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FEATURE: AI
Ian Jansen van Rensburg, VMware EMEA
Senior Systems Engineer
To address this issue, teams should
emphasise the importance of linking data
so that relationships are recorded and easily
identifiable. A standardised naming policy
can help keep companies from forking off
new silos of data.
Maximise the investment
AIOps promises to tackle some of the
toughest IT issues, but there are core
organisational issues that teams will need to
address before they can realise that value.
is to assess its quality. Common problems
that we see in the field today include noisy
data, inconsistent or insufficient reporting
frequencies, and even inconsistent naming
policies across applications or data centres.
Organisations should develop procedures for
standardising and filtering data collection,
as well as identifying the types of data that
are most valuable for their specific priorities.
Adopting procedures based on this shared
understanding will provide value now and in
the future.
transaction rate’ represent a per-second,
per-minute, or per-hour rate? And was the
choice made here applied consistently
across other related metrics?
As another example, if data is being
collected from a microservice that’s
running within a container that’s running
on a VM that’s running on a physical host,
is the data tagged at all levels so that
behaviour at one level can be correlated
and compared to other levels? Otherwise,
enterprise data can resemble a series of
siloed, parallel universes.
Because some of the most common
problems organisations encounter when
it comes to AIOps integration are found
in the quantity, quality, and interpretation
of their data, the first critical step – before
jumping into an AIOps solution – is to take
a meaningful assessment of existing IT
systems and use cases. That includes the
collection methods, business motivations,
and contextual meaning behind the data.
By building a foundation of robust
data infrastructure and clear use case
identification, companies will find that their
future AIOps investment will not only deliver
what it promised but also provide ongoing
additional value that wasn’t even expected. n
Problem 4: Meaning of data
Companies can collect abundant amounts
of high-quality data, but without the
right context, the data is nearly useless.
Data points that lack semantic definition
or consistency are less valuable for both
human operators and AIOps – for example,
do the values of a given metric like ‘user
www.intelligentcio.com
EVEN THE MOST POWERFUL AIOPS
TOOLS CAN BE IMPAIRED IF THEY DON’T
HAVE ENOUGH DATA TO PROCESS.
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