1. W H I T E P AP E R
Ad d r e s s i n g S t o r a g e C h a l l e n g e s t o S u p p o r t B u s i n e s s
An a l y t i c s a n d B i g D a t a W o r k l o a d s
Sponsored by: IBM
Laura DuBois
September 2012
E X E C U T I V E S U M M AR Y
Today, business analytics projects are being initiated to improve business and customer
operations across nearly every business sector while having a transformative effect on
many businesses. Traditional data warehousing and online analytical processing
approaches, combined with new analytical processing run against streaming and real-
time data, enable firms to react dynamically to different customers, users, suppliers, and
other key stakeholders. What was once just information, delivered on a periodic and
point-in-time basis, has now been transformed into insight with data and analysis
available in real time and at any moment.
The benefits that firms can derive from business analytics and big data projects are
broad. Growing customers, identifying the most profitable customers, and measuring
and increasing retention rates are benefits realized by telecommunications providers.
Call centers for firms in many sectors can leverage analytics processes to measure
and improve operational efficiency and/or customer service. Banking institutions can
mitigate business, security, and privacy risks; reduce fraud; and manage compliance.
In addition, healthcare providers can transform and automate financial and other
operational processes as well as personalize patient care.
S I T U AT I O N O V E R V I E W
B i g D a t a a n d t h e F o u r V s
The convergence of intelligent devices (consumer products such as smartphones and
tablets, smart cars, smart buildings, smart infrastructure, etc.), social networking,
pervasive broadband networking, and analytics ushers in a new era for business
analytics that is redefining relationships among producers, distributors, and
consumers of goods and services.
In the past, enterprises only had to deal with a finite and manageable number of data
sources. However, today's business environment includes not only more data but also
more types of data than ever before. The combination of data from a variety of data
sources and in a variety of formats is a key challenge with which business analytics and
big data projects must contend. Another component of big data is velocity, or the speed
at which information arrives and is analyzed and delivered. The velocity of data moving
through the systems of an organization varies from batch integration and loading of data
at predetermined intervals to real-time streaming of data. The former can be seen in
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traditional data warehousing and is also today the primary method of processing data
using Hadoop. The latter is the domain of technologies such as complex event
processing (CEP), rules engines, text analytics and search, inferencing, machine
learning, and event-based architectures in general. Successful analytics projects require
the right information at the right time with the right degree of accuracy.
In the context of big data, value refers to both the cost of technology and the value
derived from the use of big data. Value can be broadly seen from both an infrastructure
perspective and a business perspective. Business benefits can include operational
efficiency and business process enhancements. Operational efficiency gains are
measured by a reduction in labor costs due to more efficient methods for data
integration, management, analysis, and delivery. Business process enhancements are
measured by an increase in revenue or profit due to new or better ways of conducting
business, including improvements to commercial transactions, sustainable management
of communities, and appropriate distribution of social, healthcare, and educational
services. The fourth attribute of big data is volume. Big data projects tend to imply
terabytes to petabytes of information. However, some industries and organizations are
likely to have mere gigabytes or terabytes of data as opposed to the petabytes or
exabytes of data for some of the social networking organizations. Nevertheless, these
seemingly smaller applications may still require the intense and complex information
processing and analysis that characterize big data applications.
S t o r a g e C h a l l e n g e s w i t h B u s i n e s s A n a l y t i c s
a n d B i g D a t a W o r k l o a d s
Today, storage organizations must manage the explosive growth of storage
infrastructure and capacity while reducing the costs associated with growing data
sets. Data volumes tend to double annually. However, while primary data continues to
grow, IT budgets and the number of IT resources to manage this increasing capacity
remain flat. Growth in corporate data comes, in part, from increasing numbers of
corporate connected devices, new data from social applications and programs, and
the desire for more real-time and dynamic information across the enterprise.
In addition to continued data growth, firms face legal, regulatory, and business
imperatives to retain data from a variety of different content sources. Retention of this
data allows firms to preserve institutional memory. In addition, the data is available to
provide business value in the future. Increasingly, firms are leveraging historical or
fixed content data for the purposes of data analytics. Industries such as healthcare
leverage archived data of patient healthcare records and other clinical research to
study mortality rates. Telecommunications providers must retain call records for
prescribed periods of time, but they are also analyzing this data for future customer
behaviors and improvements to customer service. Financial services, healthcare
providers, and insurance firms leverage large data sets to detect and isolate fraud.
However, storage infrastructure teams must not only address infrastructure
challenges of data growth and the retention of fixed content for longer periods of
time but also respond to business demands more quickly. These business demands
can come in the form of new customer applications, new business programs, and
new business analytics projects — all requiring scalable, optimized, and resilient
storage infrastructure.

