Good morning and welcome. So thank you very much for joining us here in San Francisco as well as all of you out on the webcast. We appreciate you being here So as many of you may know, we just celebrated the 50th anniversary of Moore's Law and Moore's Law has The rise of the digital service economy has sparked tremendous innovation in new services And it's also enabled the improvement of existing services. It made those services more efficient, so driving bottom line growth value.
And data is the new currency. It's the currency of the digital world. And it's so amazing, in fact, So please grab your Data is the New Bacon T shirt on your way out. But we talk about Data is the New Bacon because adopting Big Data Analytics Solutions allow businesses to have a true competitive advantage. So it's also though clear that All data is not equal.
There is a time value on data. The faster you can aggregate and analyze and take action, the greater the results. There isn't an industry or a business that isn't able to benefit from data and data analytics. So Every industry, including one that was founded in 10,000 BC can be transformed with big data analytics,
We have to do all of this without Getting any more cornwinds. In fact, we actually lose farm ground every single year.
Part of the challenge is the complexity because we're trying to warn people
We take data from tons of different sources and bring it all together and analyze it for the grower. The data center is actually enabling us to do
From real time analytics, we're able to generate the earthquake early warning. We look continuously at the data from 100
Our customers are able to see everything happening across their farm in real time, such as harvest data flowing in growing
Our programs are designed to reduce the damage from earthquakes. We can't eliminate it, but we can provide people with enough warning to take some preventive
So as exciting As the proof points of the data analytics are, and there are still clearly hurdles. The first is around trust. So data management for employee, whether it be all the social media solutions or the instrumentation of businesses in support of the Internet of Things. All of this is increasing the challenge of data security, both confidentiality of the data as well as integrity of the data. The second issue is speed.
So the analysis of enterprise data has historically been batch mode. So For instance, you're analyzing your customer bookings every day or so as the quarter comes to an end. But those Systems don't hold up in the new era of real time analysis and real time action. And the third challenge is scale. Want the data set to grow and become much, much larger because the larger the amount of data, the greater the insight that you're going to achieve.
So The unfortunate is that those early adopters of big data analytics solutions, only 27% of them will It starts by deploying a solution that supports the complementary architecture of scale So meeting the needs of speed with the needs for massive data sets, scale out, Adding more and more capacity to deliver greater performance using a distributed processing approach. So for Hadoop is a great example of a distributed data storage processing solution that can process large bulk jobs with computing occurring at And then scale up, so adding ever increasing resources to a single node. So achieving maximum performance through higher core count, And so to deliver a step function improvement in the performance delivered in scale up applications, we are introducing today the new Xeon E7D3. The performance gain of the new processor is significant, especially for those applications that can take advantage of the new instructions, and I'll talk about those new instructions in a minute. Thanks to Moore's Law.
We're able to pack now 18 high performing cores onto a single die. There's 2 threads per core. We support 2 higher and higher levels of both security and reliability consistent with what you would expect for a mission critical solution. So for data confidentiality, our encryption instructions, which are AES and I, we've improved those in this So it makes now IT now can insist upon data being encrypted both in transit and at rest because the performance of the To ensure data integrity, we've increased The highly computationally intensive process of creating hash value for data authentication. So we've increased the performance up to 2x We've done that through new instructions around ABX2 as well as adding additional a 4th AOU solution.
And then for reliability, we have numerous reliability solutions that we've added into this processor generation, enhance memory check architecture recovery. So we've added more information that gets logged that gives information about what actually generated the error, increasing the probability of recovery. We've added accurate range memory mirroring, so you have finer granularity of the memory that's going to be mirrored, so you get better utilization out of this out of this total memory capacity. We added a fair rank behind the memory controller. So again, increasing the probability of uptime.
And we've also added parity checking for parity on address and command lines as well. So again, increasing systems uptime. Now a new feature that we're We're very excited about is new instructions called TSX. And TSX provides higher performing for concurrent software with a lower software investment required. So what it does is it moves the effort of optimizing memory locking, The first thing that an application thread does when it goes out to access data is to issue a lock.
And that lock will likely protect a large range of data because the specific thread doesn't know which byte it's actually going to require. So that lock then placed on the memory block means that all other threads are going to have to sit idle waiting for that block to be removed. So waiting in a business critical application is obviously a bad thing to do. And if you were to ask any Application developer, he or she will tell you that optimizing thread synchronization is the most complex portion of developing an application. And we actually worked with 1 large enterprise software vendor.
