SPECIAL REPORT:

Big Data for Analytics Services

JANUARY 2016

IT Infrastructure for the

Cognitive Era Helping IT leaders become better service providers

2 // JANUARY 2016 Data for Analytics Services

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3 IBM PERSPECTIVEIT analytics services for the cognitive era: are you ready?

4 AN INSIGHT-DRIVEN WORLDAPI integration brings data analytics into the cognitive era

8 DRIVING BETTER OUTCOMESIn-place analysis enables faster, smarter predictive analytics

12 GAME-CHANGING ANALYTICS STARTS WITH INFRASTRUCTUREPower Systems is a key driver in clients’ digital transformation

17 TRANSFORMING INFRASTRUCTUREOrganizations maximize storage to deliver faster insights and cost savings

INSIDE

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IT Analytics Services for the Cognitive Era: Are You Ready?

B usiness innovation is happening at ever-increasing speeds as IT leaders look for the latest and best capabilities to facilitate their digital transformation in the cognitive era.

For service providers in the age of hybrid cloud, cognitive computing and real-time analytics capabilities are two key drivers of innovation. Given the breakneck pace of business, companies that want to compete have to be in the fast lane—and that means delivering intelligent insights in real time without wait. Through cognitive computing, systems that can sense, learn and adapt are making faster analytics possible.

Cognitive systems go beyond what traditional programming can do by learning and adapting as new information becomes available. They enable computing at an enormous scale and give businesses the power to out-think their competition.

By using the latest real-time analytics services and locating data optimally for analysis, companies can access an unprecedented level of business visibility and insights. Stream processing addresses data in motion—data coming in at high speed that requires high-throughput, low-latency processing. In-transaction analytics embed the analytic capability in the transactions themselves, exercising more

profitable business actions during interactions with users. Both stream processing and in-transaction analytics are helping organizations derive better business outcomes when the action window is milliseconds.

The innovations happening today in cognitive computing and real-time analytics drive infrastructure design requirements. How do businesses address and monetize the natural resource of big data? Do we have the infrastructure capabilities to make real-time insights a reality?

Today’s big data comes in massive quantities and a variety of forms (i.e., structured, unstructured, at rest and in motion). To capture the largest volume and variety of data, businesses need data acquisition tools that can ingest data rapidly. Real-time ingestion makes real-time decision making possible.

The ability to efficiently locate data in the right place, at the right cost, with the right protection, is also critical for implementation of analytics services.

Finally, delivering analytics services depends upon servers and storage that are designed for insights: high-speed processors,

hardware accelerators, low-latency storage and a large memory capacity.

Systems designed for data and analytics can enable smarter, faster business value from big data and, ultimately, better customer experiences that drive business growth. IBM servers and storage have the strength to support today’s analytics workloads; our infrastructure capabilities are built to support a new age of analytics computing. Only one question remains: is your business ready for the cognitive era?

IBM PERSPECTIVE

RNIN LEI

Distinguished Engineer, CTO, Analytics and

Big Data, IBM Systems

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An Insight-Driven WorldAPI integration brings data analytics into the cognitive era

A ll organizations are looking for ways to create exceptional customer experiences that will drive business growth and generate loyalty—and data insights will be key to rewarding outcomes. Successful IT leaders are providing enterprise-wide analytics services that enable real-time insight

from a broad and diverse set of big data sources. The ability to acquire and integrate external data with internal operational data is a foundational step to provide relevant, contextual and insight-driven content to users. As a result, businesses must master the API economy and bring the power of cognitive to their data.

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In an insight-driven world, data acquisition is a critical infrastructure capability. By rapidly capturing the largest volume and variety of data, and then enabling cognitive technologies that sense, learn and adapt, companies can put more of their data to work to achieve their business goals.

Such technology trends are opening doors to new ways of doing business. They are strengthening our ability to optimize business processes, propel innovation and revenue growth, and ultimately offer customers meaningful, personalized interactions that generate loyalty.

Cognitive businesses take maximum advantage of their data by:

• Integrating it in hybrid clouds through the standards-based API economy

• Incorporating new cognitive capabilities

• Driving better processes and decision making through analytics insights

So, how do APIs and cognitive technology make it possible to integrate data and bring analytics into the cognitive era to enable your business to deliver compelling consumer experiences and open new revenue channels?

Connecting Data Sources Through API IntegrationTo get the most out of a huge volume and variety of data, and identify and mine the right insights, companies must integrate their existing business applications with the oceans of unstructured data coming from external sources. Hybrid cloud makes this possible by bringing together systems of record (e.g., data in existing business applications such as ERP and CRM systems) and systems of

engagement (e.g., external social, mobile and Internet of Things data). A hybrid cloud architecture spans traditional IT, private cloud, dedicated or shared public cloud, and third-party services and data. By adopting a hybrid approach, organizations can expose their business logic and apps as APIs, opening up new possibilities for integration and innovation.

