Archive for the ‘IT Transformation’ Category

Unwrapping Machine Learning

Ashvin Naik

Cloud Infrastructure Marketing at Dell EMC

In a recent IDC spending guide titled Worldwide cognitive systems and artificial intelligence spending guide,   some fantastic numbers were thrown out in terms of opportunity and growth 50+ % CAGR, Verticals pouring in billions of dollars on cognitive systems. One of the key components of cognitive systems is Machine Learning.

According to wikipedia Machine Learning is a subfield of computer science that gives the computers the ability to learn without being explicitly programmed. Just these two pieces of information were enough to get me interested in the field.

After hours of daily  searching, digging through inane babble and noise across the internet, the understanding of how machines can learn evaded me for weeks, until I hit a jackpot. A source, that should not be named pointed me to a “secure by obscurity” share that had the exact and valuable insights on machine learning. It was so simple, elegant and completely made sense to me.

Machine Learning was not all noise, it worked on a very simple principle. Imagine, there is a pattern in this world that can be used to forecast or predict a behavior of any entity. There is no mathematical notation available to describe the pattern, but if you have the data that can be used to plot the pattern, you can use Machine Learning to model it.  Now, this may sound like a whole lot of mumbo jumbo but allow me to break it down in simple terms.

Machine learning can be used to understand patterns so you can forecast or predict anything provided

  • You are certain there is a pattern
  • You do not have a mathematical model to describe the pattern
  • You have the data to try to figure out the pattern.

Viola, this makes so much sense already. If you have data, know there is a pattern but don’t know what that is, you can use machine learning to find it out. The applications for this are endless from natural language processing, speech to text to predictive analytics. The most important is forecasting- something we do not give enough credit these days. The Most critical component of Machine Learning is Data – you should have the data. If you do not have data, you cannot find the pattern.

As a cloud storage professional, this is a huge insight. You should have data. Pristine, raw data coming from the systems that generate it- sort of like a tip from the horses mouth. I know exactly where my products fit in. We are able to ingest, store, protect and expose the data for any purposes in the native format complete with the metadata all through one system.

We have customers in the automobile industry leveraging our multi-protocol cloud storage across 2300 locations in Europe capturing data from cars on the roads. They are using proprietary Machine Learning systems to look for patterns in how their customers- the car owners use their products in the real world to predict the parameters of designing better, reliable and efficient cars. We have customers in the life-sciences business saving lives by looking at the patterns of efficacy and effective therapies for terminal diseases. Our customers in retail are using Machine Learning to detect fraud and protect their customers. This goes on and on and on.

I personally do not know the details of how they make it happen, but this is the world of the third platform. There are so many possibilities and opportunities ahead if only we have the data. Talk to us and we can help you capture, store and secure your data so you can transform humanity for the better.

 

Learn more about how Dell EMC Elastic Cloud Storage can fit into your Machine Learning Infrastructure

 

 

Is that a tier in your eye – or is your EDA storage obsolete?

Lawrence Vivolo

Sr. Business Development Manager at EMC²

We’ve all come to expect to have our data from our corporate laptop or workstation: e-mails, schedules, papers, music & videos, etc. backed-up automatically. Some less often accessed data, like archived e-mail, aren’t kept locally blue digital binary data on computer screen. Close-up shallow DOFto save disk space. When you access these files, you find that it’s slower to open. If the archive is very old, say a year or more, then you might even have to ask IT to “restore” from tape before you can open it. In the storage world, this process of moving data between different types of storage is called data tiering and is done to optimize performance and cost. Since ASIC/SoC design is all about turnaround time, time-to-market and shrinking budgets, it’s important to know how tiering impacts your EDA tool flow and what you can do to influence it.

In most enterprises there are multiple levels of tiering, where each offers different capacity/performance/cost ratios. The highest performance tier is reserved typically for the most critical applications because it is the most expensive, and with the least storage density. This tier, typically referred to as Tier “0”, is complemented by progressively lower performance, higher density (and lower cost) tiers (1, 2, 3, etc.). Tiers are generally made using different types of drives. For example, a storage cluster might include Tier 0 storage made using very high performance, low capacity solid-state drives (SSDs); Tier 1 storage made of high-capacity, high-performance Serial-attached SCSI (SAS) drives, and Tier 2 storage consisting of high-capacity Serial-ATA (SATA) drives.

