W H I T E P AP E R

R e t h i n k i n g Y o u r D a t a R e t e n t i o n S t r a t e g y t o B e t t e r E x p l o i t t h e B i g D a t a E x p l o s i o n

Sponsored by: Dell

Richard L. Villars Marshall Amaldas

October 2011

I D C O P I N I O N

The continued generation of business-critical semistructured data (including large

volumes of machine-generated data [MGD] from smart sensors and mobile devices)

is changing the storage dynamic in a wide range of industries and organizations.

Making investments to extract value from this expanding pool of information is fast

becoming a core business mandate, but such efforts can quickly lead to spiraling IT

costs and growing corporate risk without the right data retention and long-term

archiving strategy.

Making the wrong choice in a technology decision (e.g., deciding between an OLTP,

OLAP, or OLDR approach to data storage) can lead to significantly high data

management and retention costs in both the short run and the long run. It can also

jeopardize compliance and privacy standards for data such as call detail records

(CDRs) and trading records. IT organizations need to deploy active archival storage

solutions that address the total cost of ownership (TCO) for archival data at many

layers. Specifically, such a solution:

Provides a semistructured archive platform that's significantly less expensive

than archiving that same information on individual database, data warehouse, or

file systems

Maximizes the utilization of that hardware with intelligent data

management/reduction software

Reduces the ongoing operational burden of the archival storage environment

When selecting a storage and data management partner to help you manage the

"Big Data" challenge, you will need a partner that can address the entire spectrum of

data assessment, data retention, and data use requirements of this new environment.

Dell, as a leading designer and provider of IT solutions optimized for Big Data

analytics, is also providing enterprise-class solutions that address the cost,

performance, and intelligence requirements at the heart of Big Data retention and

active archiving.

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I N F O R M AT I O N E V E R Y W H E R E , B U T W H E R E ' S T H E K N O W L E D G E ?

For the first 40 years of the IT industry, the main data challenge for most organizations

was enabling/recording more and faster business transactions, often referred to as

structured data. Today, much of the focus is on more and faster exchanges of

information (e.g., documents, medical images, movies, gene sequences, data streams,

tweets) from scale-out cloud clusters to systems, PCs, mobile devices, and living

rooms. This information is often categorized as unstructured data (e.g., image, audio, or

video files) or semistructured data (e.g., emails, logs, call detail records).

Semistructured data is often overlooked, but with the advent of RFID tracking, smart

sensors, mobile devices with geospatial information, and a growing array of data

collection devices, MGD will be a leading driver of the data explosion.

The business challenge for the next decade will be finding ways to better analyze,

monetize, and capitalize on all this MGD (see Figure 1). It will be the age of Big Data.

For the IT organization, the challenge will be to implement an archival storage system

that ensures that this information is reliably and efficiently ingested, protected,

organized, accessed, and preserved.

F I G U R E 1

C h an g i n g B u s i n e s s P r i o r i t i e s i n a F a s t - S h i f t i n g W o r l d

Source: IDC, 2011

MORE

APPLICATIONS

MORE

DEVICES

MORE

CONTENT

MORE

DATA

The range of information created, accessed, and retained affects how

companies organize datacenters and retain information.

Companies rely on a growing range of devices, data sources, and

applications to compete in today's evolving business environment.

Facebook

salesforce.com

Apple

VMware

©2011 IDC #230747 3

T h e O n g o i n g D a t a E x p l o s i o n

Data creation is occurring at a record rate. In 2010, the world generated over

1 zettabyte (ZB) — that's 1 million petabytes (PB) — of data; by 2014, we will

generate 7ZB a year. While much of this is "unsaved" or highly duplicated data like

personal photos or copies of music/videos, one of the fastest-growing and most

important sources of growth is machine-generated data:

Financial transactions. With the consolidation of global trading environments

and the greater use of programmed trading, the volume of transactions that need

to be collected and analyzed can double or triple in size, while the transaction

volumes can also fluctuate much faster, more widely, and more unpredictably,

and competition among firms forces trading decisions to be made at ever smaller

intervals.

Smart instrumentation. The use of intelligent meters in "smart grid" energy

systems that shift from a monthly meter read to an "every 15 minutes" meter read

can translate into a multi-thousandfold increase in data generated. Similar data

bursts are looming in healthcare, where low-cost gene sequencing will have a

profound impact on medical data volumes.

