- Jay Seaton,
CMO, GlassHouse Technologies, says:
Before adopting a multipronged approach to data center deployment,
it’s crucial to first understand the underlying infrastructure challenges that result
from a data centers’ evolution. As new technological advancements such as cloud
and virtualization mature, they offer great promise but make it increasingly
difficult to maintain data center service levels, cost control and security.
Not only is the volume of data exploding, but data now comes from a variety of
new sources, users have far greater agility in procuring and provisioning new
services, and security is more challenging.
And data center managers must continue to manage downtime, better
utilize existing resources (infrastructure and people), balance the need for
power and space requirements, and implement proper security protocols, among
other essential infrastructure components. However, once organizations properly
assess these challenges, they will be better armed to properly implement their
data center strategies.
For instance, one of the first things to include in a modern data
center strategy is how much data and time organizations can afford to lose. Well-defined
Recovery Point Objective (RPO) and
Recovery Time Objective (RTO) measurements, which dictate the average cost
per occurrance, will help organizations manage their downtime and risk
thresholds. Additionally, mitigating resource consumption should be a priority
in data center management. Server and storage virtualization can circumvent the need to buy additional resources and
alternative energy sources, like solar, wind and geothermal, will maximize
existing resources as well as increase efficiency. Additionally, many legacy
systems are still highly effective and can be an alternative to investing in
new technology. Therefore, IT should look to optimize existing systems, which
will keep costs down and allow IT to focus resources on their core business.
With a well-evaluated IT strategy underway, organizations can then
take the time to evaluate the pros and cons of various data center models. For
example, while in-house facilities provide total control over information and
are potentially more secure, they are expensive to maintain and very labor
intensive. Cloud services are scalable and inexpensive, but there’s a
significant risk of downtime. And while colocation facilities provide increased
physical security and infrastructure control, they come with CapEx and OpEx
burdens and access restrictions. At this stage, IT will quickly realize that a
tiered approach, implementing all three models according to their strengths, is
the only way to reap the full benefits of in-house, cloud and colocation offerings
without suffering their drawbacks.
As explained in a recent whitepaper, one
effective approach to data center tiering would involve tier-1, or critical
applications and data, residing in in-house and colocation facilities, which
provide increased security and fast
access to important information. Meanwhile, to reap the benefits of the public
cloud’s scalability and flexible pricing without worrying about huge security
risks, IT could allot e-mail, back office applications and other tier-3
priorities to a cloud services model. Tier-2, or custom applications made only
for the business, can be reserved for the private cloud to achieve
middle-ground cost savings and satisfy specialized security policies. This is
by no means the only approach to tiering—the exact approach organizations
should take depends on their unique business structures and IT objectives.
The bottom line is, there’s no single tiering
approach that will fit all business needs, just as there’s no one data center
option that will meet IT’s various demands. Often IT will find that cloud or
colocation environments become an extension of an existing in-house data center
given how rapidly data is being developed. Whatever the combination might be,
the time spent evaluating organizations’ infrastructure needs upfront will
ensure that they are prepared to accommodate technology’s evolution and turn raw
data into actual business value.
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