We worked with their application development team on that Locking algorithm and eventually they gave up. And we're talking about threads, 60 threads and more trying to manage The memory space becomes very, very complex. So what we've done is added the new TSX instructions. So this now The hardware will automatically determine what the threads are actually accessing and it allows Just the blocking of those specific bytes and all other threads can continue without waiting. So you have multiple threads now able to given memory block region memory region simultaneously.
So this capability, we've delivered it into our standard libraries that most application developers are already using today. So it's a very simple seamless way of integrating TSX instructions into the applications. And as we noted, TSX instructions can deliver up to a 6x improvement in performance on multi threaded application. So it truly is a breakthrough in managing parallel workloads, not only because the performance gains are so outstanding, but because And so we are proud to announce that with The new Xeon E73, we have 20 new world records. And we want to thank obviously our customers for delivering the innovation that results in those breakthroughs.
They represent a wide variety of applications from database to virtualization to business processing to name just a few. You have obviously over time grown to expect that we have industry leading performance on 4 sockets with the Xeon E7 Processor, but we're So very proud to say that with the new GNE 73, we have taken the leadership position at 8 Socket as well, taken that leadership from Power 8. So we now have leading performance on 2 of the most standard industry benchmarks, spec intrate and specfprate, and those So I thought it would be helpful to show you a demonstration of Lisa is the General Manager of the Data Center Marketing Organization. So, Lisa, hi, Lisa. How are you?
So, tell us about We've applied E73 to energy. Sure.
Thank you for having me. So I want to start first by showing you what we have here. Over here To my right, we have a new one of our 4 socket servers with running our 18 cores Xeon E7v3 Processor, also includes Intel high performing SSDs as well as our 40 gigabit network adapters. So, just looking over here, I've got the SaaS analytics dashboard running off this mobile workstation here. And I wanted to start showing you 3 things today.
First, I think it's fun to look at a little raw performance here. So this
So just remain anonymous. But this is real data from an energy company.
It is real data. And what we're What we're showing is 100x the data in the same amount of time. So if you start thinking about What you're now capable of and the granularity that you can start doing analytics and prediction when you can in your same amount This is billions of rows being crunched in just that short little time zone. So, we love the world We love seeing stuff like this, but I think it's best if we can translate it into business value and customers Okay. This is
So this is historical. On the old system, once a day, they ping all of those meters to decide how much capacity they're going to need, So then the yellow line shows now
that they have this new capability, they can ping every 15 minutes and still combine Now that they can analyze that exponential amount of data. And what does this do for them from a business perspective is this allows them to bring their daily So they're not producing to the top line, they're producing to what they actually need. And as you guys know, energy is not something that's able to
So they're seeing $9,000,000 operational savings per year, thanks to the new system and the SaaS analytics that runs on top
of it. Yes. They're both 9% The 38,000 smart meters, but also starting to look how they look across the cities they support and The transformers that they transformer failure is that for multiple reasons. But One of the things they did was take their 38,000 meters of data, they added an information about actual load on the transformers, They added age and model of the transformers, able to combine that including predicted usage of the transformers and look and see what the What they found was that they were able to predict 96% of their transformer failures before they happen. So again, Efficiency, but in this case, even more importantly, is user experience.
Nobody's
So, in addition to Yes. So they can put
this into production now and change their own maintenance costs, their own workforce loading as well as again drive up that user satisfaction, grabbing always on power delivery. Now that's a machine critical type of usage. So this is what I wanted to show you today about how we have a utility customer using the new system.
Great. Thank you so much. Thank you for that wonderful real world use case that we can all relate to the value proposition delivered. So after 16 years of our investment in mission critical performance in our Xeon product line. We have achieved a significant industry leadership position versus risk alternatives.
The remaining risk footprint is small and declining at less than 2% of all servers shipped last So when IT is going to make a decision on deploying an infrastructure solution, the decision is based on how And so when you compare the Xeon E7 B3 against iGAM's Power 8 at an 8 We deliver 10x better performance per dollar, and we deliver a staggering 85% lower total cost of operation. And so as we've seen over time, this is the true value of an open standards based architecture. And you can see that value then across a full range of real world applications with SaaS, as Lisa just showed, a 72% performance improvement with the new Xeon E73, so customers can run more complex analysis across a much broader dataset. In Healthcare, the Bio Imaging, a solution for 2 d and 3 d medical image viewing, allows the concurrency of the solution to go from 12 users to 20 with a 66% performance improvement. Tufco is an area of rapid adoption of Intel architecture, starting with the big back end enterprise applications.