An API is a set of routines, proto-cols and tools for building software applications that act as a technol-ogy glue to connect data and busi-

ness systems through the cloud. APIs help developers design, build, deliver and manage composable services and business processes. In terms of business value, they ensure secure integration across enterprise IT and hybrid clouds in a way that unlocks new value.

The API economy enables much innovation today, helping companies expose their core business assets and data through APIs to a system of developers, customers or partners. To take advantage of this, you must adopt an API strategy as part of your overall digital transformation.

API integration makes organizational data available for analytics; this is where we find ways to leverage data for business growth and open up new possibilities. Standards-

based API integration across the business ecosystem can help you monetize your data and provide rich customer experiences that are the basis of competitive advantage.

An enterprise service bus can enable systems and applications to communicate with each other and provide a platform for the API economy and analytics. Messaging middleware can speed the integration of diverse applications and business data across multiple

platforms. Finally, API management platforms can help enterprises create, deploy and manage their APIs in a secure, controlled fashion. IBM Middleware provides all of these capabilities—and more.

Connecting data sources in a hybrid cloud, through middleware capabilities that support API integration, makes it possible for organizations to capitalize on the oceans of data available from internal and external sources.

Bringing Cognitive Power to Your DataThe connectivity and integration made possible through the API economy also create a foundation for cognitive business. As more APIs are created, the ability to innovate will depend largely on an

Connecting data sources in a hybrid cloud, through middleware capabilities that support API integration, makes it possible for organizations to really capitalize on the oceans of data available from internal and external sources.

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organization’s capacity to find and work with the right ones—and this is where cognitive can help. Cognitive systems think, learn and adapt to evolving business contexts. They enable companies to analyze information and services to decide which APIs they should create and expose.

Applying cognitive APIs across organizational data will help drive decision making based on

a richer understanding of all of your data sets. But harnessing cognitive technologies requires building an architecture that allows you to quickly and flexibly plug in cognitive capabilities.

Data acquisition is a key competency for creating IT analytics services that deliver insights, inform operational decisions and enable superior customer experience. You must capture the data quickly and enable cognitive APIs that sense and learn from it. Time is of the essence when it comes to data analysis, and cognitive APIs can speed a company’s ability to get data-driven answers.

IBM is offering new services and software that allow companies to get greater use of the API economy and cognitive technology. IBM API Harmony, announced in 2015, is a tool for building applications in the cognitive era. It uses technologies like intelligent mapping and graph technology to anticipate what a developer will require to build new apps, make recommendations on which APIs

to use, show API relationships and identify what is missing. IBM DataPower Gateway is a security and integration platform that provides the API gateway to enforce API security and control, allowing businesses to expand the scope of their data and other IT assets to new channels.

The ways we can access and learn from big data in the cognitive era are expanding rapidly, and harnessing the

power of cognitive APIs can inject greater intelligence into your business decisions.

Reinventing Processes for Better DecisionsSuccess in the digital age requires businesses to do work differently because how work gets done can redefine the customer experience. It’s not enough to integrate data through APIs and adopt cognitive computing; you also have to reinvent the processes your technology supports to convert your digital transformation into a true transformation of the way your organization operates.

Bringing cognitive capabilities into business operations can help organizations better sense what is happening and act quickly and consistently. Cognitive decision support tools can learn from every interaction with people, processes, systems, data and devices—thus adapting in an evolving digital environment.

Cognitive business operations enhance the expertise of a workforce and influence the following needs:

• How we find context and understand the relationships between data

• The application of predictive analytics and business rules

• Real-time, continuous delivery of data intelligence

IBM operational decision management solutions enable businesses to codify their policies, practices and regulations to manage decision logic; empower business users to take control of business logic; and automate decision making with context.

Cognitive operations result in real business outcomes—fast, seamless, insightful responses to customer needs.

An Infrastructure Designed for InsightsEvolving your infrastructure for digital transformation can help boost revenues, inspire customer satisfaction and deliver greater value to your users.

An infrastructure built for the cognitive era offers agile, continuous delivery across the mix of cloud and traditional IT that provides the best performance, cost and risk profile for the needs of your business at any given time. It is built on open standards, allows you to build on your existing IT investment and is flexible enough to change at the speed of digital business.

These are the hallmarks of a hybrid cloud architecture designed for data. Are you ready?

David Crozier is a technology marketing expert with 20 years of experience across the IT industry.

Cognitive operations result in real business outcomes—fast, seamless, insightful response to customer needs.

