Nimble Storage Leverages Big Data and Cloud to Produce Data Performance Optimization on the Fly

Transcript of a BriefingsDirect podcast on how a hybrid storage provider can analyze operational data to bring about increased efficiency.

Listen to the podcast. Find it on iTunes. Download the transcript.

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

Gardner

Once again, we’re focusing on how IT leaders are improving their business performance for better access, use and analysis of their data and information.

Our next innovation case study focuses on how optimized hybrid storage provider Nimble Storage has leveraged big data and cloud to produce significant storage  performance, and efficiency. Nimble is, of course, also notable for its recent successful IPO.

Learn here how Nimble Storage has leveraged the HP Vertica analytics platform to analyze operational data on mixed-storage environments to optimize workloads. High-performing, cost-effective big-data processing via cloud helps to make the best use of dynamic storage resources, it turns out. A fascinating story.

To learn more, join me in welcoming our guest, Larry Lancaster, Chief Data Scientist at Nimble Storage Inc. in San Jose, California. Welcome, Larry.

Larry Lancaster: Hi, Dana, it's great to talk to you today.

Gardner: I'm glad you could join us. As I said, it's a fascinating use-case. Tell us about the general scope of how you use data in the cloud to create this hybrid storage optimization service.

Lancaster: At a high level, Nimble Storage recognized early, near the inception of the product, that if we were able to collect enough operational data about how our products are performing in the field, get it back home and analyze it, we'd be able to dramatically reduce support costs. Also, we can create a feedback loop that allows engineering to improve the product very quickly, according to the demands that are being placed on the product in the field.

Lancaster

Looking at it from that perspective, to get it right, you need to do it from the inception of the product. If you take a look at how much data we get back for every array we sell in the field, we could be receiving anywhere from 10,000 to 100,000 data points per minute from each array. Then, we bring those back home, we put them into a database, and we run a lot of intensive analytics on those data.

Once you're doing that, you realize that as soon as you do something, you have this data you're starting to leverage. You're making support recommendations and so on, but then you realize you could do a lot more with it. We can do dynamic cache sizing. We can figure out how much cache a customer needs based on an analysis of their real workloads.

We found that big data is really paying off for us. We want to continue to increase how much it's paying off for us, but to do that we need to be able to do bigger queries faster. We have a team of data scientists and we don't want them sitting here twiddling their thumbs. That’s what brought us to Vertica at Nimble.

Using big data

Gardner: It's an interesting juxtaposition that you're using big data in order to better manage data and storage. What better use of it? And what sort of efficiencies are we talking about here, when you are able to get that data in that massive scale and do these analytics and then go back out into the field and adjust? What does that get for you?

Lancaster: We have a very tight feedback loop. In one release we put out, we may make some changes in the way certain things happen on the back end, for example, the way NVRAM is drained. There are some very particular details around that, and we can observe very quickly how that performs under different workloads. We can make tweaks and do a lot of tuning.

Without the kind of data we have, we might have to have multiple cases being opened on performance in the field and escalations, looking at cores, and then simulating things in the lab.

It's a very labor-intensive, slow process with very little data to base the decision on. When you bring home operational data from all your products in the field, you're now talking about being able to figure out in near real-time the distribution of workloads in the field and how people access their storage. I think we have a better understanding of the way storage works in the real world than any other storage vendor, simply because we have the data.

Gardner: So it's an interesting combination of a product lifecycle approach to getting data -- but also combining a service with a product in such a way that you're adjusting in real time.

Lancaster: That’s right. We do a lot of neat things. We do capacity forecasting. We do a lot of predictive analytics to try to figure out when the storage administrator is going to need to purchase something, rather than having them just stumble into the fact that they need to provision for equipment because they've run out of space.

That’s the kind of efficiency we gain that you can see, and the InfoSight service delivers that to our customers.

A lot of things that should have been done in storage from the very beginning that sound straightforward were simply never done. We're the first company to take a comprehensive approach to it. We open and close 80 percent of our cases automatically, 90 percent of them are automatically opened.

We have a suite of tools that run on this operational data, so we don't have to call people up and say, "Please gather this data for us. Please send us these log posts. Please send us these statistics." Now, we take a case that could have taken two or three days and we turn it into something that can be done in an hour.

That’s the kind of efficiency we gain that you can see, and the InfoSight service delivers that to our customers.

Gardner: Larry, just to be clear, you're supporting both flash and traditional disk storage, but you're able to exploit the hybrid relationship between them because of this data and analysis. Tell us a little bit about how the hybrid storage works.

Challenge for hard drives

Lancaster: At a high level, you have hard drives, which are inexpensive, but they're slow for random I/O. For sequential I/O, they are all right, but for random I/O performance, they're slow. It takes time to move the platter and the head. You're looking at 5 to 10 milliseconds seek time for random read.

That's been the challenge for hard drives. Flash drives have come out and they can dramatically improve on that. Now, you're talking about microsecond-order latencies, rather than milliseconds.

But the challenge there is that they're expensive. You could go buy all flash or you could go buy all hard drives and you can live with those downsides of each. Or, you can take the best of both worlds.

Then, there's a challenge. How do I keep the data that I need to access randomly in flash, but keep the rest of the data that I don't care so much about in a frequent random-read performance, keep that on the hard drives only, and in that way, optimize my use of flash. That's the way you can save money, but it's difficult to do that.

It comes down to having some understanding of the workloads that the customer is running and being able to anticipate the best algorithms and parameters for those algorithms to make sure that the right data is in flash.

It would be hard to be the best hybrid storage solution without the kind of analytics that we're doing.

We've built up an enormous dataset covering thousands of system-years of real-world usage to tell us exactly which approaches to caching are going to deliver the most benefit. It would be hard to be the best hybrid storage solution without the kind of analytics that we're doing.

Gardner: Then, to extrapolate a little bit higher, or maybe wider, for how this benefits an organization, the analysis that you're gathering also pertains to the data lifecycle, things like disaster recovery (DR), business continuity, backups, scheduling, and so forth. Tell us how the data gathering analytics has been applied to that larger data lifecycle equation.

Lancaster: You're absolutely right. One of the things that we do is make sure that we audit all of the storage that our customers have deployed to understand how much of it is protected with local snapshots, how much of it is replicated for disaster recovery,  and how much incremental space is required to increase retention time and so on.

We have very efficient snapshots, but at the end of the day, if you're making changes, snapshots still do take some amount of space. So, learning exactly what is that overhead, and how can we help you achieve your disaster recovery goals.

We have a good understanding of that in the field. We go to customers with proactive service recommendations about what they could and should do. But we also take into account the fact that they may be doing DR when we forecast how much capacity they are going to need.

Larger lifecycle

You're right. It is part of a larger lifecycle that we address, but at the end of the day, for my team it's still all about analytics. It's about looking to the data as the source of truth and as the source of recommendation.