Hidic And with the Xeon E73, they can support more customer transactions per minute, they can deliver quicker customer service and Increase in performance of their solution with the new beyond, thanks to the key acceptance instructions that Hana has optimized against as well as the general performance that we deliver with the new Xeon 7 V3. So when I'd say that big data and real time analytics can benefit all businesses and all industries, I do mean it, including a product that we made and it may sound as simple as paint. So those that adopt Real Data Analytics Solutions have a competitive advantage and Nippon, which is the largest paint manufacturer in Asia Pacific, Data Analytics Solutions and the impact that it's had on Nippon is our Chief Business Strategy Officer, Tony Hsu. So Tony, come on out. Thank you for joining us.
Hi. Welcome,
welcome. Good morning,
everyone. Good
morning, everyone. Yes,
thank you.
So you'll have to start by telling us the About Nippon and the fundamental scale of the corporation that you folks run.
Certainly. Nippon Ping is the largest paint manufacturer in Asia. In China alone, for example, we have 28 factories, 1700 distributors, 3,500 retail outlets, 8,000 suppliers and 250,000,000 customers to serve. We're also a company who invest heavily in IT infrastructure because we believe that's the backbone of our business operation. On top of that, we are also believing very much in eco friendly.
So, we use the eco friendly materials to produce our paint to make sure our product is environmental friendly as well as user friendly. Yes.
It's a very impressive outcome. It's 2 HANA over 3 years ago. So you must have had some real specific product tooling to solve or opportunities you were looking to see, maybe you could tell us about What drove you to deploy a HANA solution?
We did. The first one we have was we
were trying to reduce our to be generated. So we just sit there and watch computer turn and turn and turn. But with HANA and Intel's We're able to reduce that processing time from 5 days down to just 4 hours. So that means our management team can make their business decisions On the 1st day of the month, that means a 16% improvement of efficiency and productivity for our entire company.
Yes, real business results, top line and bottom line value. So that's a great example. And I think you had another compelling example for deploying a solution you have to tell us about.
The other one is about using big data to improve customer In China, e commerce is growing very quickly. And consumer in China nowadays, I think, they're somewhat spoiled Because when they place the order online, they will expect to receive their delivery in 3 days. So China being
Yes,
exactly. So for us, that is a 1,000 fold increase in the sales order that comes in, which we have to fulfill quickly. How do we meet that challenge? So again, using SAP HANA's technology based on Intel's E7 processor plus the HP DL580 server, we're able to crunch 4 big database that we have. So the first one is our sales database, which we collect from our 2,500 retail outlets, plus our historical sales data.
The second is our e commerce sales data. The third is our supplier data because we have to make sure that our 8,000 suppliers have the right material to supply to us, so we can make the product. And the quarter one will be the data which we purchased from Taobao because we realized that in the executive process, Consumers are more likely to buy plumbing or electrical products in the beginning and tend to be the last product that they purchase. So for example, if we identify someone who is buying a faucet, then that person is likely to buy some paint sometime down the road.
Isn't that clever? You're buying data from Taobao about people that are buying electrical equipment or something as simple as possible because you then assume they must be On the move for purchasing tanks sometime.
Yes. Exactly.
Very great predictive analytics in the paint manufacturing industry.
Yes. So we're able to generate what we call a demand map, tell us where and where what consumer may buy sometime down the road. And so we can pre produce our product, Please ship to our 1700 distributors warehouse. And the end result is that we're able to deliver 30% of our order on the 1st day, 60% order on the 2nd day and 98% of the order on the 3rd day.
Wow. Who wouldn't want their e commerce purchase to be delivered In a single day, I can't put 30% of your purchases as well.
And for that kind of achievement, we were rewarded by Taobao as the best supplier of the year.
In which regions do you forward produce the right color of paint even? I mean, the complexity is very, very impressive. You guys have done an amazing job in applying So as I mentioned, it's the combination of the complementary architectures scale out and scale up that's needed. And we see IT organizations implementing both architectures and increasingly connecting those architectures to provide both the scale and the speed needed as the datasets grow as we saw with that Nippon Pink. So you have Hadoop running on The Xeon E5 to deliver the data scale, so creating a data hub where high volumes of varying types of data can be managed.
And then the enterprise analytics software like SAP HANA or SaaS or Oracle running on Xeon E7v3 So the scale up solutions and pulling the hot or working data set from Hadoop into main memory for real time analytics. So it is the power of the integrated solution that is that compelled us to partner with Cloudera, the industry leader in Hadoop, a partnership that is now 1 year old. And the objective of that partnership was three So number 1, we wanted to work with Cloudera to provide enterprise ready, enterprise class solutions that are taking advantage of all of the unique features that we embed in our processors and products. For instance, we have already demonstrated a 2.5x increase in performance of data encryption by taking Hadoop and optimizing it to use our encryption instructions AES and I. The second objective is to accelerate the innovation in the open source community around big data analytics.
