Information Life Cycle Management

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Information Life Cycle Management

Seminar Report On

INFORMATION LIFE CYCLEMANAGEMENT

Submitted by

PREENA K P

In partial fulfillment of requirements in Degree of

Master of Technology (M-Tech)

In

Software Engineering

DEPARTMENT OF COMPUTER SCIENCE

COCHIN UNIVERSITY OF SCIENCE ANDTECHNOLOGY

KOCHI-682022

2008

Department of Computer Science


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COCHIN UNIVERSITY OF SCIENCE ANDTECHNOLOGY

DEPARTMENT OF COMPUTER SCIENCE

COCHIN-682022

Certificate

This is to certify that the seminar report entitled

INFORMATION LIFE CYCLE MANAGEMENT submitted by Preena

K P , in partial fulfillment of the requirements of the award of M-Tech

Degree in Software Engineering, Cochin University of Science and

Technology, is a bonafide record of the seminar presented by her duringthe academic year 2008.

Dr. Sumam Mary Idiculla Dr. Paulose Jacob

Course Coordinator Head of the Department

Place: Cochin

Date: 20-10-08



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ACKNOWLEDGEMENT

I thank GODalmighty for guiding me throughout the seminar. I would

like to thank all those who have contributed to the completion of the seminar and

helped me with valuable suggestionsfor improvement.

I am extremely grateful to Prof. Dr. K Paulose Jacob, Director,

Department of Computer Science, for providing me with best facilities and

atmospherefor the creative work guidanceand encouragement.

I would like to thank my coordinator, Dr. Sumam Mary Idicula for all help and

support extend to me. I thank all staff members of my college and friends for

extendingtheir cooperationduring my seminar.

Above all I would like to thank my parents without whose blessings I would not

have been able to accomplishmy goal.



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Abstract

A new framework, created to enhance information life cycle

management by clarifying the relationship between value-at-risk and

total cost of ownership, helps IT managers build a winning storage

environment.

Faced with higher storage costs and burgeoning data growth, the concept

of information life cycle management (ILM) has emerged to help management

understandtheir information needs and to structure their storage spending in awaythat meets those needs.

Information Lifecycle Management (ILM) is designed to address these

issues, with a combination of processes, policies, software and hardware so that

the appropriate technologycan be used for each phase of the lifecycle of the data.

The task of measuring the risk to information value and deciding how

additional storagespendingmay be able to reduce that risk to more tolerable levels

is not unlike the task facing fund managers within the financial services sector.

Value-at-Risk, expressedin absolute or relative percentageterms, acts as a trigger

for corrective action but it also showshow much a firm can spend to protect itself if

VaR is exceeded. Total cost of ownership (TCO)is a standard metric for evaluating

storage environments. The critical task is to determine how VaR responds to a

changein TCO.

A frameworkthat enhancesILM by clarifying the relationship betweenVaRand TCO, a relationship that is often in flux because of the dynamic nature of

information value and firms growing desire to capture data on multiple aspects of

their business. By considering this frameworkand VaR in particular, we argue that

IT managerscan forge an effective and secure storageenvironment.



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1. INTRODUCTION

Although most organizations have long regarded their stores of data as

one of their most valuable corporate assets, how this data was managed and

maintained varies enormously. Originally, data was used to help achieve

operational goals, run the business and help identify the future direction and

successof the company.

However, new government regulations and guidelines are a key driving

force in how and why data is being retained, as they are now requiring

organizations to retain and control information for very long periods of time. So

today there is two additional objectives IT managers are trying to satisfy: to store

vast quantities of data, for the lowest possible cost; and to meet the new regulatory

requirementsfor data retention and protection.

1.1 Regulatory Requirements

Previously organizations retained data because they wanted to, today,

many organizations have to retain specific data for specified periods of time.