We can tell you roughly how much space you're going to need to do disaster recovery on a given type of application, because we can look in our field and see the distribution of the extra space that would take and what kind of bandwidth you're going to need. We have all that information at our fingertips.

When you start to work this way, you realize that you can do things you couldn't do before. And the things you could do before, you can do orders of magnitude better. So we're a great case of actually applying data science to the product lifecycle, but also to front-line revenue and cost enhancement.

Gardner: I think this is a great example and I think you're a harbinger of what we're going to see more and more, which is bringing this high level of intelligence to bear on many other different services, for many different types of products. IT and storage is great and makes a lot of sense as an early adopter. But I can see this is pertaining to many other vertical industries. It illustrates where a lot of big-data value is going to go.

Now, let's dig into how you actually can get that analysis in the speed, at the scale, and at the cost that you require. Tell us about your journey in terms of different analytics platforms and data architectures that you've been using and where you're headed.

I have to tell you, I fell in love with Vertica because of the performance benefits that it provided.

Lancaster: To give you a brief history of my awareness of HP Vertica and my involvement around the product, I don’t remember the exact year, but it may have been eight years ago roughly. At some point, there was an announcement that Mike Stonebraker was involved in a group that was going to productize the C-Store Database, which was sort of an academic experiment at UC Berkeley, to understand the benefits and capabilities of real column store.

[Learn more about column store architectures and how they benefit data speed and management for Infinity Insurance.]

I was immediately interested and contacted them. I was working at another storage company at the time. I had a 20 terabyte (TB) data warehouse, which at the time was one of the largest Oracle on Linux data warehouses in the world.

They didn't want to touch that opportunity just yet, because they were just starting out in alpha mode. I hooked up with them again a few years later, when I was CTO at a company called Glassbeam, where we developed what's substantially an extract, transform, and load (ETL) platform.

By then, they were well along the road. They had a great product and it was solid. So we tried it out, and I have to tell you, I fell in love with Vertica because of the performance benefits that it provided.

When you start thinking about collecting as many different data points as we like to collect, you have to recognize that you’re going to end up with a couple choices on a row store. Either you're going to have very narrow tables and a lot of them or else you're going to be wasting a lot of I/O overhead, retrieving entire rows where you just need a couple fields.

Greater efficiency

That was what piqued my interest at first. But as I began to use it more and more at Glassbeam, I realized that the performance benefits you could gain by using HP Vertica properly were another order of magnitude beyond what you would expect just with the column-store efficiency.

That's because of certain features that Vertica allows, such as something called pre-join projections. We can drill into that sort of stuff more if you like, but, at a high-level, it lets you maintain the normalized logical integrity of your schema, while having under the hood, an optimized denormalized query performance physically on disk.

Now you might ask you can be efficient if you have a denormalized structure on disk. It's because Vertica allows you to do some very efficient types of encoding on your data. So all of the low cardinality columns that would have been wasting space in a row store end up taking almost no space at all.

What you find, at least it's been my impression, is that Vertica is the data warehouse that you would have wanted to have built 10 or 20 years ago, but nobody had done it yet.

Vertica is the data warehouse that you would have wanted to have built 10 or 20 years ago, but nobody had done it yet.

Nowadays, when I'm evaluating other big data platforms, I always have to look at it from the perspective of it's great, we can get some parallelism here, and there are certain operations that we can do that might be difficult on other platforms, but I always have to compare it to Vertica. Frankly, I always find that Vertica comes out on top in terms of features, performance, and usability.

Gardner: When you arrived there at Nimble Storage, what were they using, and where are you now on your journey into a transition to Vertica?

Lancaster: I built the environment here from the ground up. When I got here, there were roughly 30 people. It's a very small company. We started with Postgres. We started with something free. We didn’t want to have a large budget dedicated to the backing infrastructure just yet. We weren’t ready to monetize it yet.

So, we started on Postgres and we've scaled up now to the point where we have about 100 TBs on Postgres. We get decent performance out of the database for the things that we absolutely need to do, which are micro-batch updates and transactional activity. We get that performance because the database lives on Nimble Storage.

I don't know what the largest unsharded Postgres instance is in the world, but I feel like I have one of them. It's a challenge to manage and leverage. Now, we've gotten to the point where we're really enjoying doing larger queries. We really want to understand the entire installed base of how we want to do analyses that extend across the entire base.

Rich information

We want to understand the lifecycle of a volume. We want to understand how it grows, how it lives, what its performance characteristics are, and then how gradually it falls into senescence when people stop using it. It turns out there is a lot of really rich information that we now have access to to understand storage lifecycles in a way I don't think was possible before.

But to do that, we need to take our infrastructure to the next level. So we've been doing that and we've loaded a large number of our sensor data that’s the numerical data I have talked about into Vertica, started to compare the queries, and then started to use Vertica more and more for all the analysis we're doing.

Internally, we're using Vertica, just because of the performance benefits. I can give you an example. We had a particular query, a particularly large query. It was to look at certain aspects of latency over a month across the entire installed base to understand a little bit about the distribution, depending on different factors, and so on.

I'm really excited. We're getting exactly what we wanted and better.

We ran that query in Postgres, and depending on how busy the server was, it took  anywhere from 12 to 24 hours to run. On Vertica, to run the same query on the same data takes anywhere from three to seven seconds.

I anticipated that because we were aware upfront of the benefits we'd be getting. I've seen it before. We knew how to structure our projections to get that kind of performance. We knew what kind of infrastructure we'd need under it. I'm really excited. We're getting exactly what we wanted and better.

This is only a three node cluster. Look at the performance we're getting. On the smaller queries, we're getting sub-second latencies. On the big ones, we're getting sub-10 second latencies. It's absolutely amazing. It's game changing.

People can sit at their desktops now, manipulate data, come up with new ideas and iterate without having to run a batch and go home. It's a dramatic productivity increase. Data scientists tend to be fairly impatient. They're highly paid people, and you don’t want them sitting at their desk waiting to get an answer out of the database. It's not the best use of their time.

Gardner: Larry, is there another aspect to the HP Vertica value when it comes to the cloud model for deployment? It seems to me that if Nimble Storage continues to grow rapidly and scales that, bringing all that data back to a central single point might be problematic. Having it distributed or in different cloud deployment models might make sense. Is there something about the way Vertica works within a cloud services deployment that is of interest to you as well?

No worries

Lancaster: There's the ease of adding nodes without downtime, the fact that you can create a K-safe cluster. If my cluster is 16 nodes wide now, and I want two nodes redundancy, it's very similar to RAID. You can specify that, and the database will take care of that for you. You don’t have to worry about the database going down and losing data as a result of the node failure every time or two.

I love the fact that you don’t have to pay extra for that. If I want to put more cores or  nodes on it or I want to put more redundancy into my design, I can do that without paying more for it. Wow! That’s kind of revolutionary in itself.

