Gateway to Intelligent Data Management

It is the information age. Companies are relying heavily on

enterprise planning systems like ERP, SCM, and CRM to automate and manage their

enterprise resources. These systems generate and host vast amount of data which

is well structured in its own way to fulfill a specific need.

Apart from this structured data, enterprise also store

massive volumes of unstructured data in form of email, instant messages,

documents and images. Both the structured and unstructured information is

required to be stored and retained within these systems for various strategic

business and regulatory requirements.

No wonder Charlie Gary from Meta group says "Data is

growing at 125% per year. In a typical enterprise, up to 80% of this data is

inactive and yet remains in production systems to cripple performance".

This is what I call the 'Data Tsunami' and it can be avoided by intelligent data

management.

Safeguarding critical information



Information by itself is considered a resource. Enterprises need to plan

effectively and put the right combination of strategy, software and hardware

tools in place to avoid a data tsunami. Apart from the new data that is

generated everyday, the strict data retention policies and legal regulations to

retain transactional data over long periods is fueling data growth into

unmanageable scale.

This ever-increasing volumes of inactive data which is

retained for compliance, affect the application performance, limit data access

and strain storage infrastructure. This has resulted in increased complexities

in mission-critical IT environments and



is a growing concern among businesses.

It is also where the increasingly popular concept of

information lifecycle management (ILM) comes into picture. ILM helps companies

strategize on how to manage data right from cradle to grave-from the time the

data is generated/captured to the time it is deleted from the systems.

The value of the information keeps changing with time,

processes, business and regulatory needs. This in turn affects the probability

of usage of data. Data reuse has been one of the key matrix of ILM which helps

strategize staging of data on different storage tiers to cost



effectively optimize the storage infrastructure and enhance performance. A well
planned ILM strategy will allow the enterprise to retain all the reporting and

access capabilities as if the data were lying on the same server.

Analysts have been scouting through experiences to come out

with the best practices that would guide companies through the changing times of

ILM. The experiences of various organizations clearly state there are no

definitive best practices.

ILM means different things for different organizations.

Nevertheless, there seem to be enough common issues that every organization is

coming across during their implementation of ILM strategies. But there are key

issues which need to be addressed by it:

Data classification



The importance of data retention policies during ILM implementations, is of

key significance. The data value otherwise called data classification forms the

foundation for a successful and efficient information management. The data

retention policies need to have a buy in from all the entities which own or use

the data. Classification of data which results into staging the data onto

different tiers is probably the most important step for ILM implementation for

any organizations.

Choosing the right storage tier



In a recent conference in California, database administrators from few

companies were complaining that their senior management is misinterpreting the

hierarchical storage management (HSM) and are looking forward to totally

removing Tier 1 (Production tier) from their IT environment. But, the Tier 2

storage cannot handle data request of any real-time production environment. They

are only for the data which is rarely accessed. Moreover Tiering the data should

be for eliminating the unnecessary load on the production servers and improve

performance and achieve optimized storage utilizations.

Restore data



Businesses need to expect the unexpected and be prepared for any

circumstances. Generally the archived data is always in 'read only' mode for

compliance reasons. The archiving software which helped the company to archive

the data needs to allow for de-archiving the data into the production database

without loosing the data integrity. This is necessary incase of editing

requirements of archive data (e.g. product recall). This is a key component in

any ILM strategy.

Data security



The need for setting apt user and management level access privileges for

data increases as we classify the data into various tiers based on its value.

Only required users need to be given access to Production / Archive / or both

depending on their responsibilities. Also, sensitive data (e.g. Financial,

Health data) needs to be protected in production, archive and non-production

environments (testing, development, and outsourcing).

Compliance



One of the reasons for ILM to come into existence is compliance. Various

regulatory bodies across the world have been coming out with their own way of

governing data retention. For today's global companies, the ILM software should

allow for incorporating any number of regulations without overriding the other

and help achieve compliance

Data integrity



ILM requires that data of any value needs to be available for immediate access
for reporting and compliance purposes. A few regulatory bodies also require all

tiered data — say production and archived data to be accessed through the same

application which created the data. This online seamless availability of data

can be achieved only if the data integrity and referential integrity are

maintained during hierarchical staging of data.

More than a single application



Many vendors are attacking the archive market from a packaged application

perspective (e.g., Oracle Applications, PeopleSoft, SAP). But most companies

will have a need to archive more than a single application; for this reason,

users should evaluate the scope of packaged solutions. What companies need is a

comprehensive enterprise archiving solution which covers both structured data as

in packaged application like Oracle Apps and unstructured data like email, IM,

documents etc.

Database archiving as a gateway to effective information

management is gaining ground and according to a recent study, the data life

cycle market has the potential to reach $4 billion by the year 2007.

The author, Sai Gundavelli is the CEO of Solix Technologies.

Source: DQ Channels