Wednesday, March 25, 2015

IT Operations Modernization Helps Energy Powerhouse Exelon Acquire Businesses

Transcript of a BriefingsDirect discussion on how a large utility operation uses HP tools to get better insight into IT operations.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: HP.

Dana Gardner: Hello, and welcome to the next edition of the HP Discover Podcast Series. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for this ongoing sponsored discussion on IT innovation and how it’s making an impact on people’s lives.

Gardner
Once again, we're focusing on how companies are adapting to the new style of IT to improve IT performance and deliver better user experiences, as well as better business results.

Our next innovation case study interview highlights how Exelon Corporation, based in Chicago, has been employing a great deal of technology and process to improve their operations and also to help manage a merger and acquisition transition period, as well as bring outsourced IT operations back in house.
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To learn more about how Exelon, a leading energy provider in the US with a family of companies having $23.5 billion in annual revenue, accomplishes these goals we're joined by Jason Thomas, Manager of Service, Asset and Release Management at Exelon. He's based in Baltimore. Welcome, Jason.

Jason Thomas: Thank you, Dana. Its a pleasure to be here.

Gardner: I gave a brief overview of Exelon, but tell us a little bit more. It's quite a large organization that you're involved with.

Thomas: We are vast and expansive. We have a large nuclear fleet, around 40-odd nuclear power plants in three utilities, ComEd in Chicago, in the Illinois space; PECO out of Philadelphia; and BG and E in Baltimore.

So we have large urban utilities center. We also have a large retail presence with the Constellation brand and the sale of power both to corporations and to users. So there's a lot of that we do obviously in the utility space, and there are some element of the trade, the commodity trading side, as well in trading power in these markets.

Gardner: I imagine it must be quite a large IT organization to support all that?

Thomas: There are 1,200 to 1,300 IT employees across the company.

Gardner: Tell us about some of the challenges that you've been facing in managing your IT operations and making them more efficient. And, of course, we'd like to hear more about the merger between Constellation and Exelon back in 2012.

Merger is a challenge

Thomas: The biggest challenge is the merger. Obviously, our scale and the number of, for lack of a better word, things that we had to monitor, be aware of, and know about vastly increased. So we had to address that.

Thomas
A lot of our efforts around the merger and post-merger were around bringing everything into one standard monitoring platform, extending that monitoring out, leveraging the Business Service Management (BSM) suite of products, leveraging Universal Configuration Management Database (UCMDB).

Then there was a lot around consolidating asset management. In early 2013, we moved to Asset Manager as our asset manager platform of choice, consolidating data from Exelon from their tool, the Cergus CA Argis tool, into Asset Manager in support of moving to new IT billing that would be driven out of the data and Asset Manager in leveraging some of the executive scorecard and financial manager pieces to make that happen.

There was also a large effort through 2013 to move the company to a standardized platform to support our service desk, incident management, and also our service catalog for end-users. But a lot of this was driven last year around the in-sourcing of our relationship with Computer Sciences Corporation for our IT operations.

This was to basically realize a savings to the company of $12 to $15 million annually from the management of that contract, and also to move both the management and the expertise in house and leverage a lot of the processes that we built up and that had grown through the company as a whole.

Gardner: So knowing yourself well in terms of your IT infrastructure and all the elements of that is super important, and then bringing in-sourcing transition to the picture, involves quite a bit of complexity.
You've leveled the playing field and you have that common set of tools that you're going to drive to take you to the next level.

What do you get when you do this well? Is there a sense of better control, better security, or culture? What is it that rises to the top of your mind when you know that you have your IT service management (ITSM) in order, when you have your assets and configuration management data in order. Is it sleeping better at night? Is it a sense of destiny you have fulfilled -- or what?

Thomas: Sleeping better at night. There is an element of that, but there's also sometimes the aspect of, "Now what's next?" So, part of it is that there's an evolutionary aspect too. We've gotten everything in one place. We're leveraging some of the integrations, but then what’s next?

It's more restful. It's now deciding how we better position ourselves to show the value of these platforms. Obviously, there's a clear monetary value of what we did to in-source, but now how do we show the business the value that we have done? Moving to a common set of tools helps to get there. You've leveled the playing field and you have that common set of tools that you're going to drive to take you to the next level.

Gardner: What might that next level be? Is it a cloud transition? Is it more of a hybrid sourcing for IT? Is this enabling you to take advantage of the different devices in terms of mobile? Where does it go?

