Enterprise AI Needs High Data Quality to Succeed
by Kishan Cv CEO Helping organizations effectively use cloud,There’s no doubt that AI has usurped big data as the enterprise technology industry’s favorite new buzzword. After all, it’s on Gartner’s 2017 Hype Cycle for emerging technologies, for a reason.
While
progress was slow during the first few decades, AI advancement has
rapidly accelerated during the last decade. Some people say AI will
augment humans and maybe even make us immortal; other pessimistic
individuals say AI will lead to conflict and may even automate our
society out of jobs. Despite the differences in opinion, the fact is,
only a few people can identify what AI really is. Today, we are
surrounded by minute forms of AI, like the voice assistants that we all
hold in our smart phones, without us knowing or perceiving the
efficiency of the service. From Siri to self-driving cars, a lot of
promise has already been shown by AI and the benefits it can bring to
our economy, personal lives and society at large.
The question
now turns to how enterprises will benefit from AI. But, before companies
or people can obtain the numerous improvements AI promises to deliver,
they must first start with good quality, clean data. The success of AI
relies on accurate, cleansed and verified data.
Data Quality and Intelligence Must Go Hand-in-Hand
Organizations currently use data to extract numerous
informational assets that assist with strategic planning. The strategic
plans dictate the future of the organization and how it fairs within
the rising competition. Considering the importance of data, the
potential impact caused by low quality information is indeed
intimidating to think of. In fact, bad data costs the US about 3
trillion per year.
Recently, I had the opportunity to interview Nicholas Piette and Jean-Michel Francofrom Talend, which is one of the leading big data and cloud integration company.
Nicholas Piette, who is the Chief Evangelist at Talend, has been
working with integration companies for nine years now and has been part
of Talend for over a year.
When asked about the link between
both Data Quality and Artificial Intelligence, Nick Piette responded
with authority that you cannot do one without the other. Both data
quality and AI walk hand-in-hand, and it’s imperative for data quality
to be present for AI to be not only accurate, but impactful.
The Five R’s
To better understand the concept of data quality and how impacts
AI, Nick used the five R’s method. He mentioned he learned this method
from David Shrier, his professor in MIT. The five R’s mentioned by
Nicholas include:
- Relevancy
- Recency
- Range
- Robustness
- Reliability
If the data you are using to fuel your AI driven initiatives
ticks off each one of these R’s, then you are off to the right start.
All five of these hold a particular importance, but relevancy rises
above the rest. Whatever data you have should be relevant to what you
do, and should serve as a guide and not as a deterrent.
We might
reach a point where the large influx of data we have at our fingertips
is too overwhelming for us to realize what elements of it are really
useful vs what is disposable. This is where the concept of data
readiness enters the fold. Having mountains of historical data can be
helpful for extracting patterns and forecasting cyclical behavior or
re-engineering processes that lead to undesirable outcomes. However, as
businesses continue to advance toward the increase use of real-time
engines and applications, the importance of data readiness—or
information that is the most readily or recently made available—takes
on greater importance. The data that you apply should be recent and
should have figures that replicate reality.
AI Use Cases: A look at Healthcare
When asked for the best examples of the use of AI at work today, Nick said he considered the use of AI in healthcare
as a shining example of both what has be achieved using AI to-date and
what more companies can do with this technology. More specifically,
Nick said:
“Today, healthcare professionals are using AI
technology to determine the chances of a heart attack in an individual,
or predict cardiac diseases. AI is now ready to assist doctors and
help them diagnose patients in ways they were unable to do before.”
Our
understanding or interpretation of what the AI algorithms produce
dictates the use of AI in healthcare. This is true regardless of its
current accolades. Thus, if an AI system comes up with new insights that
seem ‘foreign’ to our current understanding, it’s often difficult for
the end-user to ‘trust’ that analysis. According to Nick, the only way
society can truly trust and comprehend the results delivered by AI
algorithms is if we know that at the very core of those analyses is
quality data.
Quality-Driven Data
Nicholas Piette added that ensure data quality is an absolutely
necessary prerequisite for all companies looking to implement AI. He
said the following words in this regard:
“100% of AI projects
are subject to fail if there are no solid efforts beforehand to improve
the quality of the data being used to fuel the applications. Making no
effort to ensure the data you are using, is absolutely accurate and
trusted—in my opinion—is indicative of unclear objectives regarding
what AI is expected to answer or do. I understand it can be difficult
to acknowledge, but if data quality mandates aren’t addressed up front,
by the time the mistake is realized, a lot of damage has already been
done. So make sure it’s forefront.”
Nick also pointed out that
hearing they have a data problem is not easy for organizations to
digest. Adding a light touch of humor, he said “Telling a company it
has a data problem is like telling someone they have an ugly child.”
But the only way to solve a problem is to first realize you have one
and be willing to put in the time needed to fix it.
First Step is Recognition
Referring to the inability of the companies to
realize that they have a problem, Nicholas pointed out that more than
half of the companies that he has worked with did not believe that they
have a data problem until the problem was pointed out. Once pointed
out, they had the AHA! Moment.
Nick Piette further voiced his
opinion that it would be great if AI could, in the future, exactly tell
how it reached an answer and the computations that went into reaching
that conclusion. Until that happens, data quality and AI run parallel.
Success in AI will only come from the accuracy of data inputted.
“If you want to be successful, you have to spend more time working on the data and less time working on the AI.”
This Article Source is From : https://readwrite.com/2017/10/09/data-quality-fuels-business-ai/
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Created on Oct 11th 2017 03:43. Viewed 441 times.