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O B S T AC L E S T O S U C C E S S F U L B U S I N E S S
AN AL Y T I C S P R O J E C T S
Successful business analytics engagements require a broad and deep set of skills and
capabilities including business and content analytics software, information integration
software to bridge information from disparate data sources, business analytics services,
and the right storage infrastructure. To support business analytics effectively, storage
and infrastructure professionals must continue to seek ways to more economically and
effectively store data while ensuring that scalability and resiliency objectives for
business analytics workloads are not only met but also exceeded.
The volumes of information about business analytics solutions and best practices
frequently neglect to highlight the impact of hardware infrastructure on the success of
business analytics projects. The assumption is that business analytics represents a
single, homogeneous, enterprisewide requirement. This assumption leads to many
market misconceptions that result in suboptimal system performance, rigid architecture,
and costly maintenance — in other words, failed projects. The reality is that:
 Business analytics is an umbrella term that federates multiple related workloads,
end-user decision support and automation requirements, and high-performance
compute and storage technologies.
 A combination of scale-out and scale-up server and storage infrastructure may
support data collection and analysis. No one approach will address all use cases.
 Enterprise customers must consider how best to support complex workflows with
a range of server and storage deployments. The placement of IT resources often
influences the latency and performance of the overall end-to-end workload. In
addition, the network infrastructure has strong bearing on the outcome of server-
based and storage-based technologies that support analytics workloads.
I m p o r t a n c e o f I n f r a s t r u c t u r e C o n s i d e r a t i o n s
t o S u c c e s s f u l B u s i n e s s A n a l y t i c s D e p l o y m e n t s
The lack of an effective storage infrastructure strategy for business analytics
workloads can often lead to performance issues, unanticipated costs, and business
unit dissatisfaction.
When the "pool" of data was primarily online transaction processing (OLTP), then the
generation of 1s and 0s was the main focus of any IT project, and boosting
performance was the main driver of new innovation for that project.
Today, no one person or business department can absorb and analyze all of the data
that is being generated. In fact, multiple sources of data are giving IT organizations
something to think about: engineering data, healthcare data, transportation/logistics
data, and — most noticeable — social media data generated by Web sites and
mobile phones. So, new approaches must be developed to gather the multistructured
data and to store it and analyze it in a timely way.

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I B M S M AR T E R S T O R AG E F O R B U S I N E S S
AN AL Y T I C S W O R K L O AD S
IBM is accelerating its Smarter Computing initiative by enhancing the scalability,
optimization, and resiliency features of its storage solutions, which, together with IBM's
technical computing systems, are the foundation of business analytics. IBM has a
strategic approach to designing and managing storage infrastructure for greater
automation and intelligence. These offerings help enterprise customers achieve faster
analytical results and meet growth objectives while offering improved economics for
business analytics workloads.
However, business analytics infrastructure evaluation and purchasing depends on many
variables. Table 1 highlights some of the variables that an organization faces and how
they map to IBM Smarter Storage offerings to optimize business analytics workloads.
T A B L E 1
A n a l y t i c s W o r k l o a d a n d I n f r a s t r u c t u r e C o n s i d e r a t i o n s
Analytical
Considerations
Business
Considerations
Infrastructure
Design Point
IBM Smarter Storage
Features
Analytical Workload Considerations
Online analytical
processing (OLAP)
Speed of query output versus
ad hoc flexibility
Speed to build cubes or cube
metadata
Tiering, use of solid state
drives (SSDs), high-
performance storage
Deep analytics Ability to consider all
information necessary for
deep insight
Scale and complexity;
petabyte-class data handling,
complex joins, read heavy
Storage virtualization,
scale-up and scale-out
storage
Operational analytics Speed of insight required for
business processes,
especially for customer service
Concurrency of users and
latency of data access and
computation
Tiering, use of SSDs, storage
close to the compute layer
Workload Characteristic Considerations
Data variety, velocity,
and volume
Ability to trust data for implicit
decision making
Rapid, reliable data ingest;
storage management
Storage management, unified
storage, compression, thin
provisioning, and other
efficiency features
Variety of analytics,
integration of models,
analysis and model
output
Speed of results versus
accuracy
Interprocessor
communications, network
bandwidth
Tiering, use of SSDs, storage
close to the compute layer

