I’m excited about machine learning and what we are doing with it at Kenshoo. And, while what we are doing with machine learning is really cool, it’s not what excites me most.
Instead, I’m excited because of what it suggests: the rise of machine learning in top-tier online marketing solutions implies that advanced analytics technology is catching up to data. The growth we have experienced in the amount of data we collect has, for the most part, out-paced abilities to analyze the data.
Part of the reason for this is that, with the amount of data we are collecting today, human scale just doesn’t work. Traditional analytics aren’t very well-suited for uncovering the value in the large data sets of today. We need machine scale to perform some of the most valuable analytics.
Machine learning is a vast subject area with many sub-genres. I’m most interested in the data aspect— the data-driven analytic approach. Humans can adopt a data-driven approach but never at the scale today’s machines allow.
The machine learning approach allows for the processing of billions of data points —not stuff you can plop in Excel and analyze with a pivot table—where each data point taken by itself may contribute little or no information. In fact, the more data available, the better the machine learning approach often works.
Machines are able to use all the data points fed in to test the accuracy of predictions and train the system to improve its predictions. The best approaches, and ones we use at Kenshoo, are even able to both learn and correct themselves independently.
As the system becomes skilled at recognizing valuable patterns, these predictive capabilities can be easily distributed and disseminated across a machine learning platform at a scale that just doesn’t seem to have a human equivalent.
At a very basic level, in machine learning we have a technology that allows us to access the vast amounts of data that we have been accumulating (and which are only continuing to grow exponentially) and can help us understand the patterns in the data to do some very useful things for our business.
Our efforts to understand patterns in data and build adaptive technology has led to two, what I would classify as “ground-breaking,” patent-pending technologies: Halogen and ActiveCluster technology.
We cracked the code on machine learning and now data is a more valuable asset to our clients. Our models accurately give a glimpse into the future.
From here, we only will find more opportunities and advance in our use of artificial intelligence.
Machine learning is a massive field and part of the art of utilizing algorithms from this field is picking the right approach for the data and problem you are trying to solve. To me, cracking open the treasure chest of data we have available today is a very exciting part of the approach.
And that is why I’m very excited about machine learning.
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