Failure to comply with these regulations could result in organizationshaving to pay

very heavy fine. Therefore, around the world, a numberof regulatory requirements,

in the US and the European Data Privacy Directive in the European Union are

changing how organizations manage their data. These regulations specify what

data must be retained, whether it can be changed, and for how long it must be

retained, which could be for a period of 30 years or even longer. These regulations

frequently demand that electronic data is secure from unauthorized access and

changes, and there is an audit trail of all changesto data and by whom.



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In the aftermath of the IT Doesnt Matter debate [3], a consensus has

emergedthat if corporate IT assets such as hardware, soft-ware, and networksare

susceptible to replication by competitors, a competitive advantage can only comefrom the information created by such assets [11]. Meanwhile, a precipitous decline

in data storage costs of 45% per annum, on a cost-per-gigabyte basis [6], has

satiated firms desire to capture information on multiple business transactions and

relationships. Moreover, the much anticipated adoption of RFID and recent

regulatory reforms such as SEC Rule 17a- 4, mandating the retention of

electronic communicationsrecords (email, VoIP, and IM) in securities trading firms

for up to three years, are likely to accelerate the pace of data accumulation even

further. In information-rich sectors such as credit card lending, retail, and health

care, data growth has already begun to outstrip the decline in hard-ware costs,

prompting a net increase in storage spending [8]. With storage now consuming

12%15%of IT budgets, CIOs fear that further increases could erode strategic IT

spending[4].

2. WHAT IS ILM?

Information today comesin a wide variety of types, for exampleit could be

an email message, a photograph or an order in an Online Transaction Processing

System. Therefore, once you know the type of data and how it will be used, you

alreadyhave an understandingof what its evolution and final destiny is likely to be.

The challenge now before all organizations, is to understand how their

data evolves, determine how it grows, monitor how its usage change over time,

and decide how long it should survive. Whilst adhering to all the rules and

regulationsthat now apply to that data.

Faced with higher storage costs and burgeoning data growth, the concept

of information life cycle management (ILM) has emerged to help management



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understandtheir information needs and to structure their storage spending in away

that meets those needs

Changesin the storage infrastructure at most companiesover the last five

years are also driving interest in information life-cycle management (ILM). The

transition from host-based to network-based storage (both fabric-attached and

network-attached) and the development of new storage technologies (i.e., Serial

ATA) have led IT managers to tier their storage environment to deliver the proper

balance of performance, data accessibility, cost, and data reliability for different

classesof data accordingto their value to the business.

The growingimportanceof ILM comesfrom the realization that the value of

data to a company changes over time and can vary between different users or

departmentswithin an organization. If information about the changingvalue of data

can be harnessed, then that information can be used to better manage data and

storage resources. While placing data on the most appropriate storage resource

over its life cycle makes a lot of sense, many challengesneed to be addressed to

implementan ILM solution.

A universal challenge that IT organizations face is understanding what

data and storage resources they have in their environment. Although storage

administrators often know how much total capacity has been allocated to

applications and departments, they are less familiar with the different types of data

created by each departmentand end user, the growth of these different data types,

how much data is stale or most active, or how much capacity is available and used

at each storage location. A tougher challenge is determining the value of different

sets of data at any point in time. The same data set will changeits value over time

and different departmentswill value the same set of data differently. Therefore, the

value of data needs to be reassessed periodically and allowed to vary across an

organization.



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Once data has been identified and classified based on its relative value,

the next challenge is matching data to the most appropriate storage resource.

Placing data on the right storage device involves moving files from the originallocation where they were stored to a new storage location. End users and

applications, however, need ready access to their data as it is moved from location

to location over its life cycle. File movement, therefore, has to be done

transparently to users and applications. Once files are moved, managing backup

and recovery can also becomean issue. If administrators do not have an accurate

record of where files have been moved, then they will not know what servers,

volumes,and directories need to be includedin the organization's backupprocess.

Information Lifecycle Management (ILM) is designed to address these

issues, with a combination of processes, policies, software and hardware so that

the appropriate technologycan be used for each phase of the lifecycle of the data.