Automation and cloud

Thomas: A lot of it is really around automation, the intermediate step around cloud. We've looked at cloud. We do have areas where the company has leveraged it. IT is still trying to wrap their heads around how we do it, and then also how we expose that to the rest of the organization.

But the steps we’ve done around automation are very key in making leaner operations, IT operations, but also being able to do things in an automated fashion, as opposed to requiring the manual elements that, in some cases, we had never done prior to the merger.

Gardner: Any examples? You mentioned $15 million in savings, but are there any other metrics of success or key performance indicator (KPI)-level paybacks that you can point to in terms of having all this in place for managing and understanding your IT?

Thomas: We're still going through what it is we're going to measure and present. There's been a standard set of things that we've measured around our availability and our incidents and whether these incidents are caused by IT, by infrastructure.
One of the key things is how you're changing and how you do IT operations.

We've done a lot better operationally. Now it's taking some of those operational aspects and making them a little bit more business-centric. So for the KPIs, we're going through that process of determining what we're going to measure ourselves against.

Gardner: Jason, having gone through quite a big and complex undertaking in getting your ITSM and Application Lifecycle Management (ALM) activities, what comnes next? Maybe a merger and acquisition is going to push you in a new direction.

Thomas: We recently announced the intent to acquire Pepco Holdings, which is the regional utility in Washington, DC area, that further widens our footprint in the mid-Atlantic area. So yeah, we get to do it all over again with a new partner, bringing Pepco in and doing some elements of this again.

Gardner: Having gone through this and anticipating yet another wave, what words of wisdom might you provide in hindsight for those who are embarking on a more automated, streamlined, and modern approach to IT operations?
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Thomas: One of the key things is how you're changing and how you do IT operations. Moving towards automation, tools aside, there's a lot of organizational change if you're changing how people do what they do or changing people's jobs or the perception of that.

You need to be clear. You need to clearly communicate, but you also need to make sure that you have the appropriate support and backing from leadership and that the top-down communication is the same message. We certainly had that, and it was great, but there's alway going to be that challenge of making sure everybody is getting that communication, getting the message, and getting constant reinforcement of that.

Organizational changes resulting from a large merger or acquisition are huge. It's key to show the benefits, even to the people who are obviously going to reap some of these immediate benefits,  those in IT. You know the business is going to see some. It's couching that value in the means or method appropriate for those actors, all of those stakeholders.

Full circle

Gardner: Of course, you have mentioned working through a KPI definition and working the executive scorecard. That makes if full circle, doesn’t it?

Thomas: Defining those KPIs, but also having one place where those KPIs can be viewed, seen easily, and drilled into is big. To date, it's been a challenge to provide some of that historiography around that data. Now, you have something where you can even more readily drill into it to see that data -- and that’s huge.

Presenting that, being able to show it, and being able to show it in a way that people can see it easily, is huge, as opposed to just saying, "Well, here's the spreadsheet with some graphs" or "Here’s a whiz-bang PowerPoint doc."

Gardner: And, Jason, I suppose this points to the fact that IT is really maturing. Compared to other business services and functions in corporations, things that had been evolving for 80 or 100 years, IT is, in a sense, catching up.
Now, you have something where you can even more readily drill into it to see that data -- and that’s huge.

Thomas: It's catching up, but I also think it's more of a reflection. It's reflection of a lot of the themes of the new style of IT. A lot of that is that consumerization aspect. In fact,  if you look at the last 10 years ago, the wide presence of all of these, your smart devices and your smartphones, is huge.

We have brought to most people something that was never easily accessible. And having to take that same aspect and make it part of how you present what you do in IT is huge. You see it in how you're manifesting it in your various service catalogs and some of the efforts that we're undertaking to refine and better the processes that underlie our technical service catalog to have a better presentation layer.

That technical service catalog will refer to what we've seen with Propel. It's an easier, nicer, friendlier way to interact, and people expect that. Why can’t this be more like my app store, or why can't this be more like X.

Is IT catching up or has IT become more reachable, has become more warm and fuzzy as opposed to something that’s cold, hard, and stored away somewhere? You kind of know about it, and perhaps the guys in the basement are the ones who are doing all the heavy lifting, and it's more tangible.

Gardner: Humanization of IT perhaps.

Thomas: Absolutely.