The digital revolution has shifted the IT infrastructure conversation to a strategic discussion

IBM Institute for Business Value (IBV) asked IT executives about the challenges they face competing in the emerging digital world. Read the results in the IBV report “New Technology, New Mindset.”

To register for the IBV report or learn more, visit ibm.com/systems/infrastructure-report

8 // JANUARY 2016 Data for Analytics Services

D R I V I N G B E T T E R

OUTCOMESIn-place analysis enables faster,

smarter predictive analytics

W hat if you could always anticipate customer needs? In the cogni-

tive era, delivering superior customer service requires busi-nesses to evolve their infrastruc-ture for smarter analytics and real-time response.

How can banks, for example, prevent fraudulent transactions without flagging legitimate ones or slowing down the process, thus increasing customer confidence without sacrificing satisfaction? Or how can retailers provide exceptional point-of-sale experiences for consumers by offering them personalized suggestions instantly and in context? Regardless of the industry, customer experience is the consumer battleground today—and in the age of cognitive business, real-time analytics is at the heart of delivering great customer service.

Organizations face numerous challenges on this front: decades of managing operations and analysis as separate practices have resulted in a massive volume and variety of data distributed over multiple, disparate platforms. Analytics solutions are often aligned to specific architectures and tied to inflexible programming models. Ongoing manual intervention is required to integrate data into coherent analytics solutions. All of these complications cost businesses time—a key asset they can’t afford to waste when it comes to delivering real-time customer service.

IT departments must become the trusted service providers to their organizations and ecosys-tems partners, and therefore are innovating to address these challenges with the latest technol-ogy solutions that deliver immedi-

ate insights, inform operational decisions and enable superior customer experience. In-place data analysis through integrated IT systems is facilitating faster, smarter predictive capabilities and helping organizations drive better outcomes for their customers. When every millisecond counts, an infrastructure designed to handle both operations and analysis can make all the difference.

Speeding up Data AnalysisA single interaction can make or break a customer’s experience with your brand. In such an environment, speed of response can be a differentiator, and integrating analytics services into operational systems is one way to design an infrastructure for faster insights. Through capabilities like in-place predictive analytics, organizations are identifying,

By Paul DiMarzio and Mythili Venkatakrishnan

9 // JANUARY 2016 Data for Analytics Services

adapting and acting on their data in real time. They are predicting outcomes before they happen, all while saving on the cost and risk of moving data from one place to another.

Predictive analytics helps organizations better understand customers, ensure good customer experience and stand out against the competition. So, how do you deliver forward-looking analytics services that are deeply embedded into your operational systems?

Processing Data at the Point of OriginFirst, let’s think about effective placement of data and applications. Traditionally, data residing in operational systems is copied, moved to a centralized

location and then analyzed. The copying and transfer of data, however, run counter to the concept of real-time analytics because each step in the process takes time. Not only that, but moving highly sensitive data increases the risk of a security breach.

A new approach has evolved to address these challenges: processing data at the point of origin. “In-place analysis” simply means processing operational data where it resides instead of moving it to another location. This removes the time, cost and security risk involved in moving operational data from the equation, which makes it possible to speed up analytics and, ultimately, address customers more quickly.

A Hybrid System for More Agile Analysis The latest hybrid technology is fusing transactional data and analytics in systems that make real time more real than ever before. The majority of high-value data enterprises possess today resides on the z Systems* platform, and several IBM z* solutions have evolved to address pressing analytics challenges:

IBM DB2* Analytics Accelerator for z/OS* is one such solution. It’s a single, integrated system combining transactional data and historical data for highly accelerated business analysis and reporting. By co-locating analytics and operations, businesses can gain more instantaneous

insights to help them respond to customer wants and needs before they even happen. This sort of capability enables a retailer to offer a customer that in-context upsell or cross-sell, or a financial institution to mitigate risk by preventing fraudulent transactions. The DB2 Analytics Accelerator for z/OS is a hybrid platform built on the security, safety and reliability of z Systems. It enables in-place data analysis, saving organizations precious time and helping them drive smarter outcomes for their customers.

In-place analytics solutions for z Systems can do more than provide in-the-moment response for customers—they make the anticipation of customer needs a reality. This capability to process data at the point of origin means

more embedded analytics instead of disconnected analytics. It enables organizations to stand out by knowing what is going to happen before it happens.

Predictive analytics services provide for in-place data processing prediction. Through advanced machine learning and statistical methods, predictive analytics helps businesses discover patterns and create models, which are then executed within the scope of a transaction for operational decision making.

