FINAL WORD
very different strengths and weaknesses
than human operators, meaning that the
maximum value of the algorithm is realised
in workflows that are different from those
that were created for humans. For example,
self-driving cars don’t limit themselves to
placing two cameras in the driver’s seat
to mimic human eyes, nor did they force
the cameras to swivel around like a human
neck. Rather, these cars can utilise a half-
dozen fixed cameras all around the car and
supplement this data with LIDAR, RADAR
and even ultrasonic sensors.
“
TEAMS SHOULD EMPHASISE
THE IMPORTANCE OF LINKING DATA
SO THAT RELATIONSHIPS ARE
RECORDED AND EASILY IDENTIFIABLE.
to process. AI/ML algorithms are famously
data-hungry, requiring both a large training
data set as well as ongoing real-time data for
robust inference.
The same notion applies to AIOps. Data
and processes that have been optimised for
humans may not be the best way to leverage
these algorithms. To avoid an inefficient
piecemeal adoption lifecycle, enterprises
should start with a top-down assessment of
all the systems, applications and processes to
determine where integration of AIOps might
have the most impact. A first step in preparation for the AIOps
investment is to implement a performance
measurement system that looks across
all layers of the app code, hardware and
software infrastructure, and even user and
business data. This initiative will both provide
the company with greater visibility for current
operations and also build the right platform
for an effective AIOps implementation.
Challenge 2: Not enough data Challenge 3: Low-quality data
Even the most powerful AIOps tools can be
impaired if they don’t have enough data Once there’s a process in place to collect
an adequate volume of data, the next step
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.
Problem 4. Meaning of data
Ian Jansen van Rensburg, VMware EMEA
Senior Systems Engineer
76
INTELLIGENTCIO
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
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.
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.
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
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