So doing things like providing and libraries as well as directly contributing to open source with the Intel engineers and the Cloudera engineers. And then the third is to drive development of a big healthy ecosystem around big data analytics, so making it much easier for enterprise IT So I'm really happy to have with us here today the CEO of Cloudera, Tom Reilly, and he'll talk about some of the momentum that we've seen in the past year since It's been a year. The year has flown by, but it has been a year working together. So you have to tell us what all we've accomplished over that year?
All right. So when we started this partnership a A year ago, I was really, really excited. A year in, I'm even more enthralled about what this partnership is going to deliver. It's amazing when you bring together hardware engineers and software developers to design systems that are optimized to improve performance. We did a lot of work in Taking the best of Intel's distribution for Hadoop and combining with Cloudera's.
We now have the number one adopted distribution of Hadoop on the planet with those capabilities. Four releases
in 1 year, that's pretty darn good. And the performance delivered in those releases is impressive.
The Performance is very impressive. One of the things I'm most excited about and you mentioned is around encryption. So Intel, within the 1st 6 months, Intel taught how to take advantage of encryption in the silicon. And so now when data lands into Hadoop, we actually encrypt it in the hardware, which has tremendous And we are now advocating that our enterprise data hub is the safest place for data in an enterprise. A year ago, we could not make that claim.
In fact, Mastercard has not only certified our enterprise data hub as PCI compliance. They're now taking it to market to their retailers and financial partners, asking to invest where they want credit card data And finally, we've done some great work with your help with your customers, the OEM building pre engineered systems optimized for specific analytic workloads. So now we have appliances in market That are designed and optimized for doing Spark workloads in memory analytics.
That exactly goes to our objective of building out an ecosystem to make it
Yes. And with your help on the product side of things, It's really accelerated our business results. I'd love just to share a few things that have happened to our company. We're just 6 years old this past year, going into our 7th year. We saw that our customer adoption in large enterprises grow very fast and now we have more than 550 large enterprises with big data projects, tremendous growth there.
Now, to help our business, in our 6th year of operations, we achieved $100,000,000 in revenue. Now Intel is big, dollars 100,000,000 is not very big.
Dollars 100,000,000 is huge.
It turns out enterprise software and subscription enterprise software and open source, we're one of the fastest growing companies in history of enterprise software. Our software revenue grew 100% year over year, And we're expecting that to actually accelerate in years out as these projects go into production.
The results are crazy. And you guys have grown Both organically, but you've also made some exciting acquisitions to help build that capacity faster?
Yes. So we did 3 acquisitions this past year. We acquired a company called Duvang. When we saw the work with we're doing Intel around encryption, it gives the opportunity to go out and buy a key management company called Duvang. So now we natively offer key management in our platform.
We acquired a company called Datapad, which has tools for developers to make the platform more Approachable and recently acquired a company called Explain. Io. And this is helping us understand SQL workloads and whether they work best in your traditional databases or they'll work
Very nice. Yes, very nice. So obviously, tremendous business growth, tremendous growth, great partnership. And what's most exciting When you actually see the deployment by a customer and you see them benefiting from the solution. I know you have lots of great proof points and compelling customer testimonials and you have someone that you are going to bring out?
Yes, we're very fortunate to have one of our customers here to share their use case with you. I hope one of the things you learned today is we're really focused on the use cases around big data. And this next customer is probably one of the One of our customers we're most proud of and the use cases that are having a tremendous impact every single day. Our customer who is here with us today is Cerner in the medical industry. Our presenter is actually a fellow with Cerner.
I just know Cerner has 20,000 employees and only five So the VP of Engineering and Fellow for Cerner, Mr. David Edwards, is here to share the Cerner use case. Thank you, David. Thank you.
Thank you for coming. Yes. So if you have to tell us a little bit about
Sure. Well, it turns out that Cerner is in the midst of an evolution in which we're expanding Our boundaries beyond the historical focus on electronic medical records. We're on a mission to bring together and analyze all the world's healthcare data with the goal of making systemic improvements, not only to the delivery of healthcare, but also to the health of communities. In order to accomplish that goal, we need to create an enterprise data hub that could provide scale and the data management necessary to handle what is already several petabytes of data. Our cloud environment performs a significant amount of process to both normalize the structure and to standardize the content of the raw data sets we're ingesting.