The underlying premise of ILM is that information follows a natural lifecycle from capture throughapplication and decline.

At each point in its life cycle, the issue for ILM is to identify the value of its

information and how best to protect that information from loss and corruption. In

this way, storage is like an insurance policy whose cost mirrors the value of the

underlying asset and the risk that value will decline due to adverseevents.



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2.1 Stages of ILM

Stage of ILM Information value

StorageArchitecture

Storage Concerns &Issues

1. Capture LowNear-line

storage

Ownershipof data

Creation policies

2. Application High Primarystorage

Available capacity

Accessand performance

Backupand recoverytimes

Efficiencyand utilizationSecurity and access

3. DeclineMediumto

LowTape, optical

Retention policies

Security and access

Unfortunately, the complex task of valuing information has forced firms toapply ILM using cost criteria alone. Thus, firms tend to spend more on fault-tolerant

hardwareand backup monitoring for data in regular usetheperception being that

frequent use implies higher value. Data that is no longer in use or that is perceived

to have lower value is archived onto inexpensive media such as magnetic tape or

are deleted outright. Intel, for example, uses a 35-day email retention policy for its

employees; after this period, email messagesare automatically deleted regardless

of their perceivedvalue to the end user or the firm as a whole.



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3. CONSIDERATION OF RISK

A problem with this cost-centric approach is its failure to consider risk. In

the event of a systems or media failure, time to recovery and point of recovery (that is, the age of the last backup)may vary widely.

For example, hot sites routinely provide synchronous mirroring of data in

real time but this entails a much greater level of investment than RAID devices or

periodic backing up to tape. Delayed recovery may not be an issue for low-value

data such as pay-roll records or social calendars but for critical data such as stop

loss orders in a brokerage firm, any delay could prove embarrassing and lead to

severe financial penalties. From a legal viewpoint, there is also a possibility that

courts will order that electronic records be provided to opposing counsel within a

certain time frame; failure to comply with such orders can prove costly [12].

If risk is overlooked, firms have no way of knowing if they are spendingtoo

much or not enough on protecting their data. In an era of regulatory oversight and

paranoia over data loss, firms are unlikely to be risk seekers. Storing high-valuedata on unreliable, albeit less expensive media constitutes a risk that even the

most recklessfirms are unlikely to accept. For risk averse firms, a desire to have all

data recoverable in real time is impractical and cost prohibitive, especially if the

volumeof data is expectedto rise sharply.

Risk neutrality represents a compromise where firms are neither exposed

to inordinate levels of uninsured risk nor spending vast sums on storage solutions

to eliminate risk entirely. Risk neutrality does not mean that firms are indifferent to

risk; rather, they are cost-effectively insured against all knownrisks.

In theory, risk neutrality assumesperfect knowledge of all adverse events

that could put the value of information at risk. For example, firms must know the

probability of calamitous events such as terrorist attacks or natural disasters and

the probability that the storage systems and backup media will perform as



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expected. In practice, bounded rationality and less-than- perfect foresight means

that all risks will never be fully quantifiable.

The best that a firm can do is to review historical outage patterns and

extrapolate to a level of storage spending that protects against as many future

adverse events as possible. In reality, this task is rarely performed with any

appreciable degree of accuracy and so, adapting from a similar problem facing

fund managementfirms, we outline a frame-work that balancesthe risk associated

with data loss or corruption against storage spending that is meant to prevent or

contain such adverseevents.

4. VALUE-AT-RISK

The task of measuring the risk to information value and deciding how

additional storagespendingmay be able to reduce that risk to more tolerable levels

is not unlike the task facing fund managers within the financial services sector.

Fund managers know that fund values will vary based on market conditions.Managers are willing to accept some losses but only within predefined limits. To

help establish this limit, fund managers apply value-at-risk (VaR), a measure that

summarizes the worst loss over a target horizon with a given level

of confidence [9].