In-database processing improves the responsiveness and accuracy of a predictive model so organizations can anticipate their customers’ wants, needs and motivations. Instead of wasting time on data transfer

and complex reporting processes, companies can seek more automated, intelligent decisions.

For predictive analytics services to deliver optimal performance, the data must be close to the analytics tool—which brings us back to the idea of in-place analysis. Any distance between the data and the decisions creates delays; therefore, an integrated system that combines mainframe hardware and analytics software with business processes can be a key differentiator. New capabilities of the IBM DB2 Analytics Accelerator enable advanced predictive models to be built from z/OS data with speed and accuracy.

Companies whose operations are built on IBM z Systems have access to tools that make it

ARE YOU READY TO TAKE THE NEXT STEP IN YOUR DIGITAL TRANSFORMATION AND DIVE INTO THE ERA OF COGNITIVE BUSINESS?

10 // JANUARY 2016 Data for Analytics Services

possible to perform advanced analytics in the scope of a z/OS transaction. IBM SPSS* Modeler and SPSS Statistics are predictive analytics software solutions that work with new capabilities of the IBM DB2 Analytics Accelerator for z/OS to provide real-time analytics through predictive modeling.

Once a predictive model is built, the most effective execution of that model involves deploying it directly into the transactional system. IBM Business Partner Zementis has made its predictive analytics engine available on z/OS for this purpose—Zementis for z Systems is a Predictive Model Markup Language (PMML) engine that installs in the transactional application and can execute a predictive score in just a few milliseconds. These capabilities enable businesses to place the analytics directly in line with their transactions and data to drive successful customer interactions through real-time predictive analytics services.

Embedded Analytics for Unparalleled Performance and InsightsBusinesses are looking for ways to more easily gain insight from all of their data; shifting toward a more embedded analytics environment can make a big difference in delivering insights that change the tide in customer experience. But not all data is in IBM DB2 or on the mainframe.

The data available to organizations today comes from both external systems of engagement (e.g., social media and mobile device data) and internal systems of record (e.g., transactional enterprise data held in operational systems). Both types of data are highly valuable to organizations seeking to understand customer

behavior and drive better customer experiences, and the greatest insight comes from combining them.

In the past, applying analytics services to multiple data sets in different locations has been complicated—especially given the requirement for real-time response. We need the ability to analyze heterogeneous, potentially distributed data sources without moving the data.

For diverse data sources, Apache Spark enables the “democratization of data”—helping companies use common standards to gain faster insights. Apache Spark is an analytics framework and platform that can help meet the demands for faster analytics services by offering a federated analytics approach, meaning that data can be analyzed in place for better security and optimized speed. Spark is an open-source cluster computing framework with in-memory processing and is being enabled natively for z/OS as well as Linux* on z Systems. It is not reliant on a specific file system or platform, and it offers a unified programming environment; support for diverse programming languages; and standard framework for common analytic methodologies

IBM Business Partner Rocket Software has developed a new product, Rocket Mainframe Data Service for Apache Spark on z/OS, to enable Spark to be used with a variety of z/OS data types. When most of the data that is going to feed Spark analytics resides on z/OS, running Spark on z/OS with integrated support from Rocket delivers performance, security and colocation advantages.

Spark is a platform with rapidly growing adoption and is giving us a fresh look at how analytics are constructed

and performed in today’s enterprises.

Spark on z Systems is another capability that can help organizations use the strengths of transactional environments and their high-value data (structured, unstructured, at rest or in motion) to offer faster, smarter insights.

Make Real Time a RealityTo deliver exceptional experiences that engage and delight customers, businesses need fast analytics services made for the cognitive era. Capabilities like in-place analysis, predictive analytics services, and a federated approach to data analysis are all making real-time results a reality—and they are all available in the z Systems family.

Companies that want to compete on the consumer battleground are adopting a strategy that analyzes data in place for truly real-time responsiveness. They are building predictive intelligence into their core operations so they can gain easier insight and anticipate customer needs. IBM z Systems analytics services allow each organization to add insight-generating capabilities to core operations at their own pace. Are you ready to take the next step in your digital transformation and dive into the era of cognitive business?

Paul DiMarzio is a mainframe strate-

gist with nearly 30 years’ experience

with IBM focused on bringing new and

emerging technologies to the main-

frame. He’s currently responsible for

developing and executing IBM’s world-

wide z Systems big data and analytics

portfolio marketing strategy.

Mythili Venkatakrishnan is an

IBM senior technical staff member

and is the z Systems Architecture and

Technology lead for analytics.

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Game-Changing Analytics Starts

With InfrastructurePower Systems is a key driver in pushing clients’ digital transformation

By Keshav Ranganathan

13 // JANUARY 2016 Data for Analytics Services

provide compute capacity that enables delivery of a range of analytics services.

