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