1.
it ops
New Data Center
‘BIG DATA’
Realities Demand
New IT Analytics
Approach
By Sasha Gilenson
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THE DATA CENTER JOURNAL
www.datacenterjournal.com

2.
I
t’s no secret that the IT landscape
has become increasingly complex,
modular, distributed and dynamic. To
be competitive in the global marketplace, organizations have been driven
to rapidly innovate while not sacrificing
cost efficiency or speed. Now more and
more sophisticated technologies are in the
data center, creating many moving interdependent pieces, with many individual
configuration parameters.
For IT Operations, this presents new
management challenges. Without visibility
into the huge amount of configuration and
content, the slightest changes can have
a major impact on operations, and leave
IT Operations in the dark as to how the
system reacts in response to environmental
changes.
Business requirements are driving
high-paced change activity with accelerated application deployment and software
deployment schedules. The recent Gartner
Hype Cycle for IT Operations Management
report emphasized this, saying how “the
business has control over more IT decisions
than ever before, and is influencing CEOs
to consider alternatives that are faster, more
agile and responsive so that the business
can accelerate its pace, while facing cost
pressures in an economically uncertain
environment.”
Charged with delivering and
maintaining that quality, IT Operations
management now has to stay on top of an
ever-growing collection of information and
environment content. Yet, for IT Operations, managing change on every level of
the application and infrastructure stack is
complicated by:
More Data:
Amount of data to manage has increased by
an order of magnitude.
Complexity:
The increased demand for system adaptability requiring complex internal structure.
Dynamics:
Systems are now composed of a large
number of moving parts, requiring global
visibility for maintaining performance and
availability.
IT teams face many issues that they
have to stay on top of in order to maintain
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top performance and availability. Over the
last 15 years, the headlines have remained
consistent, showing the chronic nature of
change and configuration management
challenges and how these lead to critical
operational issues.
Without systems to manage and organize
this growth and lacking essential controls, IT
can end up drowning in its own data.
The combination of growing data
volume, variety, velocity and increasing system complexity is forcing many traditional
approaches in IT to change, ushering in IT
Operations Analytics solutions to take on
this challenge.
IT Operations Overwhelmed
The growing complexity of application
environments and IT architecture makes
it harder than ever to collect and analyze
reams of technical (i.e. application) data
to generate operational improvements and
support business momentum.
IT Operations needs to address practical day-to-day operations questions, like:
• When an incident occurs, can you
quickly know “what changed”?
• Can you automatically validate that
your release deployed accurately?
• Can you quickly identify what is an
incident’s root-cause?
• Can you automatically analyze the
consistency of your environments?
These are the types of questions that
application owners and IT Operations
managers don’t have time to answer because
they’re too busy trying to maintain the
performance and availability of their applications (a full time job in its own right).
This can be seen in the recent report, Turn
Big Data Inward with IT Analytics, where
Forrester declared, “If you can’t manage
today’s complexity, you stand no chance of
managing tomorrow’s. With each passing
day, the problem of complexity gets worse.
More complex systems present more elements to manage and more data, so growing
complexity exacerbates an already difficult
problem.”
While available IT management tools
collect and present IT with enormous
amounts of raw data, they really lack the
analytics capabilities and the granularity to
make sense of the high volumes of “noisy”
IT system availability and performance data.
This has left IT Operations without the tools
or technology to analyze overwhelming
amounts of raw data, and unable to extract
actual meaning buried in all that data.
IT Operations leaders are looking for
new ways to deliver more value to the business. Tools for effective decision making can
improve the infrastructure and operations
(I&O) team’s ability to allocate resources to
the right types of activities.
A Big Data Problem
To make critical decisions, IT Operations teams need to reach information
that is buried in piles of noisy, distracting
data. Monitoring tools, like APM or BSM
solutions, can gather a lot of this information and from a variety of sources: logs,
application performance availability data,
change and configuration data, and transaction data. Yet, it’s a “needle in the haystack”
problem: you know it’s there but you just
can’t find it. For IT Operations, these challenges truly are “Big Data” problems.
This Big Data problem is glaringly
evident as applications are moved through
multiple complex environments, advancing through the application lifecycle. Since
development focuses on quickly delivering
application changes through parallel and
agile methodologies, IT Operations needs
to ensure that the applications work as a
whole, leaving gaps to occur throughout
this flow.
This means applying new tools to help
deal with the complexity and dynamics of
today’s IT environments. By applying some
of the same thinking as used in business
intelligence, IT can bring the analysis of
big data inwards for IT Operations, to sift
through all of the big data to find patterns.
IT Operations Analytics
Make the Big Data
Challenge Manageable
IT Operations Analytics is better
equipped to manage this kind of big data
challenge. Providing IT Operations teams
the ability to work in closer collaboration
and greater visibility into critical information, IT Operations Analytics automatically
collects comprehensive amounts of data
and analyzes this information for critical
insights.