Mobile devices. Until quite recently, the main data generated on landline and

traditional mobile phones was limited to CDRs with caller, receiver, and length of

call data. With smartphones and tablets, additional CDR data to harvest includes

geographic location, text messages, browsing history, and (thanks to the addition

of accelerometers) even motions.

All of this data creates new opportunities to "extract more value" in sectors such as

energy, human genomics, healthcare, retail, online search, surveillance, and finance,

as well as many other areas. IDC believes that organizations that are best able to

make real-time business decisions based on machine-generated data streams at the

lowest possible cost will thrive, while those that are unable to embrace and make use

of this expanding data source will increasingly find themselves at a competitive

disadvantage in the market. This situation will be particularly true in industries that are

experiencing high rates of business change and aggressive consolidation.

B i g D a t a V a l u e : W h a t ' s i n I t f o r M e ?

Regardless of industry or sector, the ultimate value of Big Data implementations will

be judged based on one or more of three criteria:

Does it provide more useful information? For example, a major retailer might

implement a digital video system throughout its stores, not only to monitor theft

but also to implement a Big Data pattern detection system to analyze the flow of

shoppers — including demographical information such as gender and age —

through the stores at different times of the day, week, and year.

4 #230747 ©2011 IDC

Does it improve the fidelity of the information? For example, a number of

earth science and medical epidemiological research teams are using Big Data

systems to monitor and assess the quality of data being collected from remote

sensor systems; they are using Big Data not just to look for patterns but also to

identify and eliminate false data caused by malfunctions, user error, or temporary

environmental anomalies.

Does it improve the timeliness of the response? Consumer products

companies can use kiosks like Coca-Cola's Freestyle to collect real-time consumer

taste preferences in different regions. This move makes it easier to tune

promotions and control inventory levels on a regional or even store-by-store basis.

Big Data Analytics Versus Retention: Distinct Solutions for Distinct

Needs

Today, a number of Big Data analytics solutions use a combination of open source

software frameworks such as Hadoop and MPP (massively parallel processing)

hardware architectures to support compute- and data-intensive applications that can

consume multiple petabytes of disk storage across thousands of individual server

nodes. Both hardware and software components of such analytics systems are

optimized for performance where the data distributed over multiple nodes is kept

redundant for resiliency and high-availability reasons.

The MPP architecture–based systems are designed such that compute and storage

are tightly coupled to minimize contention for resources. While these solutions are

best suited to run complex large-scale analytics where performance is the prime

objective these systems are not suitable targets for long-term retention of big data

content.

A key element in all these use cases is that organizations must be able to continually

go back and reanalyze the same machine-generated data sets over and over again.

They need to continually look for patterns stretching over hours, days, months, and

years. If it's too expensive to retain the needed historical data or too difficult to

organize the data for timely, ad hoc retrieval, organizations won't be able to capitalize

on their collected information. The key question you need to be asking is whether

your current storage environment can handle this new data explosion and the data

retention challenges it will create. Traditionally, MGD was treated like either

structured or unstructured data sets:

1. It was maintained in a database or data warehouse (leveraging SAN-attached

storage), which is very expensive and can significantly impact performance,

unless an organization used the archiving functions (not also provided) for each

application. In this approach, the data is also trapped in a single application

environment and is difficult to repurpose/reuse.

2. It was pushed down as a blob (sometimes aptly called a TARball) onto a file

system to be retained. In this approach, an organization sacrificed the structure

detail, significantly impacting the querying and analytical ability and, once again,

the ability to repurpose/reuse. Because MGD was often linked to a tape library, it

posed significant data retrieval burdens.

©2011 IDC #230747 5

3. It was kept as a set of personal files on a file server or NAS device and then

either orphaned (when the owner left) or deleted. In both cases, the ability to

access the data and to manage its retention/disposal for regulatory reasons was

severely compromised.

Failing to make use of systems that are built specifically for meeting the long-term

retention and compliance needs of MGD data pools will make Big Data analytics

ambitions cost prohibitive and risky. You need a Big Data retention solution like Dell's

Big Data Retention, which recognizes MGD as historic at creation and immediately

commits MGD to an intelligent, long-term, online retention pool. This approach

eliminates much of the high cost associated with databases/SAN storage while

maintaining the critical data context that is lost in NAS environments.