And then, both transfers that data into variety of data warehouses for analysis by not only our data scientists but also our clients. And so a unique feature of our DataHub is this ability to aggregate data from an unlimited number of data sources. So with the data we've already ingested, We're able to construct a much more complete picture of a person's health as well as the health of population. So one very relevant example here is our ability to more accurately predict the presence of a potentially fatal blood infection called sepsis, and then alert the care providers to take immediate action. In our opinion, the combination of and Cloudera, both being leaders in their respective areas of hardware and software, has been a very positive in strengthening the Hadoop platform.
The ongoing advancements in performance and scalability and management Security have been vitally important in helping Cerner realize our mission of improving the health of communities.
It's an amazing Sorry, David. And maybe just bring it home a little closer to how these new use cases are impacting your clients and their patients.
Absolutely. Well, it turns
out that the capabilities of
our enterprise data hub have allowed us to develop and deploy predictive models that are helping care providers make a much more informed and timely decisions. Our clients are telling us The sepsis management system has already saved hundreds of lives. And on a daily basis, we actively monitor More than 1,000,000 lives across our entire U. S. Client base.
So, that's a very powerful and compelling story. And one that we I strongly feel this particular case only represents the beginning of what can actually be accomplished.
It is extremely compelling. You think about the work of Taking technology and implying it in such a time critical and incredibly serious health issue such as such. So amazing work by by delivering a true open architecture means that the architecture is a standard and that there are many technology innovators that can build differentiated platforms against that standard. And Intel has forever been the standard building block provider to the industry and that's what we continue to do. So we have a broad range of customers and partners that have announced their Xeon E7v3 solutions, Announcing starting today.
That's bad. There we go. So there
are 17 OEMs and TAMs delivering 45 unique systems as well as, as you can see, a wide range And with our new Xeon E7 G3 Processor and the system and software innovation that comes from our partners and customers, We are advancing the state of analytics and we're doing it through increased data security, through increased speed to And with that, oh, look, you have your data is the new bacon shirt. I am so proud of you. A free shirt. A free shirt, yes. The PR always
Ladies and gentlemen, please welcome the CEO of Cloudera, Tom Reilly.
Good morning, again. Thank you for I thought I'd use this moment to take another Look at big data, we'll look at it from an application perspective. I do first want to touch though about this partnership we have with Intel. I am fascinated what can happen with 40 of ours. And I talked earlier about the first very impactful application around encryption, where our It used to be that encryption was a huge performance tax, both human performance, human tax and systems performance.
It was very costly. And therefore, we would isolate sensitive data to specific tables. We'd like to decide what data is sensitive, isolate it and then encrypt it. With the work we've done with Intel, we no longer just encrypt sensitive data. We advocate encrypting all data as if it were sensitive.
And that's one example of software engineers working with hardware engineers. Today, our data scientists who are working on the applications, are actually collaborating with Intel's engineers, looking at the algorithms that are driving applications and finding in the hardware to accelerate the performance yet again. This will have a very exciting roadmap going forward. This work is very important Because as we know, data is exploding. The data volumes are, as the world gets interconnected, growing at astronomical rates.
And the numbers are tremendous. And these use cases are helping us get very local within our cities, understanding where people are, what their buying habits are locally, and it's also connecting the global world. As we go into Mother's Day, My favorite retailer starting 2 years ago is Markel to me. That retailer is in Australia. I purchased the back of my wife 2 years ago on her birthday.
And now I turn to that retailer all the time. And so they're building a relationship with me from Australia. Every single industry is going through a revolution. What I'd like to share with you though is what are the common use cases we're seeing going into production today? How is industry capitalizing on big data.
There's 3 categories of use cases that we're seeing emerge And where the emphasis is. The first is around Customer 360. Do you know your customer, not what they have bought from you, But you know their social how they feel socially or their sentiment. You know more about their family. You know their demographics.
Do you know where they live? Do you know where they work? And can you anticipate their needs and create specific products or specific campaigns to address their needs. The next category of use cases around new data driven products or services. So we heard from Cerner earlier that they've augmented their electronic medical records software with the ability now to remotely monitor patients and anticipate toxic reactions like sepsis.
That's a new data driven service that they're offering to their clients a new revenue stream. And the 3rd area is around risk, protecting data, protecting clients, Managing your risk. I'm going to step through a few of the verticals that we see traction And then dive down into some specific use cases, so you can get a concrete understanding how this is really changing industry. So the Customer 360, very common in the telecommunications industry for them to get their arms around the customers. Those here in the U.