Gardner: All right, one last area I want to get into before we sign off. We've heard quite a bit  about The Machine, HP unveiling more detail from its labs activities. It’s not necessarily on a product roadmap yet, but it’s described through a lower footprint, much more rapid ability to join compute and memory, and then  reduce the size of the data center down to a size of a refrigerator.

I know that it's on the horizon, but how does that strike you, and how interesting is that for you?

Ramp up/ramp down

Thomas: It's interesting, because it allows you to get to bit more ability to ramp up or ramp down, based on what you need, as opposed to you having x amount of servers and x amount of storage that's always somewhere. It gives you a lot more flexibility and, to some extent, gives you a bit more tenability. It's directly applicable to certain aspects of the business, where you need that capability to ramp up and ramp down much more easily.
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I had a conversation with one of my peers about that. We were talking about how both that and the Moonshot aspect and the ability to have that for a lot of the customer-facing websites, and the ability to tie them, in particular, the utility customer-facing websites whose utilization tends to spike during weather events.

While they don't spike all at the same time, there is the potential opportunity in the Mid-Atlantic of all the utilities spiking at the same time around a hurricane or Sandy-esque event. There's obviously a need to able to respond to that kind of demand, and that technology positions you with the flexibility to do that rather quickly and easily.
It gives you a lot more flexibility and, to some extent, gives you a bit more tenability.

Gardner: Well, great. I'm afraid we will have to leave it there. We've been talking with Exelon Corporation, based in Chicago, about their journey towards a better handling of IT and transitions through mergers and acquisitions as a result of having a much deeper sense of their IT operations.

So a big thank you to our guest, Jason Thomas, Manager of Service Asset and Release Management at Exelon. Thank you, sir.

Thomas: Thank you, Dana. My pleasure.

Gardner: And also a big thank you to our audience for joining us for this special new style of IT discussion. I'm Dana Gardner; Principal Analyst at Interarbor Solutions, your host for this ongoing series of HP sponsored discussions. Thanks again for listening, and come back next time.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: HP.

Transcript of a BriefingsDirect discussion on how a large utility operation uses HP tools to get better insight into IT operations. Copyright Interarbor Solutions, LLC, 2005-2015. All rights reserved.

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Thursday, March 19, 2015

Axeda Builds Machine Cloud on HP Vertica to Deliver On-Demand Analysis Services

Transcript of a BriefingsDirect discussion on how to manage machine-to-machine communication for better big data collection and analysis.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: HP.

Dana Gardner: Hello, and welcome to the next edition of the HP Discover Podcast Series. I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for this ongoing sponsored discussion on IT innovation and how it’s making an impact on people’s lives. Once again, we're focusing on how companies are adapting to the new style of IT to improve IT performance and deliver better user experiences, as well as better business results.
Gardner

Our next innovation case study interview highlights how Axeda, based in Foxboro,  Massachusetts, is creating a machine-to-machine (M2M) capability for analysis -- in other words, an Axeda Machine Cloud.

We're going to learn how they partner with HP in doing that. We're joined in our discussion today by Kevin Holbrook, the Senior Director of Advance Development at Axeda. Welcome, Kevin.

Kevin Holbrook: Thank you for having me.

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 Internet of Things 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 access, telnet 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.

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.

Gardner: Very good. I'm afraid we’ll have to leave it there. We've been learning how Axeda in Foxboro Massachusetts is providing services to its customers to the Axeda Machine Cloud and as a partner with HP as using their Vertica Analysis Platform to provide those insights.
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So I'd like to thank our guest. We've been joined by Kevin Holbrook, the Senior Director of Advanced Development at Axeda. Thank you, Kevin.

Holbrook: Thank you.

Gardner: And a big thank you to our audience for joining us for the special new style of IT discussion. I'm Dana Gardner; Principal Analyst at Interarbor Solutions, your host for this ongoing series of HP sponsored discussions. Thanks again for listening, and come back next time.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: HP.

Transcript of a BriefingsDirect discussion on how to manage machine-to-machine communication for better big data collection and analysis. Copyright Interarbor Solutions, LLC, 2005-2015. All rights reserved.

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Tuesday, March 17, 2015

Health Shared Services BC Harnesses a Healthcare Ecosystem Using IT Asset Management

Transcript of a BriefingsDirect discussion on how a healthcare organization in Canada has improved process efficiency and standardization.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: HP.