Table 1 provides a set of questions that can help you decide whether you have a

Big Data retention problem. If you recognize any of these issues, you need to start

thinking about your current data retention strategy and how you can move to a more

active archival storage environment.

T A B L E 1

D o Yo u H a v e a B i g D a t a R e t e n t i o n P r o b l em ?

Potential Pain Point Key Indicators of a Problem

Extreme volumes of data that in many cases are caused

by automated or machine-generated data sets

Do you have or are you expecting daily data volumes that

outstrip the ingestion capability or storage capacity of your

database or repository?

Are you concerned about the impact of this data growth on

storage budgets?

Out-of-control costs associated with retaining, managing,

and supporting these new data pools

Are some of the data sets that you are retaining automated or

machine generated and typically historic upon creation?

Do you store these data sets in traditional transactional

databases/data warehouses or as big blobs or files?

Do you need to keep more of this data for discovery or

analytics purposes that require the preservation of the

structured data sets (usually lost when they are stored as a

backup file)?

Difficulty managing and supporting disparate silos

(archives and data stores) across the enterprise

Do you need to archive email, files, SharePoint, and other

unstructured data in addition to databases or other forms of

structured data?

Are you concerned about the long-term TCO for either or both

archive platforms used for each environment?

Need to back up (protect) these new data sources is

threatening backup windows for existing applications and

stressing existing backup facilities

Are you having a hard time consistently meeting your backup

and recovery SLA requirements, or are your backups failing

altogether?

Source: IDC, 2011

6 #230747 ©2011 IDC

T H E C H AN G I N G N A T U R E O F AR C H I V AL S T O R AG E I N T H E W O R L D O F B I G D AT A

Data retention via archiving has a long history as an IT practice, stretching back

decades. Archiving of structured data was required for regulatory/contractual

purposes or as a method of maintaining high levels of system performance (removing

inactive data to free up capacity and I/O performance on databases). The archive

data was rarely, if ever, accessed again and was stored on inactive media (e.g.,

tapes). Moving the data was expensive, time consuming, and often tied to the backup

process, which made retrieval after a relatively short time (e.g., 30 to 90 days) time

consuming and difficult.

The rapid growth in digital data triggered by the arrival/explosion of the Internet, along

with a series of business scandals, increased the scope of the data

retention/disposition problem. For IT managers in the past decade, regulation and

compliance requirements mandated that organizations retain semistructured data

(e.g., CDR and trading records) for even longer periods of time. They also mandated

that this archived material be more quickly accessible for eDiscovery purposes.

Concurrently, the move to online collaboration began to generate larger amounts of

emails, office documents, and rich media data, which must also be retained and

archived. These mandates added a further layer of complexity because just storing

the data was not enough. Organizations also needed to preserve the data in context.

Both of these developments drove organizations to greater reliance on active

(HDD-based) storage for their long-term data storage needs. IDC estimates that in

2010, organizations around the world deployed 4,465 petabytes of new disk storage

capacity just to store copies of their data for availability and retention purposes; by

2015, they will be deploying 16,538 petabytes (see Figure 2). While solving the

access time issue, this addition of another disk storage tier often poses even greater

storage asset management challenges.

©2011 IDC #230747 7

F I G U R E 2

W o r l dw i d e E n t e r p r i s e D i s k S t o r a g e C o n s u m p t i o n , 2 0 1 0– 2 0 1 5

Source: IDC's Enterprise Disk Storage Consumption Model, September 2011

M a c h i n e - G e n e r a t e d D a t a a n d B i g D a t a

R e f r a m e t h e D a t a R e t e n t i o n C h a l l e n g e

Today, the rate of data growth and the diversity of data types are reaching

unprecedented levels. The traditional archiving jobs of preservation and active

application offload remain daunting challenges. At the most basic level, the sheer

data volume increase associated with new and fast-growing machine-generated

environments can pose significant archival challenges. The shift to intelligent meters

as part of a smart grid energy system would lead to a 3,000-fold increase in machine-

generated data that a utility would be collecting on a monthly basis.

Certain industries such as financial services (market, trading, and tick data) and

telecommunications (logs, CDRs for lawful intercept) are continuously generating vast

quantities of data at a rate of billions of MGD records a day. With retention

requirements ranging from a few years to indefinitely, the demand for raw storage will

only accelerate unless we figure out how to be smarter about how data is retained.

