FEATURE: AI
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Challenge 2: Not enough data
ORGANISATIONS SHOULD DEVELOP
PROCEDURES FOR STANDARDISING
AND FILTERING DATA COLLECTION.
However, the most meaningful results will
come from rethinking operational use cases
in a top-down manner to complement these
bottom-up process improvements. AI/ML
algorithms have 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
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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.
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.
Even the most powerful AIOps tools can be
impaired if they don’t have enough data
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.
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 3: Low-quality data
Once there’s a process in place to collect
an adequate volume of data, the next step
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