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T A B L E 1
A n a l y t i c s W o r k l o a d a n d I n f r a s t r u c t u r e C o n s i d e r a t i o n s
Analytical
Considerations
Business
Considerations
Infrastructure
Design Point
IBM Smarter Storage
Features
Organizational Deployment Considerations
Number of users and
access method
Quality of service (QoS),
service-level agreement
(SLA), and ability to take
action at time of impact
Concurrency, network
bandwidth
Self-optimizing data
placement
Interactive analytics or
information "push"
Real-time dynamic analysis
versus static analysis
Resource management, I/O
throughput, provisioning
Storage management,
storage virtualization, tiering,
use of SSDs
Insights outside the
enterprise with
customers and partners
Value chain efficiency,
customer satisfaction
Security and provisioning Storage virtualization
Source: IDC, 2012
For a successful analytics engagement, firms must recognize that business analytics
is tightly coupled with storage infrastructure. To achieve maximum success, firms
must create a scalable, efficient and trusted information system and storage
foundation that improves IT economics and optimizes analytics workload
performance. The storage infrastructure must be able to support and optimize
workloads that are satisfying complex decision making, identifying trends and outliers,
and predicting outcomes using high-performance parallel technologies. In addition,
resilient architectures are important in supporting analytics at scale, supporting
mission-critical reliable systems that handle large numbers of users securely and
seamlessly.
S c a l a b l e
Today, different types of analytics, including online analytical processing (OLAP),
data warehousing, streaming data, and time series and deep analytics, need distinctly
different compute and storage resources that are highly scalable. Creating a scalable
and efficient storage foundation improves IT economics and optimizes analytical
workload performance using all available data and information. IBM Smarter Storage
is scalable and efficient by design, providing the core capabilities needed for smarter
analytics, including:
 Compression. IBM's Real-time Compression solution, which can be
implemented in the controller or a separate appliance, can compress active
primary data, offering a reduction of up to 40% in the cost per terabyte. Analytics
workload data and, in particular, streaming data can scale dynamically and within
the current storage frame and at a cost-effective price per gigabyte.

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 Scale-out storage. IBM supports scale-out block and file storage architectures
that allow for horizontal scaling performance and capacity as I/O and storage
needs dictate. Nondisruptive scaling can be done while the infrastructure remains
online, and minimal involvement by storage teams is required; thus, analytics
processes are not impacted.
 Storage utilization. IBM storage efficiency features such as thin provisioning,
storage virtualization, and storage tiering can provide for optimal storage
utilization. Storage utilization can be increased by as much as 50%, further
scaling existing storage infrastructure for high-growth analytics workloads.
O p t i m i z e d
Analytics anytime and anywhere requires an optimized system to support analysis at
any moment. Optimized systems are tuned systems that allocate the right resources
at the right time. Storage too can be optimized to ensure the highest storage
utilization and the least cost. IBM Smarter Storage is self-optimized, providing the
core capabilities needed for business analytics, including:
 Optimal data placement. Supporting real-time and complex analytics requires
the ability to optimally place data in the right storage tier to meet performance
requirements. IBM Easy Tier, a feature of the DS8000, Storwize V7000, and
SVC, offers a 3x IOPS performance improvement with only 3% of data on solid
state drives (SSDs).
 Self-tuning. Analytics projects are focused on data integration and data analysis.
Given the dynamic nature of analytics projects, complex storage infrastructure
requiring manual overhead is not desired. Storage should be self-managing once
initial setup has occurred. Storage should dynamically expand as needed, and
data should be balanced across all resources in the system. IBM storage
includes many self-tuning capabilities, such as the IBM XIV Storage System's
automatic data distribution capability, which eliminates traditional storage
management tasks.
R e s i l i e n t
Encouraging data-driven decision making requires a resilient IT infrastructure that can
support proliferation to a large number of users seamlessly and securely. In deploying
analytics, resilient architectures can be either on premise or in the cloud. Both
resiliency and virtualization are key to being cloud agile, a major pillar for IBM
Smarter Storage solutions. IBM Smarter Storage enables enterprises to achieve both
resiliency and accessibility for their analytics workloads.
 Storage virtualization. IBM SAN Volume Controller and SmartCloud Virtual
Storage Center virtualize storage resources. IBM storage also includes built-in
virtualization to ease cloud deployments.
 Provisioning automation. Firms can enable analytics at the point of impact with
automation of IaaS with the storage service catalog, which links user
requirements and IT capabilities.

