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Managing the Data Explosion
By Julian Stuhler, director, Triton Consulting & IDUG President
Drowning in Data
As IT becomes ever more prevalent in nearly every
aspect of our lives, the amount of data generated
and stored continues to grow at an astounding rate.
According to IBM, worldwide data volumes are
currently doubling every two years. IDC estimates
that 45GB of data currently exists for each person
on the planet: that’s a mind-blowing 281 billion
gigabytes in total. While a mere 5 percent of that
data will end up on enterprise data servers, it is
forecast to grow at a staggering 60 percent per
year, resulting in 14 exabytes of corporate data by
2011.
Industry Trends
A major trend over the last few years has seen many
organizations implementing enterprise resource
management and customer relationship management
solutions. This, in turn, has caused a dramatic
increase in the amount of data we are storing about
our customers, prospects, partners, and suppliers.
Companies are also investing in ever more
sophisticated business intelligence and analytics.
In an increasingly competitive marketplace, the
ability to base business decisions on solid,
reliable, and timely management information is
becoming a key differentiator, but trend analysis
can require very large amounts of historical data to
be stored and managed.
The trend toward company consolidation is not a new
one, but the current economic situation has
inevitably resulted in a significant increase in the
number of mergers and acquisitions. This is creating
a huge increase in data volumes, with associated
data duplication and application retirement issues.
Organizations are faced with not only managing all
of their own data, both historic and current, but
also this influx of additional data from other
parties. Imagine the data headache of combining all
of the ERP, CRM, BI, and analytic systems from
different organizations into one, manageable
enterprise system.
Legislation
Corporate compliance legislation has had a major
effect on how we use, store, and maintain our data.
The requirements placed on organizations by HIPPA,
Sarbanes-Oxley, Basel II, and others mean that many
companies are having to keep hold of more data for
longer periods. Just as importantly, that retained
data rapidly transforms from a corporate asset to a
liability once the legal minimum retention period
has expired, making it vital that such data can be
accurately identified and deleted. It is vital that
organizations adhere to this legislation in order to
avoid the cost of court appearances, heavy fines,
and the resultant damage to the brand.
Technical Trends
New capabilities within the databases used to store
corporate information are another major driver of
data growth. For example, DB2 now supports XML and
LOBs (“large objects” such as audio, video, images,
etc). The ability to store this kind of data
alongside more traditional structured information
can be very useful, but can also have a huge impact
on the overall size of the database. Other technical
trends that are contributing to database growth
include storage of data in Unicode format (which can
often expand overall database size by 10 to 50
percent depending on the data), and duplication of
databases due to replication requirements and/or
backup strategies.
Finally, there’s the perennial problem of removing
old or obsolete data once it has reached the end of
its useful life. Application data archiving is often
considered as an optional extra, and even if it is
included in the initial project plan, it is often
the first item to be postponed to a later release.
Effects of Rapid Data Growth
This unprecedented growth in data volumes is having
a significant effect on many organizations. Perhaps
the most obvious impact is on operational costs.
More staff time is required for routine maintenance
and data-related exception handling such as
out-of-space conditions and repartitioning. As the
database increases in size, so does the central
processing unit cost of running batch operations and
routine housekeeping. Ongoing running costs also
increase due to the additional disk space required,
and storage and processing capacity upgrades may be
needed even though they often haven’t been budgeted
for.
Painful though they may be, increases in operational
costs aren’t the end of the story. What price can
you place on customer satisfaction? Performance for
critical application processes can degrade as data
volumes increase, resulting in missed service level
objectives. Teams across the whole organization may
be affected, with call center staff unable to access
the information they need quickly enough to satisfy
customer demand.
Coping with the Data Explosion
Various coping strategies are available to address
the issues associated with rapid data growth.
Measures such as implementing database partitioning
and data compression, or purchasing extra CPU/direct
access storage device can help. However, these have
their own costs. There are many issues still
remaining, including::
• Disaster recovery times;
• Legal risk of exceeding minimum data retention
periods (data as a liability, not an asset);
• DBA effort to manage/tune workloads and databases;
and,
• Cost of spending IT budget on maintaining current
capacity, not innovating.
So, what are the alternatives? Implement a data
archiving strategy!
According to a recent Gartner report, database
archiving significantly lowers storage costs for
primary storage by moving older data to less costly
storage. They go on to say that archiving reduces
the size of primary storage, resulting in improved
application performance and lower storage
requirements for copies of the database for testing,
backup, and other purposes.
Also, you may think that archiving is only
applicable to the largest of applications, but in
the same report, Gartner states that performance and
cost improvements can be sizeable, even with
applications that have less than 200GB of data.
So, it would appear that a data archiving strategy
is the best way for organizations to cope with
growing data. However, once the need to archive has
been agreed on, many new questions arise:
• Build versus buy?
• Flexibility versus speed?
• Software expenditure versus staff time costs?
These tough decisions need to be made before a data
archiving strategy can be put into place. While the
temptation to build in house may be strong, is there
really justification for doing so? Can staff be
spared to work on this project? Although the
up-front cost is cheaper, what about the long-term
cost? What about the need to implement the strategy
across multiple platforms within the same
organization? Can we spare project staff from each
area of the organization to work on developing a
bespoke solution for their operating platform?
The answer is potentially a brought-in solution that
will work across multiple platforms thus bringing a
scalable solution to the enterprise without needing
to take precious staff time away onto separate,
long-term test and development projects to create a
bespoke solution.
So, it seems that there are ways to control data
growth before it controls us. By implementing a
thorough archiving policy and an intelligent
archiving system, we can manage data throughout its
lifecycle.
---Source: Information Management
Direct July 9, 2009 (www.information-management.com).
Julian Stuhler is director of Triton Consulting &
IDUG President.
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