For example, using historical data, managersmay determinethat with 99%

confidence, the worst percentage loss a fund is likely to suffer is 5% or $5M on a

$100Mfund. If, on any given day, the fund value falls by more than 5%, managers

may opt to use a hedgingstrategy to guard against further losses. VaR, expressed

in absolute or relative percentageterms, acts as a trigger for corrective action but it

also shows how much a firm can spend to protect itself if VaR is exceeded. For

example, if a 6% decline in the value of a fund creates a $1M loss above what is

expected at a 99% VaR level, fund managers know they can spend up to $1M to



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neutralize this loss. In practice, it is impossible to insure against all adverse market

events but setting a VaR level at 95% or above gives managers an opportunity to

identify how the most severe market perturbationsmight damagetheir portfolio.

To see how VaR can be adapted to a storage environment and used to

understandthe risks facing a firm and the value of its information, consider Figure I

showinga typical distribution of storage-related risks.

4.1 Probability distribution of adverse storage events

Figure I

Recognizing that valuing adverse events is not an exact sciencefor

instance, it can be difficult to accurately assess how much an hour of CRM

downtime costs a firmit is nevertheless possible to create a probability

distribution of storage-related events and their cost to the firm from backup and

restore logs, help desk tickets, and end-user surveys. As seen on the left side of

the diagram, most events are of minor significance and have no lasting effects on

the firm; accidental deletions and restoring earlier versions of files are typical

examples.

Meanwhile, other events can disrupt businessactivities leading to losses in

the form of missedsales, court-imposedfines, or expedited data recoveryfees. For

example, in 2005, Morgan Stanleys failure to report email messagesas part of an

investor lawsuit led to a jury award of $1.45 billion (this was reversed on appeal in

early 2007) [12]. Lastly, a handful of events can be catastrophic if they directly

threaten the survival of the firm.

Events such as the terrorist attacks of September 11, 2001 or natural

disasters have been known to destroy data centers (as happenedin New Orleans

following Hurricane Katrina) meaning that firms may have to bear significant


Frequency

Extent ofLossLow Medium High

VaR(95% confidence Level)

BusinessConsequences

MinorInconvenience

BusinessDisruption

CatastrophicRepercussions


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additional cost in re-creating files from transaction-level data. Using this

distribution, firms can compute VaR at, for example, the 95% confidencelevel. For

the time period coveredby this data, each firm must then decide whether their levelof storage investment is reasonable, given the worst-case scenario that VaR

represents. A firm that previously tried to reduce its storage spending by migrating

data to less expensivemedia or using less frequent backupsmay find that VaR has

jumped as end users face longer recovery times and greater disruption. On the

other hand, a firm may find that an earlier decision to increase spending by

adopting more reliable technology or to pursue a strict backup regimen has

contributed to a decline in VaR in the current period. In a financial services setting,

the VaR on an investmentcan be altered with hedging strategies but only at a cost

to the firm [9]. In the case of information value, VaR can be manipulated through

storage spending. As firms assess the risk to their informationfrom adverse events

such as hardware failure or data corruption, their goal must be to link their current

storage spending to VaR in order to decide if their current spending is too low, in

which case VaR is dangerously high, or if spending is excessive, in which case

VaR is unnecessarily low.

4.2 Total Cost of Ownership

Total Cost of Ownership (TCO) method is a technique which can be used

to make sure that all associated costs over a given time period are considered

when we are acquiring an asset

TCO can be described as all costs of owning and operating an asset over

time. TCO does not only reflect the costs of purchase. It also includes all other

aspects in the further use and maintenanceof the asset.

TCO was originally developed in the late 1980s by research company

Gartner to determinethe cost of owning and deployingpersonal computers.