THE DATA CENTER JOURNAL |
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3.
The recently released Gartner Hype
Cycle report on IT Operations Management report explained why IT Operations Analytics expected to accelerate to
mainstream, saying that “With a limited
budget, access to IT operations analytics
can facilitate making decisions quickly in
a dynamic environment, thereby enabling
more effective planning and better use of
virtualization by leveraging cloud management platforms and DevOps.”
IT Operations Analytics can be a
powerful addition to IT management tools,
as Gartner Research VP, Will Cappelli
observed in the recent report, Will IT Operations Analytics Platforms Replace APM
Suites?, “IT operations teams should use IT
Operations Analytics (ITOA) platforms to
supplement and not replace investments in
end-user experience monitoring, application topology discovery and modeling,
user-defined transaction profiling, and
deep-dive component monitoring.”
When IT Operations Analytics is applied to change and configuration management, using changes as a context base for
analysis, IT Operations can turn piles of IT
change and configuration data into actionable insights.
Extract Meaningful
Information with IT
Operations Analytics
Using mathematical algorithms and
other innovations, IT Operations Analytics tools carry out calculations that churn
through immense amounts of data, extracting meaningful information from a sea of
raw data.
The various emerging IT Operations Analytics solutions take a different
perspective on this abundant information.
Using monitoring agents to track changes
to configurations, system and application components, IT Operations Analytics can parse log files to understand what
changes were made and even who made
them, enabling IT Operations staff to more
quickly assess and pinpoint a problem. IT
Operations staff can also use trending data
to assess the risks of potential changes by
comparing a change to historical analysis.
IT Operations Analytics feeds data into its
algorithms for producing business-relevant
reporting and alerts.
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IT Analytics tools can help IT Operations ensure control with:
• Making data analysis actionable
by applying statistical pattern discovery and
recognition (SPDR).
Assessing statistical clusters of
application-stack usage and operations
patterns to identify exceptions to or deviations from complex enterprise data center
operations before they result in end-user
experience issues. Textual pattern analytics sift through streams of textual data,
such as logs, to find patterns that can be
used to identify conditions and behaviors
overlooked by more traditional numerical
collection technologies.
• Configuration analytics dynamically captures all change configuration information across IT environments,
analyzing configurations to detect what
has changed from when the system was
working fine, verifying change consistency
between environments, spotting discrepancies from desired configuration (drift),
and identifying which of the changes can
impact the environment or alternatively are
a root cause of an investigated issue.
Simplifying IT Operations
To keep IT Operations running
efficiently in support of the business, IT
Operations Analytics is being applied to
key data center use cases:
Incident management.
MTTR is woefully high in most organizations. IT Operations Analytics can dramatically reduce the time required to respond to
incidents and even feed efforts to eliminate
incidents from occurring in the first place.
For instance, when an incident occurs today, IT Operations starts a race against time
to sort through the sea of dispersed data
in an attempt to figure out “what changed”
from the last time the system was working
fine, and what caused the incident. IT Operations Analytics transforms this process
by applying pattern and statistics based
algorithms to automatically analyze all
changes that occurred since the system was
working fine, identifying the incident rootcause before it can impact performance.
Problem management.
Very similar analytics technologies can help
those involved in problem management to
arrive at root cause, or a probable cause,
identification.
Change management.
IT Operations Analytics technologies can
perform sanity checks to determine the
probability of success before any change is
executed.
Configuration management.
IT Operations Analytics can detect discrepancies from desired configuration (drift)
and reduce risk to environment stability.
Reshaping Change and
Configuration Management
with IT Operations
Analytics
Today IT Operation Analytics are
considered mainly within the context of
APM and machine event management.
One of the other critical operations areas
that could be reshaped with IT Operations
Analytics is change and configuration management. With widening adoption of Agile
development processes, IT Operations faces
hundreds of changes going into production on a daily basis for environments that
can include thousands of different configuration parameters. When any of these
parameters are mis-configured or omitted,
operations can be impacted, quickly turning into a harmful incident possibly leading
to an outage with lost opportunities that
impact reputation, customers, financial performance, legal liabilities, and the overall
organization.
Enterprises should know that depending on the quality of their IT service
revenue and profitability, in response to the
painful and chronic change and configuration challenges that undermine today’s IT
Operations, enterprises can implement IT
Operations Analytics to shorten incident
response times, make releases seamless,
and significantly reduce the number of
incidents and downtime. n
About the Author: Sasha Gilenson is CEO
for Evolven Software, a provider of IT Operations
Analytics. Prior to Evolven, Sasha spent 13 years
at Mercury Interactive. He studied at the London
Business School and has more than 15 years
of experience in IT Operations. http://www.
evolven.com
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