Here we are in the world of Big Data and all of its
possibilities. Just look at all the data we have available to us: production,
maintenance, distribution, personnel, finances - real-time, historical and
predictive. There is more data being collected more quickly and from more
sources than ever before. We are swimming in it.
So, now what? Now that we've gathered all of this data, what
does it mean to us? Personally, having reams of integers, floats, strings and
timestamps in my hands doesn't make me feel any smarter. As the old adage goes:
Data is not information. Data without context offers no insight. Data without
structure reveals no opportunities. How do we get from data to information? How
do we get from information to knowledge? And how do we get from knowledge to
action?
Finding the Anomalies
The US Department of Defense employs a process known as
Activity-Based Intelligence (ABI) to find useful details in large sets of data.
For example, in 2013, when two bombs exploded near the finish line of the
Boston Marathon, investigators immediately had at their disposal hundreds of
hours of surveillance footage, cell phone photos, and time-stamped video from dozens
of angles. To manually review all of this media would require thousands of
man-hours - time that is obviously not available in a situation like this.
To make use of this constellation of data, investigators
were forced to find a way of automating the investigation. They decided to
establish a specific set of details they wanted to locate in all of these
photos and videos. Namely, they were looking for any individuals at the scene
of the bombing who were not running away or looked unafraid. The behavior
recognition technology existed, so it was a simple matter to enter a set of
variables into a program and to let the software review the footage in an
effort to find the activity that matched these variables. Soon, two suspects
were revealed.
While it would have been nearly impossible for human
analysts to review all of this footage in a timely fashion, investigators
discovered that Big Data could in fact be very useful if combined with a
mechanism to compare and contrast the thousands of data points being reviewed.
A similar technique is now being employed in cancer
research. A so-called "Big Mechanism" has been created to review the
vast and complex medical records of cancer patients that have been established
over the years to find overlapping patterns or consistencies that can lead to a
new understanding of root causes or precipitating circumstances. By automating
the research, we are now able to analyze data sets of much greater size and
complexity than would be possible using only human analysts.
Can Similar Techniques be Employed in Industrial Automation?
Today's Industrial Automation Services find themselves in a
situation similar to those described above. Huge amounts of data are being
recorded and opportunities for improvement are known to exist, but how do we
know what to look for and how do we find it? The same sort of ABI employed by
the DoD may well have a place in the commercial world.
If we can review our historical process data to define the
circumstances surrounding certain conditions (unplanned downtime, spikes in
energy consumption, etc.), we may be able to recognize repeated patterns or
anomalous activity related to these specific circumstances, thereby enabling us
to take action to correct the situation before it happens again. By finding the
data that stands out from the rest, detailing the characteristics of that data,
and looking for those characteristics elsewhere, we may be able to pinpoint
causal relationships that were previously obscure or misleading.
On the flipside, the same techniques can be employed to
define the circumstances surrounding periods of extended productivity or energy
efficiency. The same techniques used to discern the cause of deficiencies can
be used to optimize asset performance and improve the quality and efficiency of
our processes.
By creating analytic mechanisms aligned with the principles
of ABI, we are able to create a safer, more efficient, more productive work
environment. Of course, some of this runs counter to the way most of us are
programmed to think. We tend to put more stock in consistent, reliable
information, while discounting the anomalies. ABI encourages us to find the
anomalies and focus on them.
The key to navigating the world of Big Data may not lie in
the massive set of data, but in the tiny subset of data that teaches us about
the abnormalities or anomalies we find. Look for the data points that stand out
from the rest and ask yourself why. Consider the circumstances surrounding the
collection of that data; can we map certain plant floor conditions to specific
results?
Thus far, the Big Data movement has been a combination of
hype and optimism, with very little practical value in daily operations. Some
companies are finding ways to take advantage of the opportunities, while others
have fallen behind.
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