There is no broad accepted formula for TCO. The main thought behind is

that we need to consider all relevant costs which are related to an asset



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Dependingof the IT deploymentthe followingelementscan be includedin the

Total Cost of OwnershipTCO:

End-user computerHardwarepurchasecosts

Softwarelicense purchasecosts

Hardwareand SoftwareImplementation/ deploymentcosts

Hardwarewarrantiesand maintenancecosts

Softwarelicense tracking costs

OperationsInfrastructure Costs

Infrastructure (floor space) costs

Cost for electricity and cooling

Networkhardwareand softwarecosts

Server hardwareand softwarecosts

Testing costs

Cost to upgradeor scalability

IT Personnel costs

Backupand RecoveryProcesscosts

Costs associatedwith failure or outage

Costs of security breaches(in loss of reputation and recoverycosts)



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Technologytraining costs of users and IT staff.

Audit costs

Insurancecosts

Replacementcosts

Migration costs

Decommisioningcosts

5. LINKING VAR TO STORAGE COSTS

Since many firms have felt compelled to pursue ILM on the basis of cost

criteria alone, total cost of ownership (TCO) is a standard metric for evaluating

storage environments. Fixed and variable costs are accumulated for a defined

period of time and divided by throughput, numberof users, data center capacity, or

footprint to yield a measure of cost utilization. Although hardware costs are less

than 30% of TCO[2], vendors continue to market hardwareas a way to lower TCO

while, in reality, service or labor costs (least impacted by innovation) are the

primary factors in TCO. Chargebacksystemsroutinely use TCO to assign storage

costs to end users and so these costs are identifiable with some degree of

accuracy.

Relating storage TCO to VaR is complex. The critical task is to determine

how VaR responds to a change in TCO. For firms to be fully insured against

adverse events, the goal is to uncover a level of TCOwhere a marginal increasein

storage spending (due to greater use of skilled labor, more frequent backups,

increased monitoring, or more fault-tolerant hard-ware) is matched exactly by a

marginal decreasein VaR.



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As firms expand their storage costs, VaR declines consistent with faster

recoverytimes and a reduction in the level of risk associatedwith systemsoutages.

Since storage costs tend to increase in large rather than small dollar increments,the result is a down-ward sloping step-function linking VaR to TCO, as shown in

Figure 2.

5.1 Graph- VaR vs TCO

Figure 2

If TCO is low, reflecting an environment where storage costs have been

excessively reduced, an increase in TCO will help to reduce VaR to more

manageable levels. However, consistent with the law of diminishing marginal

returns, at some point the marginal benefit from greater spending will be negligible.Beyond this point, spending is simply wasteful. The likelihood of unplannedevents

means that some risks will remain and so, in absolute terms, VaR will approach a

minimum(non-zero) threshold. Equally, TCO can never fall to zero due to certain

fixed costs associatedwith embeddedsystems(for example, PC hard disks).

To create this curve in reality, firms begin by plotting VaR (taken from their

risk data in Figure 1) against their existing TCO. This yields a single point on the

curve. Next, firms engage in a series of what-if exercises by asking how VaR

might have changed if storage spending was increased or decreasedby a certain

amount. This may seemhypothetical but, in reality, firms may already be doing this

exercise when investigating how certain severe outages occurred and how they

can be prevented in future. For example, firms may discover that a certain


Value atRisk

TCO

Excessive VaR

Point at which a marginal increase inTCO matches a marginal decline inVaR

Excessive TCO


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percentageincreasein storage spendingwould have preventedor limited the most

financially punitive outages, essentially reducingVaR.

However, to fully appreciate the inverse link between VaR and TCO, it is

not just enough to ask what can happen to VaR if TCO is increased. It is also

essential to ask how much higher VaR might be if TCO was reduced. This

hypothetical situation may seemunusual but high TCOcould mean that firms have

overinsuredthemselvesagainst very small risks.

6. DYNAMIC INFORMATION VALUE

Despite uncovering an optimal balance between VaR and TCO, this

balance can become distorted by changes in information value. ILM recognizes

that information can increase and decrease in value over time, often in dramatic

fashion.For example, in the airline industry, the value of a passengermanifest will

fall to zero the instant that a flight has landed safely. Equally, in the

pharmaceutical industry, the value of clinical trial data will increase once a drug

application moves to the next stage of FDA approval, although the data itself is

unchanged. Consequently, VaR will fluctuate based on where information is in its

natural life cycle and how quickly it movesthroughthat life cycle.