It's great to see a database company incented to give you great performance. They're incented to help you work better with more nodes and more cores. They don't have to worry about people not being able to pay the additional license fees to deploy more resources. In that sense, it's great.

We have our own private cloud -- that’s how I like to think of it -- at an offsite colocation facility. We do DR through Nimble Storage. At the same time, we have a K-safe cluster. We had a hardware glitch on one of the nodes last week, and the other two nodes stayed up, served data, and everything was fine.

If you do your job right as a cloud provider, people just want more and more and more.

Those kinds of features are critical, and that ability to be flexible and expand is critical for someone who is trying to build a large cloud infrastructure, because you're never going to know in advance exactly how much you're going to need.

If you do your job right as a cloud provider, people just want more and more and more. You want to get them hooked and you want to get them enjoying the experience. Vertica lets you do that.

Gardner: I'm afraid we'll have to leave it there. We've been learning about how optimized hybrid storage provider Nimble Storage has leveraged big data and cloud to produce unique storage performance analytics and efficiencies. And we've seen how the HP Vertica Analytics platform has been used to analyze Nimble's operational data across mixed storage environments in near real-time, so that they can optimize their workloads and also extend the benefits to a data lifecycle.

So, a big thank you to our guest, Larry Lancaster, Chief Data Scientist at Nimble Storage. Thank you, Larry.

Lancaster: Thanks, Dana.

Gardner: Also, thank you to our audience for joining us for this special HP Discover Performance Podcast.

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

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

Transcript of a BriefingsDirect podcast on how a hybrid storage provider can analyze operational data to bring about increased efficiency.  Copyright Interarbor Solutions, LLC, 2005-2014. All rights reserved.

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Learn How HP Implemented the TippingPoint Intrusion Prevention System Across its Security Infrastructure

Transcript of a BriefingsDirect podcast on how the strategy of dealing with malware is shifting from reaction to prevention.

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 co-host and moderator for this ongoing discussion of IT innovation and how it’s making an impact on people’s lives.

Gardner

Once again, we’re focusing on how IT leaders are improving the security and availability of services to deliver better experiences and payoffs for businesses and end users alike.

We have a fascinating show today. We’re going to be exploring the ins and outs of improving enterprise intrusion prevention systems (IPS), and we will see how HP and its global cyber security partners have made the HP Global Network more resilient and safe. We’ll will hear how a vision for security has been effectively translated into actual implementation.

To learn more about how HP itself has created role-based and granular access control benefits amid real-time yet intelligent intrusion protection, please join me in welcoming our guest, Jim O'Shea, Network Security Architect for HP Cyber Security Strategy and Infrastructure Engagement. Welcome to the show, Jim.

Jim O’Shea: Hello, Dana. Thank you.

Gardner: Before we get into the nitty-gritty, what do you think are some of the major trends that are driving the need for better intrusion prevention systems nowadays?

O’Shea: If you look at the past, it was about detection, and you had reaction technologies. We had firewalls that blocked and looked at the port level. Then, we evolved to trying to detect things that were malicious with intent by using IDS. But that was a reactionary-type thing. It was a nice approach, but we were reacting. Something happened, you reacted, but if you knew it was bad, why did we let it in in the first place?

The evolution was the IPS, the prevention. If you know it's bad, why do you even want to see it? Why do you want to try to react to it? Just block it. That’s the trend that we’ve been following.

Gardner: But we can’t just have a black-and-white situation. It’s much more gray. There are sorts of intrusion, I suppose, that we want. We want access control, rather than just a firewall. So is there a new thinking, a new vision, that’s been developed over the past several years about these networks and what should or shouldn't be allowed through them?

O’Shea: You’re talking about letting the good in. Those are the evolutions and the trends that we are all trying to strive for. Get the good traffic in. Get who you are in. Maybe look at what you have. You can explore the health of your device. Those are all trends that we’re all striving for now.

Gardner: I recall Jim, that there was a Ponemon Institute report about a year or so ago that really outlined some of the issues here. Do you recall that? Were there any issues in there that illustrate this trend toward a different type of network and a different approach to protection?

Number of attacks

O’Shea: The Ponemon study was illustrating the vast number of attacks and the trend toward the costs for intrusion. It was highlighting those type of trends, all of which we’re trying to head off. Those type of reports are guiding factors in taking a more proactive, automated-type response. [Learn more about intrusion prevention systems.]

Gardner: I suppose what’s also different nowadays is that we’re not only concerned with outside issues in terms of risk, but also insider attacks. It’s being able to detect behaviors and things that occur that data can detect. The analysis can then provide perhaps a heads-up across the network, regardless of whether they have access or not. What are the risk issues now when we think about insider attacks, rather than just outside penetration?

O’Shea: You’re exactly right. Are you hiring the right people? That’s a big issue. Are they being influenced? Those are all huge issues. Big data can handle some of that and pull that in. Our approach on intrusion prevention wasn’t to just look at what’s coming from the outside, but it was also look at data traversing the network.

You have a whole rogue wireless-type approach in which people can gain access and can they probe and poke around.

When we deployed the TippingPoint solution, we didn’t change our policies or profiles that we were deploying based on whether it’s starting on the inside or starting on the outside. It was an equal deployment.

An insider attack could also be somebody who walks into a facility, gains physical access, and connects to your network. You have a whole rogue wireless-type approach in which people can gain access and can they probe and poke around. And if it’s malware traffic from our perspective, with the IDS we took the approach, inside or outside -- doesn’t matter. If we can detect it, if we can be in the path, it’s a block.

Gardner: For those of our listeners who might not be familiar with the term “intrusion prevention systems,” maybe you could illustrate and flesh that out a bit. What do we mean by IPS? What are we talking about? Are these technologies? Are these processes, methodologies, or all of the above?

O’Shea: TippingPoint technology is an appliance-based technology. It’s an inline device. We deploy it inline. It sits in the network, and the traffic is flowing through it. It’s looking for characteristics or reputation on the type of traffic, and reputation is a more real-time change in the system. This network, IP address, or URL is known for malware, etc. That’s a dynamic update, but the static updates are signature-type, and the detection of vulnerability or a specific exploit aimed at an operating system.

So intrusion prevention is through the detection of that, and blocking and preventing that from completing its communication to the end node.

Gardner: And these work in conjunction with other approaches, such as security information, event management, and network-based anomaly detection. Is that correct? How do they work together?

Bigger picture

O’Shea: All the events get logged into HP ArcSight to create the bigger picture. Are you seeing these type of events occurring other places? So you have the bigger picture correlation.

Network-based anomaly detection is the ability to detect something that is occurring in the network and it's based on an IP address or it’s based on a flow. Taking advantage of reputation we can insert those IP addresses, detected based on flow, that are doing something anomalous.