Dana Gardner: Hello, and welcome to the next edition of the HP Discover Podcast Series. I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for this ongoing sponsored discussion on technology innovation and how it’s making an impact on people's lives.

Gardner
Once again, we're focusing on how companies are adapting to the new style of IT to improve IT performance and deliver better user experiences, as well as better business results.

Our next innovation case study interview examines how Health Shared Services BC in Vancouver has improved process efficiency and standardization through better integration across health authorities in British Columbia, Canada.

We'll learn 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.
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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 at HSSBC. Welcome Daniel.

Daniel Lamb: Thank you.

Gardner: We also here with Cam Haley, Program Manager for the same ITSM Program at HSSBC. Welcome, Cam.

Cam Haley: A pleasure to be here.

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?

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?

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, and that helped us.

Gardner: And who was that consultant partner?

Lamb: Effectual Systems from San Francisco.

Gardner: Well, great. I'm afraid we will have to leave it there. We have been learning about how Health Shared Services BC in Vancouver has been gaining an up-to-date single view of their IT assets across a shared services environment.
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So a huge thank you to our guests, Daniel Lamb, Project Manager for the ITSM Program, and Cam Haley, Program Manager for the ITSM Program, both at HSSBC.

And a big thank you to our audience as well for joining us for this special new style of IT discussion. I'm Dana Gartner, Principal Analyst at Interarbor Solutions, your host for this ongoing series of HP-sponsored discussions. Thanks again for listening, and come back next time.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: HP.

Transcript of a BriefingsDirect discussion on how a healthcare organization in Canada has improved process efficiency and standardization. Copyright Interarbor Solutions, LLC, 2005-2015. All rights reserved.

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Thursday, March 12, 2015

How a Hackathon Approach Juices Innovation on Big Data Applications for Thomson Reuters

Transcript of a BriefingsDirect discussion on how information giant Thomson Reuters leveraged a hackathon to spur new ideas among its big data-focused developers.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: HP.

Dana Gardner: Hello, and welcome to the next edition of the HP Discover Podcast Series. I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for this ongoing discussion on technology innovation and how it's making an impact on people’s lives.

Gardner
Once again, we're focusing on a major trend, how big data changes everything, and how companies are adapting to the new style of IT to gain new insights, deliver better user experiences, and better overall business results.

Our next innovation interview explores the use of a hackathon approach to unlock creativity in the search for better use of big data for analytics. We will hear how Thomson Reuters in London sought to foster innovation and derive more value from its vast trove of business and market information.

The result: A worldwide virtual hackathon that brought together developers and data scientists to uncover new applications, visualizations, and services to make all data actionable and impactful.
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To learn more about getting developers on board the big-data analysis train, please join me in welcoming our guest, Chris Blatchford, Director of Platform Technology in the IT organization at Thomson Reuters in London. Welcome, Chris.

Chris Blatchford: Hi. Good to be here.

Blatchford
Gardner: Tell us about Thomson Reuters, the organization, for those who don't know.

Blatchford: Thomson Reuters is the world's leading source of intelligent information. We provide data across the finance, legal, news, IP, and science, tax, and accounting industries through product and service offerings, combining industry expertise with innovative technology.

Gardner: It’s hard to think of an organization where data and analysis is more important. It’s so core to your very mission.

Blatchford: Absolutely. We take data from a variety of sources. We have our own original data, third-party sources, open-data sources, and augmented information, as well as all of the original content we generate on a daily basis. For example, our journalists in the field provide original news content to us directly from all over the globe. We also have third-party licensed data that we further enrich and distribute to our clients through a variety of tools and services

Gardner: And therein lies the next trick, what to do with the data once you have it. About this hackathon, how did you come up upon that as an idea to foster innovation?

Big, Open, Linked Data

Blatchford: One of our big projects or programs of work currently is, as everyone else is doing, big data. We have an initiative called BOLD, which is Big, Open, Linked Data, headed up by Dan Bennett. The idea behind the project is to take all of the data that we ingest and host within Thomson Reuters, all of those various sources that I just explained, stream all of that into a central repository, cleanse the data, centralize it, extract meaningful information, and subsequently expose it to the rest of the businesses for use in their specific industry applications.

As well as creating a central data lake of content, we also needed to provide the tools and services that allow businesses to access the content; here we have both developed our own software and licensed existing tools.