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 Policy-based controls. IBM Active Cloud Engine enables easy creation and
enforcement of file policies.
 Protection, recovery, and retention. IBM tape, disk-based backup systems,
and backup and archiving capabilities include industry-leading tape innovation
and support for policy-based automation.
C H AL L E N G E S / O P P O R T U N I T I E S
Firms seek to harness the power of analytics for efficiency, innovation, or control.
However, organizational goals need to be understood, user requirements need to be
defined, data sources and types need to be identified, the right storage and compute
infrastructure for IT and business unit teams needs to be selected, and ongoing
programs need to be established to continuously reevaluate all of the preceding
factors.
IBM has a broad portfolio of market-tested products and services to address business
analytics requirements. Its offerings include infrastructure and software that have
been optimized to support analytics workloads. But IBM will clearly be competing with
other large companies that see the same opportunity and with a range of smaller
companies that can work to disrupt the "status quo" and to upend the traditional
business with new technologies and approaches to analytics. IBM's range of business
analytics offerings, such as business and content analytics software, information
integration, and IT infrastructure for business analytics, can differentiate IBM from
other companies.
R E C O M M E N D AT I O N S
Storage infrastructure cannot — and should not — be an afterthought. As customers
who have adopted business analytics can attest, the most flexible, scalable, and
resilient analytics systems have been developed — and put into production —
through thoughtful implementations. Organizations across the intelligent economy
should consider the following best practices:
 Develop a business analytics strategy (or review an existing business analytics
strategy) that includes the IT infrastructure. Firms must address strategy
components such as decision types, decision makers, metrics and KPIs,
information latency requirements, data sources and data types, and the
technology and services.
 Recognize that one size (technology) does not fit all (business analytics
requirements). Different workloads, data types, and user types are best served
by technology that is purpose built for a specific use case. Consider the storage
infrastructure for different analytical workloads. There is an opportunity to deploy
storage infrastructure that is optimized for the specific software and the use case.
 Determine infrastructure and storage requirements in parallel. Although business
users are likely to provide most of their input with regard to the software
requirements, IT groups must ensure that hardware infrastructure selection does
not become an afterthought. For example, real-time access to data to perform

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rapid scenario evaluation may warrant a different storage strategy than
accessing petabytes of data. Companies such as Vestas, Bank of America, and
Walmart have reached petabyte scale.
 Consider the performance impact of enabling storage infrastructure. Choices
such as whether to use in-memory computing or MPP analytic databases,
appliances, or separate components will have a material impact on the storage
strategy as well as the business analytics solution.
 Carefully evaluate and repeatedly test the "feeds and speeds" of the analytics
infrastructure as the project scales — in terms of both the size of the system
infrastructure and the amount of data to be analyzed or the number of users
gaining access to the data. Key considerations include capacity of servers and
storage devices, amount and size of data caches, and latency-associated
internode or intranode transfer. Thought should be given to practices such as
deduplication of redundant data and integrity checking to optimize resource and
ensure valid analytical results.
C O N C L U S I O N
Today, storage and infrastructure teams are being called upon to not only manage
the current infrastructure but also support both existing and new business analytics
projects. Business analytics projects are supporting concrete business needs and
providing actionable information to decision makers, including executives, line-of-
business employees, and automated systems.
Yet the storage technology and infrastructure supporting business analytics is
paramount to the success of the project. Optimization, resiliency, and scalability of
storage infrastructure can make the difference between success and problems with
an analytics project. With the large volumes of high-velocity, multistructured data that
firms must mine and analyze, a business analytics project can be fraught with
problems.
Storage teams, IT executives, and business users will benefit by recognizing that
deploying appropriate storage infrastructure to support a wide range of business
analytics workloads will require constant evaluation and willingness to adjust the
infrastructure as needed. That means that flexibility in design is a key consideration.
The responsiveness of the resulting systems is highly important to the success of
analytics projects. The amount of time it takes end users to find their business
"answers" is key to project success and to business users' perception of the quality of
the internal IT group's performance.
IBM is in an advantageous position in offering a breadth of solutions for today's
business analytics projects. However, IBM is going a step further in accelerating its
Smarter Computing initiative by enhancing the scalability, optimization, and resiliency
features of its storage solutions, which, together with its technical computing systems,
offer a strong foundation for business analytics — both today and tomorrow.

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This document was developed with IBM funding. Although the document may utilize
publicly available material from various vendors, including IBM, it does not
necessarily reflect the positions of such vendors on the issues addressed in this
document.
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