While ideally all EDA projects would be run on Tier 0 storage (if space is available), it is highly desirable to move to lower cost tiers whenever possible to conserve budget.  Often this is done after a project has gone into production and design teams have moved on to the next project. This isn’t always the case, however, especially if tiering is managed manually. (Surprisingly, many semiconductor design companies today have deployed enterprise storage solutions that don’t support automated tiering).

Given the complexities and tight schedules involved in today’s semiconductor designs, it is not uncommon to find and fix a bug only a few weeks away from tape out. When this happens, sometimes you need to urgently allocate Tier-0 storage space in order to run last-minute regressions. If Tier-0 space is being managed manually and space is limited, you may have to wait for IT to move a different project’s data around before they can get to you.  From a management perspective, this is even more painful when it’s your old data, because you’ve been paying a premium to store it there unnecessarily!

The opposite scenario is also common: a project that’s already in production has had its data moved to lower cost storage to save budget. Later a critical problem is discovered that needs to be debugged.  In this scenario, do you try to run your EDA tools using the slower storage or wait for IT to move your data to Tier-0 storage and benefit from reduced simulation turn-around times?  It depends on how long it takes to make the transition. If someone else’s project data needs to be moved first, the whole process becomes longer and less predictable.

Isilon_Image_resizedWhile it may seem difficult to believe that tiering is managed manually, the truth is that most EDA tool flows today are using storage platforms that don’t support automated tiering. That could be due, at least in part, to their “scale-up” architecture which tends to create “storage silos” where each volume (or tier) of data is managed individually (and manually). Solutions such as EMC Isilon use a more modern “scale-out” architecture that lends itself better to support auto-tiering. Isilon, for example, features SmartPools which can seamlessly auto-tier EDA data – minimizing EDA turnaround time when you needed it and reducing cost when you don’t.

For EDA teams facing uncertain budgets and shrinking schedules, the benefits of automated tiering can be signification. With Isilon, for example, you can configure your project, in advance, to be allocated the fastest storage tier during simulation regressions (when you need performance), and then at some point after tape out (ex: 6 months), your project data will move it to a lower cost, less performance-critical tier. Eventually, while you’re sitting on a beach enjoying your production bonus, Isilon will move your data to an even lower tier for long-term storage – saving your team even more money. And if later, after the Rum has worn off,  you decide to review your RTL – maybe for reuse on a future project – Isilon will move that data to a faster tier, leaving the rest available at any time, but on lower cost storage. So next time you get your quarterly storage bill from IT, you should ask yourself “what’s lurking behind that compute farm – and does it support auto-tiering?”

Digital Strategies:  Are Analytics Disrupting the World?

Keith Manthey

CTO of Analytics at EMC Emerging Technologies Division

Close up of woman hand pointing at business document during discussion at meeting“Software is eating the world”.  It is a phrase that we often see written, but sometimes do not fully understand.  More recently I read derivations of that phrase that posits that “analytics are disrupting the world”.  Both phrases have a lot of truth.  But why? Some of the major disruptions in the last 5 years can be attributed to analytics.  Most companies that serve as an intermediary, such as Uber or AirBNB, with a business model of making consumer and supplier “connections” are driven by analytics.  Pricing surges, routing optimizations, available rentals, available drivers, etc. are all algorithms to these “connection” businesses that are disrupting the world.  It could be argued that analytics is their secret weapon.

It is normal for startups to try new and sometimes crazy & risky investments into new technologies like Hadoop and analytics.  The trend is carrying over into traditional industries and established businesses as well.  What are the analytics uses cases in industries like Financial Services (aka FSI)?

Established Analytics Plays in FSI

Two use cases naturally come to my mind when I think of “Analytics” and “Financial Services”; High Frequency Trading and Fraud are two traditional use cases that have long utilized analytics.  Both are fairly well respected and written about with regard to their heavy use of analytics.  I myself blogged recently (From Kinetic to Synthetic) on behalf of Equifax regarding the market trends in Synthetic Fraud.  Beyond these obvious trends though, where are analytics impacting the Financial Services industry?  What use cases are relevant and impacting the industry in 2016 and why?