It could be that they’re beaconing out, spreading a worm. If they look like they’re causing concerns with a high degree of accuracy, then we can put that into the reputation and take advantage of moving blocks.

So reputation is a self-deploying feature. You insert an IP address into it and it can self-update. We haven’t taken the automated step yet, although that’s in the plan. Today, it’s a manual process for us, but ideally, through application programming interfaces (APIs), we can automate all that. It works in a lab, but we haven’t deployed it on our production that way.

Gardner: Clearly HP is a good example of a large enterprise, one of the largest in the world, with global presence, with a lot of technology, a lot of intellectual property, and therefore a lot to protect. Let’s look at how you actually approached protecting the HP network.

We wanted to prevent mal traffic, mal-formed traffic, malware -- any traffic with the mal intent of reaching the data center.

What’s the vision, if you will, for HP's Global Cyber Security, when it comes to these newer approaches? Do you have an overarching vision that then you can implement? How do we begin to think about chunking out the problem in order to then solve it effectively?

O’Shea: You want to be able to detect, block, and prevent as an overarching strategy. We also wanted to take advantage of inserting a giant filter inline on all data that’s going into the data center. We wanted to prevent mal traffic, mal-formed traffic, malware -- any traffic with the "mal" intent of reaching the data center.

So why make that an application decision to block and rely on host-level defenses, when we have the opportunity to do it at the network? So it made the network more hygienically clean, blocking traffic that you don’t want to see.

We wrapped it around the data center, so all traffic going into our data centers goes through that type of filter. [Learn more about intrusion prevention systems.]

Gardner: You’ve mentioned a few HP products: TippingPoint and ArcSight, for example, but this is a larger ecosystem approach and play. Tell us a little bit about partnerships, other technologies, and even the partnerships for implementation, not just the technology, but the process and methodologies as well.

Key to deployment

O’Shea: That was key to our deployment, because it is an inline technology and you are going inline in the network. You’re changing flows, where it could be mal traffic, but yet maybe a researcher is trying to do something. So we need to have the ability to have that level of partnership with the network team. They have to see it. They have to understand what it is. It has to be manageable.

When we deployed it, we looked at what could go wrong and we designed around that. What could go wrong? A device failed. So we have an N+1 type installation. If a single device fails, we’re not down, we are not blocking traffic. We have the ability to handle the capacity of our network, which grows, and we are growing, and so it has to be built for the now and the future. It has to be manageable.

It has to be able to be understood by “first responders,” the people that get called first. Everybody blames the network first, and then it's the application afterward. So the network team gets pulled in on many calls, at all types of hours, and they have to be able to get that view.

That was key to get them broad-based training, so that the technology was there. Get a process integrated into how you’re going to handle updates and how you’re going to add beyond what TippingPoint recommended. TippingPoint makes a recommendation on profiles and new settings. If we take that, do we want to add other things? So we have to have a global cyber-security view and a global cyber-security input and have that all vetted.

The application team had to be onboard and aware, so that everybody understands. Finally, because we were going into a very large installed network that was handling a lot of different types of traffic, we brought in TippingPoint Professional Services and had everything looked at, re-looked at, and signed off on, so that what we’re doing is a best practice. We looked at it from multiple angles and took a lot of things into consideration.

We proxy the events. That gives us the ability to have multiple ArcSight instances and also to evolve.

Gardner: Now, we have different groups of people that need to work in concert to a larger degree than in the past. We have application folks, network folks, outside service providers, and network providers. It seems that we are asking for a complete view of security, which means people need to be coordinated and cooperative in ways that they hadn’t had to be before.

Is there something about TippingPoint and ArcSight that provides data, views, and analytics in such a way that it's easier for these groups to work together in ways that they hadn’t before? We know that they have to work together, but is there something about the technology that helps them work together, or gives them common views or inputs that grease the skids to collaboration?

O’Shea: One of the nice things about the way the TippingPoint events occur is that you have a choice. You can send them from an individual IDS units themselves or you can proxy them from the management console. Again, the ability to manage was critical to us, so we chose to do it from the console.

We proxy the events. That gives us the ability to have multiple ArcSight instances and also to evolve. ArcSight evolves. When they’re changing, evolving, and growing, and they want to bring up a new collector, we’re able to send very rapidly to the new collector.

ArcSight pulls in firewall logs. You can get proxy events and events from antivirus. You can pull in that whole view and get a bigger picture at the ArcSight console. The TippingPoint view is of what’s happening from the inline TippingPoint and what's traversing it. Then, the ArcSight view adds a lot of depth to that.

Very flexible

So it gives a very broad picture, but from the TippingPoint view, we’re very flexible and able to add and stay in step with ArcSight growth quickly. It's kind of a concert. That includes sending events on different ports. You’re not restricted to one port. If you want to create a secure port or a unique port for your events to go on to ArcSight, you have that ability.

Gardner: We’ve heard, of course, how important real-time reaction is, and even gaining insights to be able to anticipate and be proactive. What is it that you learned through this process that allowed you to make that latency reduced or eliminated so that the amount of time that things go on is cut. I’ve heard that a lot of times you can't prevent intrusion, but you can prevent the damage of intrusion. So how does it work in terms of this low latency time element?

O’Shea: With TippingPoint, you get to see when an exploit is triggered, TippingPoint has a concept of Zero Days and it has a concept of Reputation. Reputation is an ongoing change, and Zero Day is a deployment of a profile. Think of Reputation as a constant updating of signatures as sites change and how the industry is recognizing them. So that gives you an ability to have a view of a site that people frequented and may now be compromised. You have that ability to see that because the Reputation of the site changed.

With TippingPoint being a block technology, you have the low latency. The latency is being detected and blocked, but now, when you pull it back into ArcSight, you have the ability to see a holistic view. We’re seeing these events or something that looks similar. The network-based anomaly detection is reporting some strange things happening, or you have some antivirus things that are reporting.

That’s a different type of reaction. You can react and deploy and say that you want to take action against whatever it is you are seeing. Maybe you need to put up a new firewall block to alleviate something.

That’s a different type of reaction. You can react and deploy and say that you want to take action against whatever it is you are seeing.

Or on the other hand, if TippingPoint is not seeing it, maybe you have the opportunity to activate this new signature more rapidly and deploy new profile. This is something new, and you can take action right away.

Gardner: Jim, let's talk a bit about what you get when you do this correctly. So using HP’s example, what were some of the paybacks, both in technical terms, maybe metrics of success technically, but then also business results? What happens when you can deploy these systems, develop those partnerships, and get cooperation? How can we measure what we have done here?

O’Shea: One of the things that we did wrong in our deployment is that we didn’t have a baseline of what is mal or what is bad. So, as it was a moving deployment, we don’t have hard and fast metrics of a before and after view. But again, you don’t know what's bad until you start trying to detect it. It might not have been for us to even take that type of view.