Similarly, legislation and the threat of lawsuits have altered the value of

archival data to the extent that penalties and fines can be imposedif data is lost or

unavailable to investigators. Hence, archival data may retroactively increase in

value if a firm receives a court order to hand over its data, as was the case with

Morgan Stanley where the discovery of 1,600 undocumented backup tapes was

seen as evidenceof an attemptedcover-up [12].



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6.1 Responding to change in information value

Figure 3

As a consequence of an increase in the value of information, the curve

linking VaR and TCOwill shift up and out, away from the origin.

As seen in Figure 3, if a firm maintains the same level of TCO as before,

making no changes to its storage architecture or storage practices, VaR will

increase because of an increase in risk. The probability of a system outage may

not have changed but the cost associated with an outage will increase

commensuratewith an upwardshift in informationvalue.

To re-establish equilibrium between VaR and TCO, a firm must either

increase spending around its existing systems by, for example, expanding the

frequency and scope of data backups, improving service and support, or by

transferringthe informationto a safer and more secure set of technologies.

When information falls in value as it nears the end of its useful life, the

curve will shift down and in. If a firm maintains the same level of storage spending

as before, it will have overinsured itself against relatively minor risks. The firm will

still need to protect its data but not with the same level of spending as before. The

simplest solution is to reduce TCO by transferring the data to less expensive

media. This will allow VaR to increase to the point where VaR and TCO are again

in equilibrium.

7. IMPLEMENTING ILM IN PRACTICE

Despite its intuitive appeal, ILM remains challengedby the complexnature

of information value. At one extreme, all data is valuable when viewed through the


Value atRisk

TCO

Constant TCO leads toexcessive VaR

Constant TCO will lead tounnecessarily low VaR

Increase ininformation value

Decrease in information value


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eyes of end users who feel their data must be secured at all costs. Rather than

haphazardlythrowingmoneyat an ever-increasingmountain of dataestimatedto

be increasing by two exabytes (1018

) annually or 400MBfor each of the earths fivebillion inhabitants [10]an analysis of VaR can provide an objective overview of

different instances when data was unavailable or when users were impacted by

systemfailures.

ILM tries to match storage systemcapabilities with informationvalue but as

information value is resistant to measurement, erring on the side of increased

storage spending constitutes the lesser of two evils. VaR, meanwhile, can be

derived with some degree of accuracyon the basis that is it easier, for instance, to

estimate the cost of an hour of CRMdowntimethan to accurately predict the value

of a CRM application over its entire lifespan. Even if VaR and information value

correlate, VaR is not a proxy for information value. It is true that some firms may

invest in storage systems in order to improve information accessibility, accuracy,

and relevance [5]ultimately seeking to boost sales or to enhance customer

service and sup-port [7]but few look to storage systems as a competitive

differentiator when the underlying hardwareis easily replicable.

Storage remains an unavoidable cost of doing business. As such, VaR

recognizes that storage is meant to protect data from adverse events that could

give rise to financially damaging business disruptions or worse [12]. In practice,

differences in information value across applications such as payroll (low value),

email (mid-level value), and CRM (high value) are managed using different

hardwaretiers; each tier offers a specific service level that matchesthe information

value. Thus, high-value data is assignedto a premiumtier where TCOand service

levels are high while low-value data is assignedto a basic tier where TCOis lower.



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To implement ILM is to determine a level of storage spending that fully

insures firms against the consequencesof data loss, corruption, or inaccessibility. If

storage spending is seen as the premiumon an information insurancepolicy, VaRrepresents the deductible on that policy. Perceptually, falling hardware costs have

created a false belief that a firm is implementing ILM if it spends more on storage

and saves all its data.