A first step is to seamlessly integrate heterogeneous data environments across your business. Systems of record represent sources of structured data in relational databases and data warehouses from ERP, CRM and financial systems. Systems of engagement represent unstructured data from social media, surveys, public records and sensors that helps you understand customers, partners and employees. These diverse data types come together in systems of insight, which enable delivery of data and analytics services to ensure that every decision, interaction and process is fueled by data and advanced analytics—faster and in a more personalized fashion (see Figure 1 on page 14).

Building on a Solid FoundationA high-performance infrastructure that’s scalable for varying workloads, highly available and optimized for price performance is critical. It provides seamless integration of analytics services to drive better business outcomes and allows companies to build on what they already have while adding new capabilities as their needs grow.

IBM is leading innovation across the infrastructure stack to deliver analytics as a service for our clients. We are helping IT leaders become trusted service providers in the cognitive era through hybrid cloud.

So where do you begin? IBM offers a broad set of

analytics capabilities built on the proven foundation of a single platform, IBM Power Systems*. The open, secure and flexible Power Systems platform is designed for big data; it has

massive (I/O) bandwidth to deliver analytics in real time, and it can provide the capabilities needed to handle the varying analytics initiatives each business requires.

Differentiated Value for Analytics In 2014, IBM announced POWER8*—the first microprocessor designed for big data and analytics. The POWER8 microprocessor offers numerous advantages for big data and analytics solutions: processing capability with a large number of processor threads; memory capacity and bandwidth; cache workspace; and the capability to move information in and out of the system at the rapid speeds required. With these advantages, it delivers levels of performance you need to make decisions in real time, helping you capitalize on the currency of data by finding business insights faster and more efficiently. The Power Systems platform is designed for big data—from operational to computational to business and cognitive solutions. The systems are optimized for performance and can scale to support growing workloads.

The data and application layers are key elements of a big data and analytics architecture.

On the left side of Figure 2 (see page 15) is the data layer—that is, all of the data discussed earlier. A variety of data-management options are needed to effectively handle the avalanche of data, including NoSQL databases; relational databases; analytics-optimized; in-memory, columnar databases; Hadoop; and data warehouses.

On the right side is the application layer—the key analytics workloads, everything

D ata is the new basis of competitive advantage for businesses, and it is

driving digital transformation for many companies today. Trailblazing organizations are using data to accelerate insights and decision making. They are:

• Applying analytics across many different data sources inside and outside the enterprise

• Capturing the time value of data to help deliver real-time insight to improve business decisions

• Gearing up for cognitive computing—a paradigm where systems themselves can hypothesize, learn and improve over time

As organizations become data-savvy and insight-driven, analytics become mission critical. This places additional emphasis on the infrastructure needed to deliver analytics services across the enterprise.

Integrating Data EnvironmentsInsight-driven companies are infusing analytics in everything they do. From attracting, growing and retaining customers to optimizing operations while countering fraud and threats to transforming financial and management processes—every aspect of the business is improved by analytics. To deliver game-changing analytics capabilities, you must evolve your IT infrastructure to address data and analytics services. The infrastructure must help with acquisition, placement and management of all types of data—structured, unstructured, data at rest and in motion—and

from cognitive solutions around IBM Watson* to Cognos* and SPSS* to industry solutions, that are integrated into business processes. IBM offers all of this on a single platform with Power Systems.

Improving Operational SimplicityIT budgets are dedicated mostly to management of the IT environment rather than to delivering new capabilities to the business. With Power Systems innovations, we address the needs of structured and unstructured data management with solutions that simplify delivery of data and analytics services and are optimized for price performance. Key solutions include:

• Structured data. For structured data, IBM DB2* with BLU Acceleration* on Power Systems brings faster analytic queries and reports processing as well as operational simplicity. IBM DB2 with BLU Acceleration is optimized to take advantage of simultaneous multithreading in POWER* processors; it automatically detects and exploits larger cache sizes and memory bandwidth. BLU Acceleration technology takes advantage of multiple cores, providing consistent performance for

a large number of users. With BLU Acceleration and the latest LC line for Power Systems, we saw 2.03x more query results per hour per core versus Intel* Haswell servers, at 36 percent lower hardware cost of acquisition.

• Unstructured data—NoSQL database. The explosive growth of new mobile, social and cloud applications creates a need for lightning-fast response at high data volumes. A growing number of NoSQL databases are supported and optimized on Power Systems. IBM Data Engine for NoSQL is an integrated platform that uses the Coherent Accelerator Processor Interface (CAPI) in POWER8 to improve infrastructure density. The result is lower hardware, maintenance and energy costs with minimal performance impact. Data Engine for NoSQL can deliver up to 56 TB of extended memory with one POWER8 processor-based server with CAPI-attached Flash system—and without sacrificing performance. That means faster read-write speeds to address the millions of records of unstructured data that don’t fit into traditional SQL data structures. Redis Labs’ key-value pair NoSQL database is supported today, and we are on track

to deliver a broader set of NoSQL databases (e.g., graph, document, columnar) in 2016.