S, we know that we get 1 year or 2 year contracts. And Every time our contacts are coming up for renewal or we're getting a new phone, we're trying to think of do we switch carriers. And customer churn is one of the biggest challenges in the telecommunications industry, and they're using big data to reduce churn. Now, we can imagine looking at call data records, looking at dropped calls, looking at spotty cell coverage that they can anticipate unhappy customers and reach out to you. Having our long contracts, I think of on annual or every other year basis.
We're working with telecom companies overseas on an even greater challenge with tremendous results. Pay with prepaid cards. And so they might buy 3 weeks worth of calling time on a card and then they turn. So they have used cases in Indonesia where it's kiosk where they can buy a new card. Well, now it turns out that kiosks in Indonesia across the 1100 islands, kiosks are actually young boys and girls on bikes who can now ride young boys and girls on bikes who can now ride near to you and replenish your card.
And that's a great big data Great success in the manufacturing industry. So for instance, in heavy machinery, Tractors and big heavy machinery equipment is getting fully instrumented. So that now if you manufacture heavy equipment, you You don't say, hey, I've got the best equipment for the job. You say, I offer a service where I will keep that equipment in production with minimal downtime by remotely monitoring it and providing predictive and proactive maintenance controls, basically outsourcing the maintenance of those devices just by monitoring them. Cloudera ourselves.
We're a manufacturer of software. Our software is deployed in 100 in hundreds of data centers around the world with thousands of clusters and tens of thousands of nodes and servers. And our customers opt in and allow us to monitor the health of those clusters across the globe. But now with this amount of data, we're able to predict and proactively reach out to customers who may have a wrong configuration or may have downtime. And that authority called FINRA, who manages all the exchanges, is actually collecting data every day from 4 1,000 secondurity firms in all the exchanges would amount to $50,000,000,000 event every day.
And they're using our software for trade surveillance, monitoring for bad trades, insider trading. They used to be able to have the sample data sets and guess what they think insider trade may be occurring. And now they're able to look across 50,000,000,000 events every day and use technology to look for trade surveillance. Caesars, The first thing we think of when we think of Caesars is the table on the right. It's where you go to gamble.
Well, it turns out in Vegas, gambling no longer is the largest source of revenue. These resorts have turned into retail malls, high end restaurants, entertainment venues and a lot of resort services, whether spas, Going golfing, playing tennis. And in fact, the demographic of the customer seizures has changed from the Heavy gamblers they want to understand 2 families that they need to really understand who they are. So now their old systems couldn't handle this new type of data, non gambling data. They used to run or called shelf campaigns where they identify The demographics of customers are gamblers and of course one for the whales and how to understand how they recruit more gamblers from around the world.
They've now completely change and have a new approach of understanding how they can make money across all of their properties. And they've completely changed What it means to understand the customer in the traditional casino industry. This is an interesting one with eharmony. Eharmony is obviously a dating site. But how eharmony differentiates themselves from all the other dating sites is they really pride themselves on making great matches.
These are not simple matches. What they're really trying to understand is each of their clients very intimately and look for attributes about those clients that will make them have a very good match. If you look at eHarmony's advertising and marketing campaigns, they talk about the success of their matching. Lee Harmony is using big data and Hadoop in a cloud application and they're leveraging Spark, the new in memory analytics engine. Why is this important?
Every morning, they have to create 10,000,000 matches. They have 10,000,000 matches, people waking up in the morning hoping to be introduced to their future partner. And the success rate of that is very important to them. So they're adding more and more data and running different algorithms all the time. Yet one thing doesn't change, they have to deliver the matches every morning and that morning changes with time.
But that's when they find their clients are most likely looking to see if they've had a match. And so by leveraging Spark, by leveraging Cloudera's Hadoop offering and running on the Intel platform In the cloud, they're able to increasingly make more matches, success rate of those matches and do it with We heard about Cerner from David Edwards this morning. And Jeff. Rather kind of repeating the Cerner use case, because Dave did an amazing job. I'll talk about 2 other use cases in healthcare that are pretty interesting in how big data is changing how we look at healthcare.
So Intel with the Michael J. Fox Foundation last year. And the Michael J. Fox Foundation is focused on improving Parkinson's disease, Quarterly and there's a 15 minute survey done where the doctor asks, how are you sleeping? How are your tremors?
How is this medication Maybe talk to me or what have you. And that was the data point where we built patterns of treating Parkinson's. In fact, over the last 100 years, very little has changed about our knowledge of Parkinson's. What Intel did with the Michael J. Fox Foundation was actually to instrument Parkinson patients.