So, we could demonstrate that we could build big-data tools using our internal expertise, and we could demonstrate that we could plug in third-party specific applications that could perform analysis on that data. What we hadn’t proved was that we could plug in third-party technology enterprise platforms in order to leverage our data and to innovate across that data, and that’s where HP came in.

HP was already engaged with us in a number of areas, and I got to speaking with their Big Data Group around their big data solutions. IDOL OnDemand came up. This is now part of the Haven OnDemand platform. We saw some synergies there between what we were doing with the big-data platform and what they could offer us in terms of their IDOL OnDemand API’s. That’s where the good stuff started.

Gardner: Software developers, from the very beginning, have had a challenge of knowing their craft, but not knowing necessarily what their end users want them to do with that craft. So the challenge -- whether it’s in a data environment, a transactional environment or interface, or gaming -- has often been how to get the requirements of what you're up to into the minds of the developers in a way that they can work with. How did the hackathon contribute to solving that?
As well as creating a central data lake of content, we also need to provide the tools and services that allow businesses to access the content.

Blatchford: That’s a really good question. That’s actually one of the biggest challenges big data has in general. We approach big data in one of two ways. You have very specific use cases, for example, consider a lawyer working on a particular case for a client, it would be useful for them to analyze prior cases with similar elements. If they are able to extract entities and relevant attributes, they may be able to understand the case final decision, or perhaps glean information that is relevant to their current case.

Then you have the other approach, which is much more about exploration, discovering new insights, trends, and patterns. That’s similar to the the approach we wanted to take with the hackathon -- provide the data and the tools to our developers for them just to go and play with the data.

We didn’t necessarily want to give them any requirements around specific products or services. It was just, "Look, here is a cool platform with some really cool APIs and some capabilities. Here is some nice juicy data. Tell us what we should be doing? What can we come up with from your perspective on the world?"

A lot of the time, these engineers are overlooked. They're not necessarily the most extroverted of people by the nature of what they do and so they miss chances, they miss opportunities, and that’s something we really wanted to change.

Gardner: It’s fascinating the way to get developers to do what you want them to do is to give them no requirements.

Interesting end products

Blatchford: Indeed. That can result in some interesting end-products. But, by and large, our engineers are more commercially savvy than most, hence we can generally rely on them to produce something that will be compelling to the business. Many of our developers have side projects and personal development projects they work on outside of the realms of their job requirement. We should be encouraging this sort of behavior.

Gardner: So what did you get when you gave them no requirements? What happened?

Blatchford: We had 25 teams that submitted their ideas. We boiled that down to 7 finalists based upon a set of preliminary criteria, and out of those 7, we decided upon our first-, second-, and third-place winners. Those three end results were actually taken, or are currently going through a product review, to potentially be implemented into our product lines.

The overall winner was an innovative UI design for mobile devices, allowing users to better navigate our content on tablets and phones. There was a sentiment analysis tool, that allowed users to paste in news stories or any news content source on the web and extract sentiment from that news story.

And the other was more of an internally focused, administrative exploration tool, that  allowed us to more intuitively navigate our own data, which perhaps doesn’t initially seem as exciting as the other two, but is actually a hugely useful application for us.
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Gardner: Now, how does IDOL OnDemand come to play in this? IDOL is the ability to take any kind of information, for the most part, apply a variety of different services to it, and then create analysis as a service. How did that play into the hackathon? How did the developers use that?

Blatchford: Initially the developers looked at the original 50-plus APIs that IDOL OnDemand provides, and you have everything in there from facial recognition, to OCR, to text analytics, to indexing, all sorts of cool stuff. Those, in themselves, provided sufficient capabilities to produce some compelling applications, but our developers also utilized Thomson Reuters API’s and resources to further augment the IDOL platform.

This was very important, as it demonstrated that not only could we plug in an Enterprise analytics tool into our data, but also that it would fit well with our own capabilities.

Gardner: And HP Big Data also had a role in this. How did that provide value?

Five-day effort

Blatchford: The expertise. We should remember we stood this hackathon up from inception to completion in a little over one month, and that’s I think pretty impressive by any measure.

The actual hackathon lasted for five days. We gave the participants a week to get familiar with the APIs, but they really didn’t need that long because the documentation behind the APIs on IDOL OnDemand and the kind of "try it now" functionality it has was amazing. This is what the engineers and the developers were telling me. That’s not my own words.