• Unstructured data—Hadoop and Spark (IBM BigInsights*). IBM Data Engine for Analytics is a customizable, pre-integrated infrastructure solution with integrated software optimized for big data and analytics workloads. It is designed to help companies speed insights on massive amounts of data and has the flexibility to grow as clients’ needs change. The IBM Data Engine is ideal for workloads such as Spark and Hadoop. It provides flexible storage and compute resources that are easy to deploy and align to specific business requirements. This means you can move or add more storage and compute when and where it’s needed. In addition, the recently introduced S812LC is a cost-optimized big data server that delivers superior performance and throughput for Spark and Hadoop workloads.

Together, these solutions deliver data services designed for capitalizing on data in the cognitive era—to help businesses derive real-time decisions and increase speed of innovation.

Accelerate Insights With Analytics ApplicationsMaking the best decisions means understanding what’s happening, why it’s happening, what could happen in the future and what you need to do.

IBM Solution for Analytics–Power Systems Edition delivers a solution for business intelligence and predictive analytics. It’s a flexible and integrated service that provides options to pre-load and configure one or more IBM

14 // JANUARY 2016 Data for Analytics Services

© 2015 IBM Corporation 1

FIGURE 1

15 // JANUARY 2016 Data for Analytics Services Big Data Analytics JANUARY 2016 // 15

analytics applications with data warehouse acceleration on a POWER8 processor-based server.

These analytics tools, alongside many others in the IBM analytics suite, help turn big data into actionable insights to help companies address customer retention and growth, IT costs, management of security risks, counter-fraud techniques, optimization of IT operations and

digital innovation opportunities. Together, they are helping IT leaders undergo digital transformation and become stronger service providers in the age of hybrid cloud.

Infrastructure Matters for Big Data and AnalyticsAt IBM, hardware and software organizations work together to optimize the entire solution

stack for data and analytics. The suite of capabilities built on IBM POWER8 can help businesses develop a thorough plan for addressing their big data and analytics needs in the cognitive era. The Power Systems family is optimized for performance and can scale to support demanding and growing capabilities for delivering data and analytics services while controlling cost. As a foundation of IBM’s comprehensive big data and analytics portfolio, the Power Systems platform is a key driver in pushing businesses forward in the race toward digital transformation.

IBM’s breadth and depth of solutions for big data and analytics is unmatched. The company is committed to delivering infrastructure that will contribute to your success today and evolve to meet your changing needs in the future. Welcome to the cognitive era!

Keshav Ranganathan is the

Power Systems analytics offering

manager at IBM.

FIGURE 2 The platform designed for big data & analytics

We saw 2.03x MORE

query results per hour per core

versus Intel Haswell servers,

at 36 PERCENT LOWER

hardware cost of acquisition

On-demand webcast: New technology, new mindset

Featuring Forrester Research and the IBM Institute for Business Value

How does infrastructure impact APIs, data and analytics, and scalable environments? View this on-demand webcast to find out how market leaders are defining best practices for IT infrastructure amid the shifting IT mindset.

ibm.co/NewTechNewMindset

17 // JANUARY 2016 Data for Analytics Services

H ow does Netflix predict which movies you want to watch or Plenty of Fish foresee who you’re going to fall in love with? How can Coca-Cola be confident that there’s enough product on the delivery truck to meet customer demand in

a given retail location? How does a hospital ensure it can quickly access a patient’s full medical history and provide real-time analytics-based treatment?

Organizations maximize storage to deliver faster insights and cost savings By Tom Sullivan and Yael Shani

Transforming Infrastructure

18 // JANUARY 2016 Data for Analytics Services

It all begins with data. Data is emerging as the world’s newest resource for competitive advantage. The massive volume and variety of information being collected from social media, Web logs, retail transactions, industrial equipment, smart devices and more has the power to transform the way we live, work and make decisions. Data presents enormous opportunities for businesses today—but only if you can make sense of it.

IT analytics services can deliver immediate insights to help companies innovate, optimize operations, save costs, improve services, manage risks, counter threats and fraud, make critical decisions, and engage with their customers where and when it matters most. These insights are leading digital transformation in today’s cognitive era, but companies must evolve their infrastructure to benefit from them.

