Machine Learning is the answer…but what is the question?

Picking up where I left off….there is a proliferation of technologies in the analytics domain that it has become difficult to cut through the thick bull turds and hype that the big brands are throwing at you. Whether its Sherlock Holmes’ assistant or the powerful prophetic priestess, there is no shortage in answers. The answers vary from technology to algorithm, open-sourced software, to big brand solutions. I personally prefer the deployment of algorithms that attempt to model high-level abstractions in data by using model architectures, with complex structures or otherwise; composed of multiple non-linear transformations also known as Deep Machine Learning (ref. Wiki). Whatda! Sorry, the geek seeks to leak.

What is the business problem? aka the “question”. Many organizations are embarking to mature their readiness to become a data analytics-driven organization. Some are flinging their proverbial heads long into the deep crevasse of advanced analytics without an idea of how “deep the rabbit hole” is. On the other hand, people should remember that humans still possess the best instincts and too much focus on data and analytics as a science could be perilously misinterpreted as an abandonment of entrepreneural “gut-feel”. Admittedly, there is so much data today and with the variety, volume, velocity and veracity of it, we cannot help but say that its….Big. But how do you start? where is the point by which you will hurl a Hadoop at your challenges and when is the right time to Cassandra the business? CART the Customer Segment away?

The winner and the eventual market dominator will be those who can find the right….QUESTIONS to answer! Yes..the answers will come. The solutions are plenty. There are so many PhDs and all the geniuses and wizards abound. But the questions are much more relevant…and could be game-changing. How can we enrich our ability to profile our customers using other datasets? How can we make our customer experience far superior than what it is today by collaborating between the social networks and our own data? How can the value chain benefit from the comments of the public who has taken to social media for their rants and raves? How do we improve forecasting and avoid stock outs by delivering the right SKU to the right place at the right time? And my personal favorite: What do you want your customer to become?

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