Without some consideration of VaR, however, firms have no appreciation

of the risks they face if their data is lost or corrupted. Consequently, VaR can

significantly improvethe implementationof ILM.

ILM products automate the processes involved, typically organizing data

into separate tiers according to specified policies, and automating data migration

from one tier to another. As a rule, newer data, and data that must be accessed

more frequently, is stored on faster, but more expensive storage media, while less

critical data is stored on cheaper, but slower media. However, the ILM approach

recognizes that the importance of any data does not rely solely on its age or how

often it's accessed. Users can specify different policies for data that declines in

value at different rates or that retains its value throughout its life span. A path

management application, either as a component of ILM software or working in

conjunction with it, makesit possible to retrieve any data stored by keeping track of

where everything is in the storage cycle.

Whereas data classifications vary widely among organizations, there are

common methodologies for classifying storage. Most organizations organize their

storage resources into three general classes: primary (high value, very fast

storage), secondary (medium value, fast storage), and tertiary (low value, offline

storage). SANs and direct-attached storage (DAS) are often used for high-value

data storage. SANs tend to be used for enterprise, high-availability, high-



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performance, clustered applications, whereas DAS houses legacy data that is still

highly valued. NAS and ATA disk arrays are typically categorized as secondary

storage. NAS devices are used for file sharing, user data, and first-level backup,although more and more NAS devices are being used as primary storage devices

to replace and consolidate direct-attachedfile storage.

Serial ATA disk arrays are typically used to store less-critical, older data or

fixed-content reference data that can be easily recovered or rebuilt. Serial ATA

disks are also used as first-level backup. Tape, optical disks, and other write-once

devices are usually considered tertiary storage. They are most often used for

backup, archiving, and vaulting. Replicating data among primary storage and other

levels of storageis commonlydone to improveavailability, reliability, and recovery.

Common attributes used to define classes of storage are reliability,

availability, performance, accessibility, security, price, and capacity. With advances

in storage performance today, availability tends to be the primary factor that

determinesvalue amongdifferent storage devices. As with data classification, tools

that automatically discover storage resources and classify them based on these

attributescan significantly simplify storage administration.

7.1 IBM ILM solutions

IBM has a history of data retention and management solutions, and is a

leader in ILM solutions. ILM solutions make the best use of a tiered hierarchy of

storage systems. IBMs disk systems (including midrange TotalStorage DS4000

and enterprise-class DS6000 and DS8000) and tape systems (including

TotalStorage 3592 and LTO Generation 3, both with write-once read-many

(WORM) capability) offer an outstanding range of options from which to build a

hierarchy.



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For data retention solutions, the IBM TotalStorage DR550 complements

this hierarchy with a complete system designed to deliver policy-based, non-

erasable, non-rewriteable storage to help meet legislative compliancerequirements. ILM solutions require close integration with applications, so

middlewaresys - 28 -tems such as IBM DB2 Content Manager, IBM DB2

Records Manager and IBM DB2 CommonStore are key components in IBM ILM

solutions.

8. CONCLUSION

Faced with rapidly expanding mountains of data and new government

regulation, IT managers are using ILM to bring order to a storage domain that has

previously been ignored because of its low strategic value. If data and information

are essential to a firms strategic positioning, storage must be seen in a new light.

A frameworkthat enhancesILM by clarifying the relationship betweenVaR

and TCO, a relationship that is often in flux because of the dynamic nature ofinformation value and firms growing desire to capture data on multiple aspects of

their business. By considering this frameworkand VaR in particular, we argue that

IT managerscan forge an effective and secure storageenvironment.



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12. Wall Street Journal. How Morgan Stanley botched a big case by fumbling

emails. May 16, 2005.

13.http://www.oracle.com/technology/deploy/ilm/pdf/ILM_for_Business_11g.pdf

14.http://www-03.ibm.com/systems/resources/

systems_storage_news_center_solutions_pdf_ilm.pdf



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15.http://www.computerworld.com/hardwaretopics/storage/story/0,10801,79885,00.

html

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