So we can now track how they're sleeping. We can track if they have tremors. We can track blood pressure, heart rate. And in fact, of Parkinson's disease, fascinating. We work with the Children's Hospital of Atlanta Around their NICU Center, the intensive care unit for premature born babies.
And it used to be the babies be in the glass room, isolated and fully monitored and nursing down the hall tracking what's happening with these babies. But that just falling on the floor. If nothing happened, they just lost the data. What they did working with us was actually not only retain a history of that data, but they started taking environmental data. So look at when the lights were on and the intensity of lights in the room, how often the door opened, How many people entered the room?
What the temperature of the room was? And they're able to identify What was causing stress in these little babies from just the room environment? And they completely changed the policies and procedures of who's allowed to enter the room, when they're allowed to enter, what the temperature was to improve the health rate of these poor babies. Tremendous use cases happening So Intel has in Malaysia a big manufacturing facility. What they did is they took the Intel Atom Processor and instruments throughout the manufacturing facility and work with Mitsubishi Electric that has a IoT gateway.
What that gateway does is it collects the data at the edge, transmits that data securely back to a data center Where Cloudera's analytics are running on top of Intel's platform. In a POC, the results were amazing. So the first thing you're able to do is reduce the misclassification of bad parts. So now you say these parts are bad, these parts are good, They actually were able to classify many more parts as good, which improve the productivity and the yields of the plants. They're also able to do predictive maintenance of the whole supply chain operation and proactively keep machinery up and running and place inventory throughout the supply chain faster.
During this proof of concept, and a proof of concept is generally safe, could this potentially work, we have a good idea. During the proof of concept, they delivered $9,000,000 of business value to this operation. This is the next generation of Automation, having better insight into what's happening in the shop floor and improving yield. I talked earlier about Mastercard. So Mastercard is maniacally focused on protecting all of our personally identifiable information and specifically around our credit cards.
Mastercard is one of the leading companies behind the PCI standard, the Payment Card Industry Data Security Standard. And they basically Retailers that they have forced it on themselves at the highest standard. And working with Intel and Cloudera for about a year, took us most of last year through the partnership, We actually not only address their security requirements for PCI security, becoming the first to do offering on the planet that's PCI certified. But we delivered to such a security level that Mastercard Advisors, their consulting arm, has formed a partnership with us to take this offering as what they call a secure data vault to markets. And Mastercard is advocating to their customers, retailers and financial institutions to place credit card data to analyze credit card data into an enterprise data hub built on Hadoop.
I never thought a year ago that I would be able to stand in front of customers and say, because of the work we've done with Intel, An enterprise data hub built on Hadoop is the safest place for data in your environment. Because a year ago, Security was one of the biggest concerns around this open source platform. And today, we turned it into one of its greatest advantages. So what's ahead? There's a lot more work to be done around security, And with security comes privacy.
So as the world is streaming data in, you don't want that data stream intercepted and then false data replaced with it. So you have to authenticate all these devices. You have to secure the data on transmission as it's coming back into data centers. And then not only do you keep it secure and encrypted at that central location, And it's not just individuals but applications. So we're doing a lot of work with Intel around securing data in putting tighter controls in place.
We have open source projects called Project Rhino and Project Century, which are all about access controls, protecting privacy, protecting the data. 1 of the greatest Inhibitors to growth, I think in the future, there's going to be concerns around privacy. So we as vendors have to step up and put in place the controls to deliver privacy.
And a lot of times those controls are going
to come back to us as individuals. Lowering the total cost Ownership is very, very important. The last thing our clients want are to build server farms With tens of thousands of nodes or boxes, we're a scale out platform. The advantage of our platform is you can incrementally scale it But the work we're doing with Intel is driving the performance of each box significantly up, therefore requiring fewer boxes for our customers and lowering their total cost of ownership. What we're doing around these new future algorithms and new workloads and designing analytic chips, analytic design chips will reduce the number of boxes our customers need, lowering their total cost of ownership.
And then we're collaborating on systems management tools. How do you monitor this farm of servers All this work lowers our customers' total cost ownership. And then finally, we're working on time to value. That early stats that Diane shared, I think it was like 23% or 27% of projects are successful in the big data space. What we're doing is actually addressing that and accelerating time to value for our customers.
A lot of it has to do with pre engineered systems. Can we design the software with the hardware, Pre package on appliances, so our we're not going through that what configuration do we need. We know and we can test what are the best configuration and make that available on pre engineered boxes or for services in the cloud. The next work we're doing is with the broader ecosystem. In the past year, Our partnership ecosystem has accelerated in large part to the partnership with Intel.