Once You Know, You Can’t UnknowIn a recent study titled “Becoming an Analytics-Driven Organisation” EY found that 81 percent of organizations agree that data should be at the heart of decision making, but only 31 percent have restructured their operations to do it.

If most organizations already recognize the importance of data analytics, why have so few made the necessary changes to their infrastructure to support it?

The 2014 IBM Institute for Business Value study “Analytics—The Speed Advantage” found that:

• 63 percent of organizations realize a positive return on analytic investments within a year

• 69 percent of speed-driven analytics organizations created a significant positive impact on business outcomes

• 74 percent of respondents anticipate the speed at which executives expect new data-driven insights will continue to accelerate

Given these statistics, businesses can’t afford not to strengthen their infrastructures to deliver analytics services. As the value of data continues to grow, current systems won’t keep pace. The right tools—with the right infrastructure behind them—can help companies master hybrid cloud for digital transformation.

Real-Time Insights You Can AffordHow do you reconcile budget constraints with the business imperative of improving your analytics capabilities?

Decision makers need faster insights—the right numbers in hand in real time so they can make the most informed choices. This means efficiently locating data in the right place and running analytics at the most optimal location—next to the data. But budget limitations are one of the most often-cited challenges to implementing analytics solutions. With 70 percent of IT budgets spent on running existing IT systems and operations, there’s not a lot left. Breaking this

maintenance loop can set a business apart from competitors.

The starting point to getting faster insights while saving on costs is optimizing your current infrastructure. Better utilization of current systems and storage directly affects budget because the gains in efficiency mean an immediate reduction of maintenance costs. This in turn frees up more funds for digital transformation.

Companies can see a quick ROI on analytics solutions when they start the analytics journey this way. It allows them to create new opportunities for revenue growth without increasing the cost burden.

Addressing Performance for Analytics Choices around architecture and infrastructure—systems, software and storage technologies—are critical to delivering differentiated customer experiences.

Collecting and analyzing data isn’t enough; real-time response is the key to meeting client expectations. You need an infrastructure that has been intentionally designed to provide unparalleled performance in interacting with your most valuable asset—your data. Faster infrastructure leads to faster insights and the ability to make better-informed decisions, which can actually contribute money to the bottom line.

Take storage as an example. IT storage infrastructure is a lot like a physical warehouse. If a warehouse is half filled with empty boxes, that space isn’t

Coca-Cola processed

20xmore data delivering insights

4x FASTER

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compression. The significant business outcomes they achieve demonstrate the importance of having scalable performance to handle ever-growing structured and unstructured data volumes, and the importance of accelerating business applications to enhance time to insight.

Have you ever thought about what it takes to have an ice-cold Coke waiting at your favorite shop at any time on any day? Coca-Cola Bottling Co. Consolidated had to uncover deeper insight into customer demand in order to make this happen. By implementing IBM FlashSystem* technology, Coca-Cola processed 20x more forecasting data within the existing overnight window and delivered deeper demand insights 4x faster. This allowed the company to match manufacturing output with product demand, reduce the risk of over- or understocking, enable earlier logistics planning, increase profitability and make sure you can get a Coke whenever you want it!

In today’s world, users expect from an application a response time of less than a second and 24-7-365 availability. If their experience doesn’t match their expectations, they will move on. The dating website Plenty Of Fish, with over 60 million

users, copes with hundreds of thousands of new images every day. IBM FlashSystem enabled Plenty of Fish to experience a 500x reduction in latency for viewing images on its site. Faster response times help the company retain millions of customers.

Netflix is another company that relies on real-time data processing and analysis to provide its service. Netflix allows its viewers to stream movies and TV shows online or directly to their television screen using Xbox, Wii, PlayStation and many other devices. By locating its data with IBM XIV* Storage System, the company can support approximately 300,000 sub-millisecond database transactions per minute with no downtime. This enables it to offer a seamless, high-quality service and make movie recommendations tailored to its users’ preferences.

Free Data From Hardware Constraints In addition to IBM’s $1 billion investment in R&D of FlashSystem solutions, it is redefining storage economics with new software by committing more than $1 billion over five years to developing next-generation technology and leading the way in SDS. IBM Spectrum Storage*

effectively being used. Many organizations buy more storage capacity (more buildings), but a significant portion of what they already own is wasted space.

For data, software-defined storage (SDS) capabilities can help companies genuinely use their space. Not only that, but SDS can help them compress what is in each “box”—a lot like packing boxes more effectively in a warehouse to reduce wasted space.

If you double utilization of the warehouse by removing empty boxes and store twice as much in each box, your storage infrastructure utilization can improve fourfold. The same is true with data placement. If you want to efficiently locate data and applications in the right place at the right cost, you’ll have to use policy engines and analytics-driven data management and move data between storage systems without disrupting users or applications.