We deployed TippingPoint. After the deployment we’ve had some DoS attacks against us, and they have been blocked and deflected. We’ve had some other events that we have been able to block and defend rapidly. [Learn more about intrusion prevention systems.]

If you think back historically of how we dealt with them, those were kind of a Whac-A-Mole-type of defenses. Something happened, and you reacted. So I guess the metric would be that we’re not as reactionary, but do we have hard metrics to prove that? I don’t have those.

How much volume?

Gardner: We can appreciate the scale of what the systems are capable of. Do we have a number of events detected or that sort of thing, blocks per month, any sense of how much volume we can handle?

O’Shea: We took a month’s sample. I’m trying to recall the exact number, but it was 100 million events in one month that were detected as mal events. That’s including Internet-facing events. That’s why the volume is high, but it was 100 million events that were automatically blocked and that were flagged as mal events.

The Professional Services teams have been able to deploy in a very large network and have worked with the requirements that a large enterprise has.

Gardner: How do you now take this out to the market? Is there a cyber-security platform? Do you have a services component? You’ve done this internally, but how do you take this out to the market, combining the products, the services, and the methodologies?

O’Shea: I’m not on the product marketing side, but TippingPoint has learned from us and we’ve partnered with them. We’re constantly sharing back with them. So the give-back to TippingPoint, as a product division, is that they can see real traffic, in a real high-volume network, and they can pretest their signatures.

There are active lighthouse-type installs, lighthouse meaning that they’re not actively blocking. They’re just observing, and they are testing their next iteration of software and the next group of profiles. They’re able to do that for themselves, and it's a give back that has worked. What we receive is a better product, and what everybody else receives is a better product.

The Professional Services teams have been able to deploy in a very large network and have worked with the requirements that a large enterprise has. That includes standard deployment, how things are connected and what the drawings are going to look like, as well as how are you going to cable it up.

A large enterprise has different standards than a small business would have, and that was a give back to the Professional Services to be able to deploy it in a large enterprise. It has been a good relationship, and there is always opportunity for improvement, but it certainly has helped.

Current trends

Gardner: Jim, looking to the future a little bit, we know that there’s going to be more and more cloud and hybrid-cloud types of activities. We’re certainly seeing already a huge uptick in mobile device and tablet use on corporate networks. This is also part of the bring-your-own-device (BYOD) trend that we’re seeing.

So should we expect a higher degree of risk and more variables and complication, and what does that portend for the use of these types of technologies going forward? How much gain do you get by getting on the IDS bandwagon sooner rather than later?

O’Shea: BYOD is a new twist on things and it means something different to everybody, because it's an acronym term, but let's take the view of you bringing in a product you buy.

BYOD is a new twist on things and it means something different to everybody, because it's an acronym term.

Somebody is always going to get a new device, they are going to bring in it, they are going to try it out, and they are going to connect it to the corporate network, if they can. And because they are coming from a different environment and they’re not necessarily to corporate standards, they may bring unwanted guests into the network, in terms of malware.

Now, we have the opportunity, because we are inline, to detect and block that right away. Because we are an integrated ecosystem, they will show up as anomalous events. ArcSight and our Cyber Defense Center will be able to see those events. So you get a bigger picture.

Those events can be then translated into removing that node from the network. We have that opportunity to do that. BYOD not only brings your own device, but it also brings things you don’t know that are going to happen, and the only way to block that is prevention and anomalous type detection, and then try to bring it altogether in a bigger picture.

Gardner: Well, great. I’m afraid we will have to leave it there. We’ve been learning about the modern ins and outs of improving enterprise intrusion prevention systems, and we’ve heard about how HP itself has created more of a granular access control benefit amid real-time, yet intelligent, intrusion detection and protection.

I’d like to thank the supporter for this series, HP Software, and remind our audience to carry on the dialogue through the Discover Group on LinkedIn. And of course, a big thank you to our guest, Jim O'Shea, Network Security Architect for HP Cyber Security Strategy and Infrastructure Engagement. Thanks so much, Jim.

O’Shea: Thank you.

Gardner: And lastly, our appreciation goes out to our global audience for joining us once again for this HP Discover Podcast discussion.

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

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: HP.
Learn more about prevention detection.

Transcript of a BriefingsDirect podcast on how the strategy of dealing with malware is shifting from reaction to prevention. Copyright Interarbor Solutions, LLC, 2005-2014. All rights reserved.

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Healthcare Turns to Big Data Analytics Platforms to Gain Insight and Awareness for Improved Patient Outcomes

Transcript of a BriefingsDirect on the need to tap the potential of big data to improve healthcare delivery and how the technology to do that is currently lagging.

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 of IT innovation and how it's making an impact on people’s lives.

Gardner

Once again, we’re focusing on how IT leaders are improving their services to deliver better experiences and payoffs for businesses and end users alike. I’m now joined by our co-host for this sponsored series, Chief Software Evangelist at HP, Paul Muller. Welcome Paul, how are you today?

Paul Muller: Fighting fit, and healthy Dana, yourself?

Gardner: Glad to hear it. I’m doing very well, thanks. We’re going to now examine the impact that big-data technologies and solutions are having on the highly dynamic healthcare industry. We’ll explore how analytics platforms and new healthcare-specific solutions together are offering far greater insight and intelligence into how healthcare providers are managing patient care, cost, and outcomes.

And we’re going to hear firsthand of how these new offerings, announced this week at the HP Discover Conference in Barcelona, are designed specifically to give hospitals and care providers new data-driven advantages as they seek to transform their organizations.

With that, please join me in welcoming our guest, Patrick Kelly, Senior Practice Manager at the Avnet Services Healthcare Practice. Welcome, Patrick. [Disclosure: HP is a sponsor of BriefingsDirect podcasts.]

Patrick Kelly: Thank you, Dana. It's great to be with both you and Paul.

Gardner: Just to put this into some perspective, Paul, as you travel the globe, as I know you do, how closely are you seeing an intersection between big data and the need for analytics in healthcare. Is this a US-specific drive, or is this something that’s sweeping many markets as well?

Muller: It's undoubtedly a global trend, Dana. One statistic that sticks in my mind is that in 2012 what was estimated was approximately 500 petabytes of digital healthcare data across the globe. That’s expected to reach 25,000 petabytes by the year 2020. So, that’s a 50-times increase in the amount of digital healthcare data that we expect to be retaining.

Muller

The reasons for that is simply that having better data helps us drive better healthcare outcomes. And we can do it in a number of different ways. We move to what we call most evidence-based medicines, rather than subjecting people to a battery of tests, or following a script, if you like.

The test or the activities that are undertaken with each individual are more clearly tailored, based on the symptoms that they’re presenting with, and data helps us make some of those decisions.

Basic medical research

The other element of it is that we’re now starting to bring in more people and engage more people in basic medical research. For example, in the US, the Veterans Administration has a voluntary program that’s using blood sample and health information from various military veterans. Over 150,000 have enrolled to help give us a better understanding of healthcare.