Telematics

The insurance industry has been experimenting with opt-in programs that monitor driving behavior for several years.  Insurance companies have varying opinions of its usefulness, but it’s clear that driving behavior is (1) a heavy use of unstructured data and (2) a dramatic leap from the statistical based approach using financial data, actuarial tables, and statistics.  Telematics is the name given to a set of opt-in programs around usage-based insurance / driver monitoring programs. Telematics use in insurance companies has fostered a belief that has long been used in other verticals like fraud that pins behavior down to an individual pattern instead of trying to predict broad swaths of patterns.  To be more precise, Telematics is looking to derive a “behavior of one” vs a “generalized driving pattern for 1K individuals”.  As to the change of why this is different from past insurance practices, we will draw a specific comparison between the two. Method One – historical actuarial tables of life expectancy along with demographic and financial data to denote risk vs. Method Two – how does ONE individual drive based upon real driving data as received from their car.  Which might be more predictive about the expected rate of accidents is the question for analytics.  While this is a gross over-simplification of the entire process, it is a radical shift of the types of data and the analytical methods of deriving value from the data available to the industry.  Truly transformational.

Labor Arbitrage

The insurance industry has been experimenting with analytics based on past performance data.  The industry has years of predictive information (i.e., claim reviews along with actual outcomes) based on past claims.  By exploring this past performance data, Insurance companies are able to apply logistical regression algorithms to derive weighted scores.  The derived scores are then being analyzed to determine a path forward.  For example, if scores greater then 50 amounted to claims that are evaluated and then almost always paid by the insurer, then all scores above 50 should be immediately approved and paid.  The inverse is also true that treatments can be quickly rejected as they are often not appealed or regularly turned down under review if appealed. The analytics of the actual present case was compared against previous outcomes of the corpus of past performance data to derive the most likely outcome of the case.  The resulting business effect would be that the workforce that reviewed medical claims would only be given those files that needed to be worked.  The result would be a better work force productivity.  Labor Arbitrage with data and analytics being the disruptor of workforce trends.

Know Your Customer

Retail Banking has turned to analytics as they have focused on attracting and retaining their customers.   After a large trend of acquisitions in the last decade, retail banks are working to integrate their various portfolios.  In Business people shaking hands, finishing up a meetingsome cases, resolving down the identity of all their clients on all their accounts isn’t always as straight forward as it sounds.  This is especially hard with dormant accounts that might have maiden names, mangled data attributes, or old addresses.  The ultimate goal of co-locating all their customer data into an analytics environment is a customer 360.  Customer 360 is mainly focused on gaining full insights around a customer.  This can lead to upsell opportunities by understanding what a customer’s peer set and what products a similar demographic has a strong interest in. For example, if individuals of a given demographic typically subscribe to 3 of a company’s 5 products, an individual matching that demographic should be targeted for upsell on those additional products when they only subscribe to 1 product.  This is using large swathes of data and companies own product adoptions to build upsell and marketing strategies for their own customers.  If someone was a small business owner and personal consumer of the retail bank, the company may not have previously tied those accounts together.  It gives the bank a whole new perspective on who its customer base really is.

Wrap Up

Why are these trends interesting?  In most of these cases above, people are familiar with certain portions of the story.  The underlying why or what, might often get missed.  It is important to not only understand the technology and capabilities involved with transformation, but also the underlying shift that is being caused. EMC has a long history of helping customers through those journeys and we look forward to helping even more clients face them.

 

 

 

 

Infrastructure Convergence Takes Off at Melbourne Airport

Yasir Yousuff

Sr. Director, Global Geo Marketing at EMC Emerging Technologies Division

Latest posts by Yasir Yousuff (see all)

By air, by land, or by sea? Which do you reckon is the most demanding means of travel these days? In asking so, I’d like to steer your thoughts to the institutions and businesses that provide transportation in these myriad segments.

Melbourne Airport_resizedHands down, my pick would be aviation; out of which the heaviest burden falls on any international airport operating 24/7. Let’s take Melbourne Airport in Australia for example. In a typical year, some 32 million passengers transit through its doors – almost a third more than Australia’s entire population. If you think that’s a lot; that figure looks set to double to 64 million by 2033.

As the threat of terrorism grows, so will the criteria for stringent checks. And as travelers get more affluent, so will their expectations. Put the two together, you get somewhat of a paradoxical dilemma that needs to be addressed.

So how does Australia’s only major 24/7 airport cope with these present and future demands?

First Class Security Challenges

Beginning with security, airports have come to terms with the fact that sole passport checks in the immigration process isn’t sufficient. Thanks to Hollywood movies and their depictions of how easy it is to get hold of “fake” passports – think Jason Bourne but in the context of a “bad” guy out to harm innocents, a large majority of the public within the age of reasoning would have to agree that more detailed levels of screening are a necessity.