Know What Your Customer Wants Right NowIBM clients are already benefiting from data analytics services. They are optimizing data economics by implementing data management solutions and other innovative technologies such as virtualization and

Plenty of Fish experienced a

500xreduction in latency using IBM

FlashSystem

To derive cost savings, revenue generation and business growth from big data, businesses today need to

start by evolving their existing IT infrastructure.

20 // JANUARY 2016 Data for Analytics Services

technology transforms how storage is deployed and managed to deliver clear value around simplified data management, advanced data protection and data retention, unlimited data scalability, and improved data economics.

In a perfect world, data would be placed in the fastest storage for analysis and then moved to lower-cost storage when not in use. IBM uses policy engines and analytics-driven data management to put data in the right place automatically, based on usage, and move data between storage systems without disrupting users or applications. The result? Clients can run big data and analytics projects and environments with faster performance at a lower total cost.

IBM Spectrum Scale*, for example, manages big data—both structured and unstructured—reducing the cost of storage by up to 90 percent with automatic policy-based storage tiering that moves data from flash through disk to tape and cloud tiers to help accelerate analytics of new workloads (social and mobile applications), allocating key data to highest performing tiers and lowering costs by moving “cold” data to lower-cost tiers.

For traditional workloads, IBM Spectrum Virtualize* can optimize current data storage environments to new levels of economic efficiency through virtualization, automation and compression technologies,

helping IBM Storwize* systems achieve 3x greater performance with as little as 5 percent of data on flash. Insurance company Prudential, for example, improved its storage utilization by 125 percent through virtualization, allowing its storage administrators to focus on innovation.

The healthcare industry is flooded with patient medical data, and the data volume is expected to grow as new technologies make way for new kinds of data collection. Secure storage of this data—gathered from patient histories, genomic testing, digital imaging, mobile devices and more—poses a challenge for healthcare systems. Not only is the data enormous in quantity, but it’s also sensitive personal information. The University of Pittsburgh Medical Center (UPMC) recently partnered with IBM to improve its data storage following this paradigm. IBM helped optimize UPMC’s existing storage infrastructure using SDS to address utilization of UPMC’s existing storage assets, dramatically reducing its storage infrastructure costs by 72 percent. Data compression and virtualization have allowed UPMC to accelerate transmission of patient medical data without having to add storage capacity. Instead of building another massive data warehouse, UPMC and IBM are working toward the goal of “less storage, less hardware, less space consumed, all of which lead to lower costs.”

Most companies (63 percent) realize a positive return on investment from analytics within one year—UPMC is no exception. The organization has seen quick, long-lasting results. The senior vice president of the Information Services Division at UPMC estimates that by optimizing its infrastructure UPMC has saved $40 million on new storage in the last decade.

Embrace New OpportunitiesIn the cognitive era, companies are flooded with data, but data without the right analytic capabilities is useless. To extract the insights trapped in the data, it needs to be efficiently stored, managed, protected and delivered with speed to the right applications at the right time. To derive cost savings, revenue generation and business growth from big data, businesses must evolve their existing IT infrastructure. Transforming the economics of big data means looking for data solutions that will maximize efficiency and generate real competitive advantage.

With the right approach to storage, your business can easily achieve faster insights and cost savings.

Yael Shani has been an IBM market-

ing professional for 14 years and is

currently leading the marketing of IBM

Storage Big Data & Analytics portfolio.

Thomas Sullivan is a best practices

expert with over 30 years of experi-

ence with data of all types.

Netflix supports

300,000 sub-millisecond transactions per minute with IBM

XIV Storage

TCONow reveals the enduring economics of IBM FlashSystem

The total cost is the true cost of storage

In the past, the cost of enterprise data storage was expressed only in terms of dollars per capacity. Now, IT decision-makers are taking a more inclusive approach. Total cost of ownership (TCO) incorporates a variety of relevant cost factors, including $/GB and operational costs such as electricity, HVAC and data center floor space, plus the value of storage performance, which drives staff productivity and increased CPU utilization, among many other benefits.

TCONow simply provides TCO comparisons

The new Web-based IBM TCONow tool provides quick, easy estimates of the TCO savings offered by IBM FlashSystem compared to conventional disk-based storage. Answer a few simple questions about your IT and business needs, and TCONow will instantly calculate how much your business can save when you deploy an IBM FlashSystem storage solution.

Get a customized quote detailing how you can lower your IT costs while gaining all of the benefits of software-defined storage at the speed of flash.

Go to TCONow: www.cioview.com/FlashAnalysis/

Learn more about IBM FlashSystem: ibm.com/systems/storage/flash