We’ve had similar programs in Iceland and other countries where we were using long-term healthcare and statistical data from the population to help us spot and address healthcare challenges before they become real problems.

The other, of course, is how we better manage healthcare data. A lot of our listeners, I’m sure, live in countries where electronic healthcare records (EHR) are a hot topic. Either there is a project under way or you may already have them, but that whole process of establishing them and making sure that those records are interchangeable is absolutely critical.

Then, of course, we have the opportunity of utilizing publicly available data. We’ve all heard of Google being utilized to identify the outbreaks of flu in various countries based on the frequency of which people search for flu symptoms.

There’s a huge array of data that you need to bring together, in addition to just thinking about the size of it.

So, there’s definitely a huge number of opportunities coming from data. The challenge that we’ll find so frequently is that when we talk about big data, it's critical not just to talk about the size of the data we collect, but the variety of data. You’ve got things like structured EHR. You have unstructured clinical notes. If you’ve ever seen a doctor’s scribble, you know what I’m talking about.

You have medical imaging data, genetic data, and epidemiological data. There’s a huge array of data that you need to bring together, in addition to just thinking what is the size of it. Of course, overarching all of these are the regulatory and privacy issues that we have to deal with. It's a rich and fascinating topic.

Gardner: Patrick Kelly, tell us a little bit about what you see as the driving need technically to get a handle on this vast ocean of healthcare data and the huge potential for making good use of it? 

Kelly: All the points Paul brought up were spot-on. It really is a problem of how to deal with such a deluge of data. Also, there’s a great change that’s being undertaken because of the Affordable Care Act (ACA) legislation and that’s impacting not only the business model, but also the need to switch to an electronic medical record.

Capturing data

From an EHR perspective to date, IT is focused on capturing that data. They take and then transpose what’s on a medical record into an electronic format. Unfortunately, where we’ve fallen short in helping the business is taking that data that’s captured and making it useful and meaningful in analytics and helping the business to gain visibility and be able to pivot and change as the need to change the business model is being brought to bear on the industry.

Gardner: For those of our audience who are not familiar with Avnet, please describe your organization. You’ve been involved with a number of different activities, but healthcare seems to be pretty prominent in the group now. [Learn more about Avnet's Healthcare Analytics Practice.]

Kelly

Kelly: Avnet has made a pretty significant investment over the last 24 months to bolster the services side of the world. We’ve brought numbers up to around 2,000 new personnel on board to focus on everything in the ecosystem, from -- as we’re talking about today -- healthcare all the way up to hardware, educational services, and supporting partners like HP. We happen to be HP’s largest enterprise distributor. We also have a number of critical channel partners.

In the last eight months, we came together and brought on board a number of individuals who have deep expertise in healthcare and security. They work to focus on building out healthcare practice that not only provides services, but is also developing kind of a healthcare analytics platform.

Gardner: Paul Muller, you can’t buy healthcare analytics in a box. This is really a team sport; an ecosystem approach. Tell me a little bit about what Avnet is, how important they are in HP’s role, and, of course, there are going to be more players as well.

What Avnet brings to the table is the understanding of the HAVEn technology, combined with deep expertise in the area of healthcare and analytics.

Muller: The listeners would have heard from the HP Discover announcements over the last couple of days that Avnet and HP have come together around what we call the HAVEn platform. HAVEn as we might have talked about previously on the show stands for Hadoop, Autonomy, Vertica, Enterprise Security, with the “n” being any number of apps. [Learn more about the HAVEn platform.]

The "n" or any numbers of apps is really where we work together with our partners to utilize the platform, to build better big-data enabled applications. That’s really the critical capability our partners have.

What Avnet brings to the table is the understanding of the HAVEn technology, combined with deep expertise in the area of healthcare and analytics. Combining that, we've created this fantastic new capability that we’re here to talk about now.

Gardner: Back to you, Patrick. Tell me a bit about what you think are the top problems that need to be solved in order to get healthcare information and analytics to the right people in a speedy fashion. What are our hurdles to overcome here?

Kelly: If we pull back the covers and look at some of the problems or challenges around advancing analytics and modernization into healthcare, it’s really in a couple of areas. One of them is that it's a pretty big cultural change.

Significant load

Right now, we have an overtaxed IT department that’s struggling to bring electronic medical records online and to also deal with a lot of different compliance things around ICD-10 and still meet meaningful use. So, that’s a pretty significant load on those guys.

Now, they’re being asked to look at delivering information to the business side of the world. And right now, there's not a good understanding, from an enterprise-wide view, of how to use analytics in healthcare very well.

So, part of the challenge is governance and strategy and looking at an enterprise-wide road map to how you get there. From a technology perspective, there’s a whole problem around industry readiness. There are a lot of legacy systems floating around that can range from 30-year-old mainframes up to more modern systems. So there’s a great deal of work that has to go around modernizing the systems and then tying them together. That all leads to problems with data logistics and fragmentation and really just equals cost and complexity.

One of the traditional approaches that other industries have followed with enterprise data warehouses and traditional extract, transform, load (ETL) approaches are just too costly, too slow, and too difficult for healthcare system to leverage. Finally, there are a lot of challenges in the process of the workflow.

Muller: These sound conceptual at a high level, but the impact on patient outcomes is pretty dramatic. One statistic that sticks in my head is that hospitalizations in the U.S. are estimated to account for about 30 percent of the trillions of dollars in annual cost of healthcare, with around 20 percent of all hospital admissions occurring within 30 days of a previous discharge.

Better utilizing big-data technology can have a very real impact on the healthcare outcomes of your loved ones.

In other words, we’re potentially letting people go without having completely resolved their issues. Better utilizing big-data technology can have a very real impact, for example, on the healthcare outcomes of your loved ones. Any other thoughts around that, Patrick?

Kelly: Paul, you hit a really critical note around re-admissions, something that, as you mentioned, has a real impact on the outcomes of patients. It's also a cost driver. Reimbursement rates are being reduced because of failure. Hospitals would be able to address the shortfalls either in education or follow-up care that end up landing patients back in the ER.

You’re dead on with re-admissions, and from a big-data perspective, there are two stages to look at. There’s a retrospective look that is a challenge even though it's not a traditional big-data challenge. There’s still lot of data and a lot of elements to look into just to identify patients that have been readmitted and track those.

But the more exciting and interesting part to this is the predictive, looking forward and seeing the patient’s conditions, their co-morbidity, how sick they are, what kind of treatment they receive, what kind of education they received and the follow-up care as well as how they behave in the outside world. Then, it’s bringing all that together and building a model to be able to determine whether this person is at risk to readmit. If so, how do we target care to them to help reduce that risk. 

