Database Reference
In-Depth Information
Data Lifecycle Management
Don't think of Big Data as “a new technology” (for example, Hadoop). Think
of it as a phenomenon. And the primary characteristic of the Big Data phe-
nomenon is the fact that data is growing, in every one of your systems.
Unchecked data growth has a huge impact on your existing systems—data
warehouses, transactional systems, and applications. Data growth can lead
to high costs and poor performance of those applications. The growth of data
to “Big Data levels” also impacts test data management. Think about it.
Every time you deploy a system you need to generate test data systems from
production, for development to test, and so on. Often that data is copied
from production environments, and as the total data volume grows, so too
does the exponential growth of your test data environments. A second major
issue with test data is ensuring security and privacy—masking sensitive data
before it's used in non-production environments. In short, the growth of data
in existing systems is crippling them, and the problem will only get worse in
the era of “Big Data,” unless it is proactively addressed.
Data lifecycle management controls the growth and therefore the cost of
data. It manages data lifecycle in two primary ways. First, it helps with data
growth management, providing a framework to profile and manage the
lifecycle of data and to proactively archive data in a highly compressed and
efficient manner. Second, data lifecycle management is critical for proper
test data management; specifically, for creating right-sized, governed, test
data environments to optimize data storage and costs. The IBM InfoSphere
Optim (Optim) family of products is the market leader in data lifecycle
management. Optim contains market-leading capabilities for data growth
management and archiving of complete business objects across heteroge-
neous environments, while also enabling easy retrieval of archived infor-
mation via queries. InfoSphere Optim Test Data Management (Optim TDM)
contains sophisticated test data management capabilities to generate right-
size test data sets, masking to ensure that sensitive data is protected, and
automation to enable self-service for generating test data sets.
You're already thinking about data lifecycle management when you archive
relational data warehouses to ensure that only current information is stored,
thereby improving performance and reducing cost as data volumes grow.