What's different in the new world of machine-generated data and Big Data analytics is

the need to continually go back and mine this data over and over again. You're not

just retaining it; you're continually reusing it.

Standard database and data warehouse applications aren't optimized to handle

ingestion of such volumes of data, and they are even less suitable platforms from a

cost and performance standpoint when it comes to archiving. At the same time, the

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8 #230747 ©2011 IDC

previously mentioned utility can't just park the data on some tapes. Organizations are

constantly deploying new sets of analytic applications that continually go back and

analyze behaviors (and then make real-time adjustments) on an hourly, daily, weekly,

monthly, and even yearly basis.

The context and techniques for mining that data will change and evolve. Any data

retention solution that locks the retained data into a traditional hierarchical database

or file structure severely impacts the long-term cost of storing the MGD. More

important, it greatly reduces the long-term value of the data.

A c t i v e A r c h i v i n g f o r B i g D a t a

The primary data management challenge associated with Big Data is to ensure that

the data is retained (to satisfy compliance needs at the lowest possible costs) while

also keeping up with the unique and fast-evolving scaling requirements associated

with new business analytics efforts. Organizations that strike this balance will boost

efficiency, drive down cost, and be in a far better position to capitalize on Big Data

innovations.

Firms must be able to mine their historical data to analyze and extract data for market

intelligence, product planning, and inventory planning. In R&D environments, reuse of

historical information can yield vast savings in time and effort, which in turn saves

money and in some cases provides competitive advantage by shrinking the time

required to bring products to market.

Today, many of these Big Data projects are best described as "junior science

projects" with a small core of servers and storage assets. From a business and an IT

governance standpoint, however, these kinds of "junior science projects" can quickly

turn into the next "Manhattan project" with companywide and industrywide business,

organizational, and legal consequences. IT organizations need to deploy active

archival storage solutions that address three major requirements:

Rapid, continuous, and intelligent movement of "instantly historical" data

from the data-generating devices/applications onto the active archive

system. This ability ensures that the source application continues to run at

maximum efficiency in terms of performance and reliability and that the

underlying IT assets (servers and primary storage systems) aren't compromised

by having to support multiple, incompatible workloads.

Flexibility in data ingest capability. The amount of machine-generated

data can vary significantly from time to time, depending on the amount of

activity that is experienced by a monitoring system. Financial trade

monitoring systems can experience very high levels of activity due to an

external event that causes panic, which in turn could trigger a sudden surge

in the number of trades. The active archive target should be able to

accommodate such variation and be able to ingest data at different rates as

required.

©2011 IDC #230747 9

Rapid, nondisruptive scalability of archival storage capacity and I/O

performance. This modularity makes it easier to launch initial, limited machine-

generated data mining capabilities, without jeopardizing the ability to meet rapidly

expanding requirements for capacity and performance. You may outgrow a

specific module, but you never want to outgrow the archival platform. When

you're talking about hundreds of terabytes (TB) to multiple petabytes of

information, migrating to a new platform should not be a necessary option.

Built-in efficiency. Unchecked data growth is bound to become a burden

over the course of time, even on an archive tier. IT organizations need to

look for solutions that take full advantage of proven efficiency technologies

that are purpose designed to make the most out of machine-generated data

to achieve targets.

Flexible, nonhierarchical data organization based on an object-based

storage foundation. This flexibility is critical because one of the key tenets of

Big Data applications is the ability to deal with new and unpredictable data

patterns. Machine-generated data required for one purpose today may prove

absolutely critical for enabling some new analytic algorithm tomorrow. An object-

based approach to storing information eliminates the risk that a data-organizing

approach that makes sense now doesn't render the data difficult to extract or

useless in the future.

Preserve metadata. The metadata attributes of machine-generated data are

much richer than those of other content types, which makes them very

useful for analytical purposes. Having the ability to make sense of

relationships between different data sets using common metadata and

attributes is a key analytical value of the data. IT acquisition decision makers

need to make sure that their choice of storage system does not depreciate

this value.

Because most organizations are new to the subject of active archiving for machine-

generated content, purchase decision makers need to look for solutions and vendors

that place a high emphasis on providing complete service and support throughout the

implementation.

