Causal Analytics in IIoT – AI That Knows What Causes What, and When
Poorly designed or implemented risk
controls
Poorly functioning feedback loops
Disaggregated analysis focused on
single organizations and incidents
Confusion about blame
The problem of many hands
relationship to each other and the strength
(causal coefficient) of the relationships. It
offers additional insights into events and
often finds causation that may be
counterintuitive to the views of the people
that do it manually. An algorithmic approach
also provides repeatability and scale. It will
analyze the IoT and contextual data in a
consistent way that is independent of the
person performing the analysis.
Many of these are as a result of the
subjective nature of the people doing
analysis and can be addressed with a more
objective, data-driven approach. People
can’t process all the potential data sources
of event and contextual information.
Modern advances in data, stream and event
processing address some of that challenge
and AI provides a means to make sense of
the data at scale. It removes the reliance on
the subjective nature of human analysis and
opens the opportunity to analyze fact-based
information at scale to derive insights.
U SING C AUSAL A NALYTICS TO
P ERFORM A LGORITHMIC R OOT
C AUSE A NALYSIS
Correlation is Not Causation
In this era of big data, it is commonly said
that data analytics is a prime driver of value
to enterprises 10 . This is true, but only if the
analytics performed across the data are well
grounded methodologically and perform
well and efficiently to derive the value.
The unhealthy quest for “the” root cause
further describes a challenge that can be
better addressed with an algorithmic
approach to Root Cause Analysis. Peerally
states that “the first problem with Root
Cause Analysis is its name. By implying—
even inadvertently—that a single root cause
(or a small number of causes) can be found,
the term ‘root cause analysis’ promotes a
flawed reductionist view.”
Big data creates big and complex data
volumes. This is of limited value however, if
it is not accompanied by the best available
analytics to enable the most valuable,
accurate and reliable decisions to occur 11 .
Hence, there is an increasing requirement
for the analytics component in industrial IoT
solutions to be fast, reliable and accurate to
identify the problems and opportunities and
An algorithmic approach often provides
more potential causal factors, their
10
How does business analytics contribute to business value? https://onlinelibrary.wiley.com/doi/pdf/10.1111/isj.12101
11
The Age of Analytics: Competing in a data-driven world
https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Analytics/Our%20Insights/The%20age%2
0of%20analytics%20Competing%20in%20a%20data%20driven%20world/MGI-The-Age-of-Analytics-Full-report.ashx
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June 2018