Gardner: We certainly have some technology issues to resolve and some cultural shifts to make, but what are the goals in the medical field, in the provider organizations themselves? I’m thinking of such things as cutting cost, but more that, things about treatments and experience and even gaining perhaps a holistic view of a patient, regardless of where they are in the spectrum.

Waste in the system

Muller: You kind of hit it there, Dana, with the cutting cost. I was reading a report today, and it was kind of shocking. There is a tremendous amount of waste in the system, as we know. It said that in the US, $600 billion, 17.6 percent of the nation’s GDP, that is focused on healthcare is potentially being misspent. A lot of that is due to unnecessary procedures and tests, as well as operational inefficiency.

From a provider perspective, it's getting a handle on those unnecessary procedures. I’ll give you an example. There’s been an increase in the last decade of elective deliveries, where someone comes in and says that they want to have an early delivery for whatever reason. The impact, unfortunately, is an additional time in the neo-natal intensive care unit (NICU) for the baby.

It drives up a lot of cost and is dangerous for both the mother and child. So, getting a handle on where the waste is within their four walls, whether it’s operationally, unnecessary procedures, or tests and being able to apply Lean Six Sigma, and some of these process is necessary to help reduce that.

Then, you mentioned treatments and how to improve outcomes. Another shocking statistic is that medical errors are the third leading cause of death in the US. In addition to that, employers end up paying almost $40,000 every time someone receives a surgical site infection.

From a provider perspective, it's getting a handle on those unnecessary procedures.

Those medical errors can be everything from a sponge left in a patient, to a mis-dose of a medication, to an infection. Those all lead to a lot of unnecessary death as well as driving up cost not only for the hospital but for the payers of the insurance. These are areas that they will get visibility into to understand where variation is happening and eliminate that.

Finally, a new aspect is customer experience. Somehow, reimbursements are going to be tied to -- and this is new for the medical field -- how I as a patient enjoy, for lack of better term, my experience as the hospital or with my provider, and how engaged I had become in my own care. Those are critical measures that analytics are going to help provide.

Gardner: We have a big chore ahead of us with the need for changing the way that IT is conducted in these organizations. Obviously, what you’ve just described are different ways of doing medicine based on data and analysis, but we also have this change in the way that medicine is being delivered in the US. You mentioned the ACA. We’re moving from a paid by procedure basis much more to a paid by the outcomes basis. This shifts things and transforms things tremendously too.

Now that we have a sense of this massive challenge ahead of us, what are organizations like Avnet and providers like HP with HAVEn doing that will help us start to get a handle on this? Give us a sense, Patrick, of what you are bring into the market with the announcement in Barcelona.

Kelly: As difficult as it is to reduce complexity in any of these analytic engagements, it's very costly and time consuming to integrate any new system into a hospital. One of the key things is to be able to reduce that time to value from a system that you introduce into the hospital and use to target very specific analytical challenges.

From Avnet’s perspective, we’re bringing a healthcare platform that we’re developing around the HAVEn stack, leveraging some of those great powerful technologies like Vertica and Hadoop, and using those to try to simplify the integration task at the hospitals.

Standardized inputs

We’re building inputs from HL7, which is just a common data format within the hospital, trying to build some standardized inputs from other clinical systems, in order to reduce the heavy lift of integrating a new analytics package in the environment.

In addition, we’re looking to build a unified view of the patient’s data. We want to extend that beyond the walls of the hospital and build a unified platform. The idea is to put a number of different tools and modular analytics on top of that to have some very quick wins, targeted things like we've already talked about, from readmission all the way into some blocking and tackling operational work. It will be everything from patient flow to understanding capacity management.

It will bring a platform that accelerates the integration and analytics delivery in the organization. In addition, we’re going to wrap that into a number of services that range from early assessment to road map and strategy to help with business integration all the way around continuing to build and support the product with the help system.

The goal is to accelerate delivery around the analytics, get the tools that they need to get visibility into the business, and empower the providers and give them a complete view of the patient.

Gardner: Paul, it’s very impressive when you look at what can be done when an ecosystem comes together. When you look at applications, like what Avnet is delivering, it seems to me they’re also changing the game in terms of who can use these analytics. We’re seeing visualizations and we’re seeing modular approaches like Patrick described. How much of a sea change are we seeing in terms of not just creating better analytics, but getting them to more people, perhaps people had never really had access to this intelligence before.

It’s the immediacy of interaction that is going to make the biggest difference.

Muller: That’s a critical element. It's simple, easy to understand, and visualizations are an important element of it. The other is just simply the ability to turn these sorts of questions around more quickly.

If you think about traditional medical studies and even something as simple as drug development, in the past getting access to the data, being able to have a conversation with the data, has been very difficult, because sourcing it, scrubbing it, correlating it, processing it has taken years.

Even simple queries could take days to run. It’s become more complex and you have to do things like look for correlation across longitudinal records or understanding unstructured clinical notes that have been written by a doctor or, more importantly, by different doctor's. Each of them is writing something similar, but in a different way. Then, there’s the massive volume of information involved. Patrick touched on some of the behavioral aspects or lifestyle choices people make.

The ability to take all of that information at one time and have a conversation, where it's a slice and dice it and interact with it, is another important aspect to the usability and the democratizing access to some of that information. Whether, it would be the researchers or government officials and health care workers looking for example for the potential outbreaks of disease or to plan a better health care system, it’s not just great visualizations that are important. That certainly helps, but it’s the immediacy of interaction that is going to make the biggest difference.

Gardner: Patrick, when you do these basic infrastructure improvements, when you create a different culture to make the data analysis available fast, you start to get towards that predictive, rather then reactive, approach. Do you have some sense or even examples of what good can come of this? Are there some tangible benefits, some soft benefits, to get as a payback. I’m thinking clearly pretty quickly because we probably need to demonstrate value rather soon in this environment?

About visibility

Kelly: Dana, any first step with this is about visibility. It opens the eyes around processes in the organization that are problematic and that can be very basic around things like scheduling in the operating room and utilization of that time to length of stay of patients.

A very a quick win is to understand why your patients seem to be continually having problems and being in the bed longer then they should be. It’s being able, while they're filling those beds, to redirect care, case workers, medical care, and everything necessary to help them get out of the hospital sooner and improve their outcomes.

A lot of times, we've seen a look of surprise when we've shown, here is the patient who has been in for 10 days for a procedure that should have only been a two-day stay, and really giving visibility there. That’s the first step, though very basic.

As we start attacking some of these problems around hospital-based infection, we help the provider make sure that they are covering all their bases and doing kind of the best practices, and eliminating the variation between each physician and care provider, you start seeing some real tangible improvements and outcomes in saving peoples lives.

When you see that from any population be it stroke, re-admissions -- as we talked about earlier -- with heart failure and being able to make sure those patients are avoiding things like pneumonia, you bring visibility.

A challenge for a hospital that has acquired a number of physicians is how to get visibility into those physician practices.