“Some of the things we need to look at are new technologies associated with biometrics, new methods of running through our security and our protocols. Biometrics will require significant compute power and significant storage ability,” says Paul Bunker, Melbourne Airport’s Business Systems & ICT Executive.

With biometrics, Bunker is referring to breakthroughs such as fingerprint and facial recognition. While these data dense technologies are typically developed in silos, airports like the Melbourne Airport need them to function coherently as part of its integrated security ecosystem and processed in near real-time to ensure authorities have ample time to respond to threats.

First Class Service Challenges

Then there are the all-important passengers who travel in and out for a plethora of reasons: some for business, some for leisure, and some on transit to other destinations.

Whichever the case, most, if not all of them, expect a seamless experience. In this regard, it means free from the hassles of waiting for long periods to clear immigration, picking up luggage at belts almost immediately after, and the list goes on.

With the airport’s IT systems increasingly strained in managing these operational outcomes, a more sustainable way forward is inevitable.

First Class Transformative Strategy

Melbourne Airport has historically been more reactive and focused heavily on maintenance but that has changed in recent times. Terminal 4, which opened in August 2015, became the airport’s first terminal to embrace digital innovation, boasting Asia-Pacific’s first end-to-end self-service model from check-in kiosks to automated bag drop facilities.

This comes against the backdrop of a new charter that aims to enable IT to take on a more strategic role and drive greater business value through technology platforms.

“We wanted to create a new terminal that was effectively as much as possible a fully automated terminal where each passenger had more control over the environment,” Bunker explained. “Technical challenges associated with storing massive amounts of data generated not only by our core systems but particularly by our CCTV and access control solutions is a major problem we had.”

First Class Solution

In response, Melbourne Airport implemented two VCE Vblock System 340 with a VNX5600 converged infrastructure solution featuring 250 virtual servers and 2.5 petabytes of storage capacity. Two EMC Isilon NL series clusters were further deployed at two sites for production and disaster recovery.

Business People Rushing Walking Plane Travel Concept

The new converged infrastructure has allowed Melbourne Airport to simplify its IT operations by great leaps, creating a comfortable buffer that is able to support future growth as the business matures. It has also guaranteed high availability on key applications like baggage handling and check-in, crucial in the development of Terminal 4 as a fully automated self-service terminal.

While key decision-makers may have a rational gauge on where technological trends are headed, it is far from 100%. These sweeping reforms have effectively laid the foundations to enable flexibility in adopting new technologies across the board – biometrics for security and analytics for customer experience enhancement – whenever the need calls for it. Furthermore, the airport can now do away with separate IT vendors to reduce management complexity.

Yet all these come pale in comparison to the long-term collaborative working relationship Melbourne Airport has forged with EMC to support its bid to become an industry-leading innovation driver of the future.

Read the Melbourne Airport Case Study to learn more.

 

How The Data Center Is Becoming Software-Defined

How pervasive is the concept of software-defined?

In mid 2012 VMware CTO Steve Herrod and others began to articulate the concept of the software-defined data center. This concept was just as often received as a marketing position from vendors as an observation about the evolution of the data center. At the time, I blogged about the basic concepts of the software-defined data center and followed-up the initial post with an additional blog posts about storage challenges in the software-defined data center. Other posts addressed related topics such as how cloud adoption contributes to the evolution of APIs.

Now, since many months have passed, which can be measured in dog years in high-tech, I would like to revisit the concept of software-defined as it pertains to storage as well as compute and networking, and its status in 2013. I believe that the software-defined data center has moved beyond concept, putting us on the cusp of a time when new architectures and product offerings will make it a reality. (more…)

EMC Data Protection Advisor For As-A-Service Cloud Environments

What can you do to ensure data protection as you move to cloud?

Services-based storage, infrastructure, and data protection trends and technologies are recurring topics in this blog. Awhile back I wrote a post about enabling data protection as-a-service discussing the need for centralized management at cloud-scale, multiple service rates based on customer data protection needs or usage, and historical data for analysis and trending. The reality is that you can only get so far with legacy products built for physical environments. At some point, management tools, like the data center environments they support, need to be remade to the requirements of the day. Effective data protection solutions are no exception.

Data protection needs are more acute for as-a-service cloud models and require new approaches. Now, with the release of EMC Data Protection Advisor 6.0, I would like to share what it means to augment a successful data protection solution and extend it with a new distributed architecture and analysis engine to cloud deployments, without losing any usability benefits (i.e. without making it complex). (more…)

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