Accelerating Performance with the Artificial Intelligence of Things
Figure 6: The IoT Analytics Life Cycle
Unify the Complete Analytics Life Cycle
To achieve value from the connected world,
the AIoT system first needs access to diverse
data to sense what is important as it is
happening. Next, it must distill insights from
the data in rich context. Finally, it must get
rapid results, whether to alert an operator,
make an offer or modify a device’s
operation.
Successful IoT implementations will link
these supporting capabilities across the full
analytics life cycle:
Data analysis on the fly.
This is the event stream processing
piece of it described earlier. Event
stream processing analyzes huge
volumes of data at very high rates (in
the range of millions per second) –
with extremely low latency (in
milliseconds) – to identify events of
interest.
Real-time decision making/real-time
interaction management
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The streaming data about an event of
interest – such as a car’s constantly
changing
location,
direction,
destination, environment and more –
goes into a recommendation engine
that triggers the right decision or
action.
Big data analytics
Getting intelligence from IoT devices
starts with the ability to quickly
ingest and process massive amounts
of data – most likely in a distributed
computing environment such as
Hadoop. Being able to run more
iterations and use all your data – not
just a sample – improves model
accuracy.
Data management
IoT data may be too little, too much
and certainly in multiple formats that
have to be integrated and reconciled.
Solid data management can take IoT
data from anywhere and make it
clean, trusted and ready for
analytics.