Don't Overlook Data/Information Security/Privacy

As in the case of other content types, regulation and compliance is also an important

consideration of machine-generated data. For example, the USA PATRIOT Act

stipulates strict retention requirements on CDR. Telecommunications organizations

need to make sure that this information is stored such that it is not modified from the

time it is created.

If the data involved is sensitive for reasons of privacy, enterprise security, or

regulatory requirement, then misplacement or misuse of retained data can represent

a serious security breach. More traditional database management systems support

security policies that are quite granular, protecting data at both the coarse-grained

level and the fine-grained level from inappropriate access.

10 #230747 ©2011 IDC

Today, Big Data applications generally have no such safeguards. Enterprises that

include any sensitive data in Big Data operations must ensure that the data itself is

secure and that the same data security policies that apply to the data when it exists in

databases or files are also enforced in the Big Data context. Failure to do so can have

serious negative consequences.

The archival storage environment, as the common retention point for all machine-

generated data, must enable advanced, yet easy-to-leverage, data/information

security capabilities. It must include:

The ability to automatically place specified records on disks that have WORM

capability

Monitoring and reporting capabilities, which will help IT administrators make

informed infrastructure and policy decisions proactively

The remainder of this white paper examines how well Dell's Big Data Retention

solution addresses the need for compliant, enterprise-class Big Data/MGD retention

and on-demand access.

D E L L ' S B I G D A T A R E T E N T I O N A N D A C T I V E A R C H I V E S T O R A G E S O L U T I O N

Dell is a leading provider of IT products and services for organizations around the

world. It provides the computing systems at the heart of machine-generated data

devices. It is also a leader in designing and deploying servers optimized for Big Data

analytics compute platforms that play a key role in monetizing the value of machine-

generated data.

Now, Dell is also providing enterprise-class solutions that will be at the heart of Big

Data retention and active archiving.

Dell set a goal of creating a complete archival solution (hardware, software, and

professional services) that cost-effectively solves the "Big Data" retention/archive

problem and enables a better way to retire and archive legacy applications. For Dell,

cost-effectiveness means addressing the TCO of archival data at many layers:

Providing an MGD/Big Data–optimized archive platform that is significantly less

expensive (and more useful) than archiving that same information on individual

database, data warehouse, or file systems

Leveraging the least expensive hardware (without compromising

performance/reliability)

Maximizing the utilization of that hardware with intelligent data

management/reduction software

Reducing the ongoing operational burden (provisioning, migrating, and

administrating) of the archival storage environment

©2011 IDC #230747 11

The company introduced the Big Data Retention solution in 2011. It's designed to

provide a low-cost, standard foundation for data reduction, long-term retention, and

on-demand data retrieval of historical data (machine generated and all other forms of

semistructured data). Big Data Retention is a single platform for retaining structured,

unstructured, and semistructured data across an unlimited number of data sources,

formats, and types. It is based on a clustered Web storage service that utilizes a

peer-scaling design that can start at 1TB and extend to exabytes. The DX Object

Storage Platform abstracts underlying technology by integrating compute, network,

and storage resources into one delivery unit.

Key characteristics of the Big Data Retention solution include:

Fast deployment with minimal administration overhead and no special tuning:

Minimal administration required compared with specialized DBA requirements

associated with many traditional repositories and ability to rapidly search and

retrieve data using native SQL for seamless integration with existing systems

within the enterprise

Scalable performance with high data ingestion rates and fast queries: Ability to

load big data volumes (billions of records/day, petabytes/month)

Scalable, intelligent archival storage capacity to handle big data volumes: Ability

to dedupe at the structured data value and pattern level, leveraging the resulting

40 to 1+ compression ratio (97%+ reduction in size)

Integrated compliance features such as configurable retention rules and audits:

Ability to set flexible configurable retention and expiry rules for the life cycle of

the data with guaranteed read-only immutability and audited access

Dell provides customers with multiple deployment options for the Big Data Retention

solution. IT organizations can deploy Big Data Retention as an on-premise system

within their own datacenter. In addition, Dell will be providing a cloud-based solution

running in Dell's cloud datacenters.