Then, in predictive models and optimizing how the providers and the caregivers are working is really key. There are some quick wins, and that’s why traditionally we built these master repositories that we then built reports on top of. It’s a year and a half to delivery for any value, and we’re looking to focus on very specific use cases and trying to tackle them very quickly in a 90- to 120-day period.

Gardner: Patrick, do you have any early-adopter examples you can provide for us, so that we have a sense of what types of organizations are putting this into place, what they’ve done first, and what have been the outcomes?

Kelly: We're partnering with a 12-hospital health care system, dealing with again some blocking and tackling around understanding better how to utilize their physician network.

A challenge for a hospital that has acquired a number of physicians is how to get visibility into those physician practices. How do you understand the kinds of things we've talked about -- cost, patient experience, outcomes -- out in the wild, in the primary care offices, and in the specialty offices? That data has traditionally just been completely segmented from the hospital systems.

The challenge is building tools that are going to be leveraged by the physician themselves, as well as the hospitals and at an executive level, and utilizing that information to help optimize how those practices are running. It’s kind of a basic problem for most businesses, but it's something very real for hospitals to deal with.

Massive opportunity

Gardner: Paul Muller, this seems to be a massive opportunity, something that will be going on from many years with HP, Vertica, and HAVEn. Trillions of dollars have been spent on ways that can give us better patient experiences, higher health rates, lower mortality rates. So, it’s a win, win, win, right? The hospitals win, the insurers win, the governments win, the patients win, the doctors win. What sort of opportunity is this and how is HP going at it?

Muller: You’ve absolutely nailed the assessment there. It’s an all-around benefit. A healthy society is a healthy economy. That’s pretty crystal clear to everybody. The opportunity for HP and our partners is to help enable that by putting the right data at the finger tips of the people with the potential to generate life saving or lifestyle improving insights. That could be developing a new drug, improving the impatient experience, or helping us identify longer-term issues like genetic or other sorts of congenital diseases.

From our perspective, it’s about providing the underlying platform technology, HAVEn, as the big data platform. The great partner ecosystem that we've developed in Avnet is a wonderful example of an organization that’s taken the powerful platform and very quickly turned that into something that can help not only save money, but as we just talked about, save lives which I think is fantastic.

Gardner: Patrick, as we wrap up, we can certainly see many ways in which these technologies in this analysis can be used immediately for some very significant benefits. But I’m thinking that it also puts in place a tremendous foundation for what we know is coming in the future -- more sensors, more information coming from the patients, more telemetry, so that it's coming remotely, maybe from their bodies, while they are out of the hospital.

In this industry, it’s very life and death, versus it's just purely a financial incentive.

We know that mobile devices are becoming more and more common, not only in patient environments, but in the hospitals and the care-provider organizations. We know the cloud and hybrid cloud services are becoming available and can distribute this data and integrate it across so many more types of processes.

It seems to me that you not only get a benefit from getting to a big-data analysis capability now, but it puts you in a position to be ready when we have more types of data -- more speed, more end points, and, therefore, more requirements for what your infrastructure, whether on premises or in a cloud, can do. Tell me a little bit about what you think the Avnet and HP Solution does for setting you up for these future trend? 

Kelly: At this point, technology today is just not where it needs to be, especially in healthcare. An EKG spits out 1,000 data points per second. There is no way, at this point, without the right technology, that you can actually deal with that.

If we look to a future where providers do less monitoring, so less vital collection, fewer physicals, and all of that is coming from your mobile device, it's coming from intelligent machines. There really needs to be an infrastructure in place to deal with that.

I spent a lot of time working with Vertica even before Avnet. Vertica, Hadoop, and leveraging economy in the area of unstructured data is a technology that is going to allow the scalability and the growth that’s going to be necessary to leverage the data that we need to make it an asset and much less challenge and allow us to transform healthcare.

The key to that is unlocking this tremendous trove of data. In this industry, as you guys have said, it’s very life and death, versus it's just purely a financial incentive.

Targeting big data

Muller: I might jump in on that as well, Dana. This is an important point that we can’t lose sight of as well. As I said when you and I hosted the previous show, big data is also a big target.

One of the things that every healthcare professional and regulator, every member of the public needs to be mindful of is a large accumulation of sensitive personally identifiable information (PII).

It's not just a governance issue, but it's a question of morals and making sure that we are doing the right thing by the people who are trusting themselves not just with their physical care, but with how they present in society. Medical information can be sensitive when available not just to criminals but even to prospective employers, members of the family, and others.

The other thing we need to be mindful of is we've got to not just collect the big data, but we've got to secure it. We've got to be really mindful of who’s accessing what, when they are accessing, are they appropriately accessing it, and have they done something like taking a copy or moved it else where that could indicate that they have malicious intent.

It's also critical we think about big data in the context of health from a 360-degree perspective.

It's also critical we think about big data in the context of health from a 360-degree perspective.

Kelly: That’s a great point. And to step back a little bit on that, one of the things that brings me a little comfort around that is there are some very clear guidelines in the way of HIPAA around how this data is managed, and we look at it from baking the security into it, in everything from the encryption to the audit ability.

But it’s also training the staff working in these environments and making sure that all of that training is put in place to ensure the safety of that data. One of the things that always leaves me scratching my head is that I can go down the street into the grocery store and buy a bunch of stuff. By the time I get to register, they seem to know more about me than the hospital does when I go to the hospital.

That’s one of the shocking things that make you say you can’t wait until big data gets here. I have a little comfort too, because there are at least laws in place to try to corral that data and make sure everyone is using it correctly.

Gardner: Very good. I’m afraid we’ll have to leave it there. Please join me in thanking our co-host, Paul Muller, Chief Software Evangelist at HP. Thanks so much, Paul.

Muller: Thank you for having me back on the show again, Dana. I really love being here.

Gardner: Of course and also a thank you to the supporter of this series, HP Software. And a reminder to our audience to carry on the dialog with Paul Muller through the Discover group on LinkedIn. We've been having a discussion about how big data and healthcare are intersecting and how there’s a huge opportunity for far greater insight and intelligence into how healthcare providers are managing their patient’s care, the cost and ultimately the outcomes.

And I’d also like to remind you that you can access this, and other episodes of the HP Discover podcast series on iTunes under BriefingsDirect.

And, of course, a big thank you to our guest. We’ve been talking with Patrick Kelly, Senior Practice Manager at the Avnet Services Healthcare Practice. Thanks so much, Patrick.

Kelly: Thank you, guys.

Gardner: This is Dana Gardner, Principal Analyst at Interarbor Solutions, your co-host for this ongoing series. And lastly, a big thank you to our audience for joining this HP Discover Discussion, and reminder to come back next time.

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

Transcript of a BriefingsDirect on the need to tap the potential of big data to improve healthcare delivery and how the technology to do that is currently lagging. Copyright Interarbor Solutions, LLC, 2005-2013. All rights reserved.

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