The Big Data Group was able to stand this whole thing up within a month, a huge amount of effort on HP’s side that we never really saw. That ultimately resulted in a hugely successful virtual global hackathon. This wasn’t a physical hackathon. This was a purely virtual hackathon the world over.

Gardner: HP has been very close to developers for many years, with many tools, leading tools in the market for developers. They're familiar with the hackathon approach. It sounds like HP might have a business in hackathons as a service. You're proving the point here.

For the benefit of our listeners, if someone else out there was interested in applying the same approach, a hackathon as a way of creating innovation, of sparking new thoughts, light bulbs going off in people's heads, or bringing together cultures that perhaps hadn't meshed well in the past, what would you advise them?
First and foremost, the reason we were successful is because we had a motivated, willing partner in HP.

Blatchford: That’s a big one. First and foremost, the reason we were successful is because we had a motivated, willing partner in HP. They were able to put the full might of their resources and technology capabilities behind this event, and that along side our own efforts ultimately resulted in the events success.

That aside, you absolutely need to get the buy-in of the senior executives within an organization, get them to invest into the idea of something as open as a hackathon. A lot of hackathons are quite focused on a specific requirement. We took the opposite approach. We said, "Look, developers, engineers, go out there and do whatever you want. Try to be as innovative in your approach as possible."

Typically, that approach is not seen as cost effective, businesses like to have defined use cases, but sometimes that can strangle innovation. Sometimes we need to loosen the reins a little.

There are also a lot of logistical checks that can help. Ensure you have clear criteria around hackathon team size and members, event objectives, rules, time frames and so on. Having these defined up front makes the whole event run much smoother.

We ran the organization of the event a little like an Agile project, with regular stand-ups and check-ins. We also stood up a dedicated internal intranet site with all the information above. Finally, we set-up user accounts on the IDOL platform early on, so the participants could familiarize themselves with the technology.

Winning combination

Gardner: Yeah, it really sounds like a winning combination: the hackathon model, big data as the resource to innovate on, and then IDOL OnDemand with 50 tools to apply to that. It’s a very rich combination.

Blatchford: That’s exactly right. The richness in the data was definitely a big part of this. You don’t need millions of rows of data. We provided 60,000 records of legal documents and we had about the same in patents and news content. You don’t need vast amounts of data, but you need quality data.

Then you also need a quality platform as well. In this case IDOL OnDemand.The third piece is what’s in their heads. That really was the successful formula.
You don’t need vast amounts of data, but you need quality data.

Gardner: I have to ask. Of course, the pride in doing a good job goes a long way, but were there any other incentives; a new car, for example, for the winning hackathon application of the day?

Blatchford: Yeah, we offered a 1960s Mini Cooper to the winners. No, we didn't. We did offer other incentives. There were three main incentives. The first one, and the most important one in my view, and I think in everyone’s view, was exposure to senior executives within the organization. Not just face time, but promotion of the individual within the organization. We wanted this to be about personal growth as much as it was about producing new applications.

Going back to trying to leverage your resources and give them opportunities to shine, that’s really important. That’s one of the things the hackathon really fostered -- exposing our talented engineers and product managers, ensuring they are appreciated for the work they do.

We also provided an Amazon voucher incentive, and HP offered some of their tablets to the winners. So it was quite a strong winning set.

Gardner: Sounds like a very successful endeavor others out there might be interested in emulating, so I am glad we could find out more about it.

I'm afraid we're going to have to leave it there. We've been learning about how Thomson Reuters in London sought to foster innovation and derive more value from its vast trove of business and market data, and how they used a virtual worldwide hackathon approach to bring together developers and data scientists to uncover new applications and services.

So a big thank you to our guest, Chris Blatchford, the Director of Platform Technology in the IT organization at Thomson Reuters in London. Thank you, Chris.

Blatchford: Thanks.
Bringing human understanding to the cloud
Helping developers build a new class of apps
Gardner: And I'd like to thank our audience as well for joining us for this special how Big Data Changes Everything discussion. We've explored solid evidence from early adopters of how Big Data changes everything, and how companies are adapting to the new style of IT to gain new insights, deliver better user experiences, and better overall business results.

I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your host for this ongoing series of HP-sponsored discussions. Thanks again for listening, and come back next time.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: HP.

Transcript of a BriefingsDirect discussion on how information giant Thomson Reuters leveraged a hackathon to spur new ideas among its big data-focused developers. Copyright Interarbor Solutions, LLC, 2005-2015. All rights reserved.

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