’s do these in more short sprint projects. We'll get something in, we'll start showing value to the organization, then we'll get into another thing." That’s the cycle that we're working in, and that's worked really well.

The other thing is that we had a great consultant partner that we worked with, and that was key. We were feeling a little lost when we came here last year, and that was one of the things we did. We went to a good consultant partner, Effectual Systems from San Francisco, and that helped us.

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

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