D e l l D X O b j e c t S t o r a g e P l a t f o r m I s A r c h i v a l

S t o r a g e a t t h e C o r e o f B i g D a t a R e t e n t i o n

One of the key components of the Big Data Retention solution is the Dell DX Object

Storage Platform. The DX allows IT organizations to effectively archive both structured

data (e.g., from RDBMSs) and semistructured data (e.g., logs, call data records, other

MGD) while still supporting reliable query via SQL or any business intelligence tool

using ODBC/JDBC. IT organizations can archive terabytes to petabytes of

semistructured data on the Dell DX Object Storage Platform while minimizing TCO

through the use of advanced data reduction capabilities built into the DX.

Key design characteristics of the DX platform include:

System persistence. Adding/replacing hardware components (e.g., disks or

controllers) is automated and nondisruptive and doesn't require any data

organization changes. Only one physical migration (the original one) on the DX

platform is necessary.

12 #230747 ©2011 IDC

System resiliency. The system is self-healing. If a hardware element fails, you

just plug in a replacement and the system automatically restores what needs to

be restored.

System flexibility. The system supports highly granular file-level management

to enable great effectiveness of data reduction services and employs an open

API so that your data won't be trapped in a single, proprietary environment.

The DX Object Storage Platform makes it possible for IT organizations to ingest

billions of records per day, accumulating petabytes of data per month. More

important, it ensures that this data is properly retained based on legal/governance or

business analytics requirements.

C h a l l e n g e s / O p p o r t u n i t i e s f o r D e l l

Given the continued rapid growth of machine-generated data and the increasing role

of big data analysis in organizations' new application and services plans, the storage

and information management challenges posed by Dell's data-driven customers and

prospects will only increase in the coming years. Dell needs to address a number of

requirements as it expands its role in organizations' active archiving and data analysis

environments:

Continue to improve underlying storage hardware capacity, performance, and

power management efficiencies through more tunable/intelligent automated data

movement and support for even denser/more power-efficient HDD solutions

Establish closer technical and business ties with leading analytics (Big Data)

application suppliers that will make it easier for customers to fully exploit the

information stored within the Big Data Retention archive

Extend the reach of the Big Data Retention solution to better address the active

archive and data mining needs of medium-sized and small businesses through

further expansion of Dell's cloud-based offering

F I N AL T H O U G H T S

When assessing the impact of machine-generated data and supporting analytic

applications on your IT infrastructure, you'll find that the challenges extend from data

creation, to data collection, to data retention, and, finally, to ongoing analysis. This

new environment represents both big opportunities and big challenges for CIOs.

Almost every CIO dreams about making IT a more valued asset to the organization.

Big Data projects are at the frontier of the business, where the majority of the most

significant business expansion or cost reduction opportunities lie. Taking a lead in

leveraging machine-generated data provides the CIO with a chance to be a strategic

partner with business unit leaders.

Because speed is strategically important in many early efforts, it will be tempting for

business unit teams to move forward without IT support. You will find, however, that Big

Data issues emerge at surprisingly low data volumes. They manifest themselves when

the balance between the value of data and the cost of retention becomes an issue.

©2011 IDC #230747 13

Making the wrong choice in a technology decision (e.g., deciding between an OLTP,

OLAP, or OLDR approach to data storage) will lead to significantly high data

management and retention costs in both the short run and the long run.

It will also expose the organization to greater risks when it comes to IT and corporate

governance. Your IT team needs to recognize that it must think differently (as well as

quickly) and fight for a seat at the table as analytic and data archiving strategies are

developed. You need to ensure that the solution:

Reduces infrastructure cost by ingesting and querying large volumes of data on

commodity infrastructure while cutting demand for physical storage through

compression

Retains huge volumes of records without a need to roll up or aggregate while

managing record life cycles through configurable retention policies, preserving a

secure and immutable data model

Retrieves information speedily using standard SQL over ODBC/JDBC, enabling

enterprises to leverage existing business information, reporting, and analytics

investments

When selecting a storage and data management partner to help you, picking the best

product isn't enough. You will need a partner that can address the entire spectrum of

data assessment, data retention, and data use requirements of this new environment.

IDC believes that building successful business cases around the intersection of

machine-generated data and Big Data analysis can be accomplished only through a

tight alignment of critical thinking across both IT and the business. You will need a

partner that can help you capitalize on new initiatives quickly and cost-effectively.

As a CIO, you want to be more involved in the business; creating the right data

management infrastructure for the retention and active archiving of machine-

generated data can bring your IT organization front and center in the next major

business effort.

C o p y r i g h t N o t i c e

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