A year ago, we had 800 Companies in the Cloudera Partner Program. Today, we have more than 1450. And so we work with What we call our partner engineering team that works with this whole ecosystem of partners to focus on time to value. So we test. Well, if you start at the bottom with the infrastructure players, the top we're doing, the OEM manufacturers and the cloud providers to optimize our platform for their infrastructure.
We're integrating with the traditional data systems and making sure that you can move data back and forth easily in pre testing all of integrations. All the way up to the application space, so if you're a SaaS customer or a Tableau customer or SAP HANA customer. You want your existing tools operating against this new back end of Hadoop. And so we're pretesting all of that. And then finally, the large system integrators are really stepping up their practices in this space in building industry specific use cases to transform industry.
When you think of the large systems integrators, they're always looking for game changing applications to consult to their customers. And what they're realizing is that data driven applications around big data is how to transform industry. So they're building the practices and we along with Intel are working with them on the specific use cases and making sure we have the platform in place That moves us from the hype of big data to an understanding of how industry is leveraging big data to transform business. From saving lives to protecting identities to improving operations to selling more to customers, We're seeing big data go into production. We as an industry are focused on these use cases to move from the hyphen promise of big data to the reality of what's happening.
We believe this is one of the biggest growing markets in every single industry and every To address any questions if there are any audience. Yes, sir, right back here. I think we have a microphone coming. Obviously, partner extensively with Intel, use graphics processors or FPGAs in some of these Implementations and do you think they have a play in some of these data centers? I am not I don't know the answer to that question.
So I apologize, but I wish we had one of our architects here probably answer how we're using the FPGAs. I do not know the answer to that. Yes. Oh, there is Eli. And gentlemen.
Thank you. Cloudera, Chief Technology. So, yes, we've been working with Intel on a number of the processor technologies. So, there's a lot that we've been off to the core CPU. And then they've also introduced a number of new co processors that are in the pipeline that we'll be able to enable.
And then obviously, You mentioned FPGAs and the Xeon Phi is very interesting and we've got some investigation going on there that I can't really share more than that on, but something we're actively
Any other questions?
Tom, I have one for you. So you've talked a lot about different use cases today, healthcare, telecom. What do you see as the breakout vertical for 2015? Where do you think the explosion is coming next? Obviously, there's been a lot of really great stories coming out of healthcare, but what do you see next?
So the most active vertical is financial services. In financial services, the most common use case that we're seeing Is around compliance and regulatory pressure. It is amazing amount of money that's being spent by financial institutions trying to be compliant or to address the concerns of the regulators. And the regulators are continually changing. It's not And yes, they're getting fined.
Our most recent time was with HSBC, Where HSBC has 28,000 employees working on any money laundering, that they were It turns out that any money laundering is a big data problem. And the folks that are conducting money laundering are very, very smart. You see, you have to use big data tools to look at all the different accounts that we set up in different countries by the same individuals attempting to use your bank for money laundering. An organization like HSBC that acquired across the globe has all these different systems. And so large financial institutions are towards this platform to address things like any money laundering, fraud, KYC, managing risk.
So risk aggregation is a tremendous challenge for large financial institutions. How much risk are you taking in the market? And do you have enough capital on reserve, a too big to fail concern? Well, what's happening is the regulators are putting more and more capital into reserves, which therefore the financial institutions can't put to work. So this big data platform is being used for risk aggregation and measuring risk and then having a tool to state or regulators we have control over our risk.
So I think to answer the question, financial services we're seeing a lot in a very in a use case set of use cases They're directly relevant to their platform. If you're a financial services institution, you have to retain all data by all transactions in full fidelity original format for 7 years in the U. S, 20 years in China. That volume of data, imagine the volume of data that they have to retain. And then they have to quickly analyze it, they have to quickly retrieve it.
So this platform is being used in that it's probably the most common use case we're seeing within that industry. Thank you. Jim. Certainly. So what's our competitive advantage?
From a technical standpoint, today security is a clear advantage. We've invested very heavily into the security of the platform, the security of data. And so that is our strongest But our true advantage are the number of customers that Cloudera has in production. And we are very focused on high value production use cases, working very, very closely with our customers, Leveraging our relationship with Intel to deliver time to value faster. And what's very clear is customers do not want to fail in their big data projects, And we deliver the expertise.
And we're building use cases by industry with blueprints on how to achieve these through best practices. And that is becoming our strongest differentiator is the success of these deployments. I To thank Intel for allowing Cloudera to join this event around the processor. We are big believers in scale out technology through our distributed compute platform, but we're also leveraging the scale up work that Intel is doing with E7