forecourttech April 2020

forecourttech
Digital transformation,
including AI and IoT, will
be a core technology
evolution in driving the
successful change in
customer experience.
credit cards. The famous Target breach was from hackers
coming in through the network-connected air conditioning
system and then attacking the POS systems. AI is both a
defensive and offensive strategy in digital transformation.
As we look closer into the expanding risk footprint of new
DX technologies, especially in smart cities and oil and
gas retail, the addition of new IoT smart devices helps
enrich the focus on the customer experience. With the
change, IoT brings along many new risks around these
digital technologies, including the IoT devices themselves,
cloud computing, customer applications, data privacy,
partner applications and new sensors. These new risks will
require process changes, modified priorities and even staff
modification to manage the risks associated with new IoT
technologies.
In parallel with driving DX change, operations teams must
begin a transformation of their people skills, operational
processes and tools. Collectively, DX requires improved
operational monitoring capabilities to automate and create
greater efficiency in managing this new risk footprint.
AIOps (the use of AI in IT operations) starts with the ingest
of big data (clean data). The data could be at rest and be
used for historical analysis, or the data could be in motion
(streaming) for real-time analysis. The algorithms then look
for prescriptive responses that can turn into actions. AIOps
is not “yet another technology tool.” It comes with automation
algorithms for predictive analytics, the correlation of
alerts and the remediation of issues.
AIOps is not a perfect tool. Just like humans, AIOps is
prone to errors. Unlike human errors, AIOps is prone to
various types of digital errors found in error-prone data sets
and biased training of machine learning models. Furthermore,
there is no audit trail of AIOps if it leverages neural
nets or deep learning algorithms. These types of machine
learning models are “black boxes” and cannot show how
their recommendations were reached. Today, operations
staff, whether it is facilities, NetOps or SecOps, sift through
millions of alerts and try to predict, correlate and remediate
an actionable response. With AIOps automation, the
remediation algorithm must understand causal inferences.
Meaning if A causes B, then AIOps needs to predict A to
prevent B. The AIOps algorithms from prediction, correlation
and remediation need to choreograph together and
augment the human IT operations staff into becoming Jedi
warrior IT analysts.
As far as light industry IoT goes, the operational team must
begin to take steps and look at the array of smart devices
related to facilities in their organization and then plan a fully
automated AIOps-driven life-cycle maintenance and monitoring
strategy for those “smart” and connected assets. As
a unified strategy, the SecOps team needs to design best
practices for cyber hygiene (such as NIST). In theory, this
should all be possible through AIOps automation without
requiring additional staff.
This article was first published at forbes.com.
pageseventeen
Thomas Caldwell is a member of the Forbes
Technology Council, an invitation-only community
for world-class CIOs, CTOs and technology
executives. Thomas will be speaking at
forecourttech ’20, 6-7 October in Alicante, Spain.
For more information on Thomas’ talk and other
presentations please visit
www.forecourttech.com/speakers
#forecourttech @forecourttech www.forecourttech.com