Simplifying the Advanced Analytics Discussion (DL/ML/RL/AI)
Will I ever understand the nuances of the advanced analytics landscape? Well, maybe the better question is will the advanced analytics landscape ever stop changing? The advanced analytics landscape, into which I include Deep Learning (DL), Machine Learning (ML), Reinforcement Learning (RL) and Artificial Intelligence (AI), seems to be in a constant state of evolution. New advanced analytic algorithms and tool sets seem to be coming out of every university, every startup, every digital media company and every technology company. And many of these new advanced analytic algorithms and tool sets are open source, which means that they are available for others to build upon.
Unfortunately, many of these algorithms and toolsets don’t come with the supporting hands-on training materials necessary for the average business user to understand where and how to apply these advanced analytic algorithms. One Machine Learning startup has built an impressive, easy-to-use machine learning toolset (think “Tableau for Machine Learning”). Unfortunately, their documentation is geared for experience data scientists. They miss an opportunity to create a “textbook,” coupled with sample data sets and step-by-step instructions, to help business users to experience the capabilities of machine learning.
There are a few excellent sources of advanced analytics training. Probably the most promising is Andrew Ng’s Deep Learning Training. There are also lots of free sources, but I’ve found them to be poorly organized; a cobblestone of loosely-related advanced analytic topics versus a curriculum that’s designed to take you from step A to Z with hands-on, easy-to-execute examples (I don’t want to spend hours setting up a virtual machine, loading and unpacking the software that I need to make the analytics environment work…sorry, but I’m really lazy).
Digital Rust Bowl and Advanced Analytics Training
Training, education and workforce transformation has never been more important, and while the advanced analytic toolsets continue to evolve, the supporting education and training is lacking, especially for those who do not have the luxury of dropping out of the workforce to attend university.
From the TBR report “The Impending Digital Dust Bowl: Mitigation, Survival and Interdependence” written by Geoff Woollacott, Senior Strategy Consultant and Principal Analyst, and Patrick Heffernan, Practice Manager & Principal Analyst, we get the following terrifying assessment:
“We are, in essence, creating a Digital Dust Bowl of displaced manufacturing, clerical and middle- management workers whose jobs will be replaced by automated machines and different methods of establishing trust in a wide range of economic transactions. Technology executives and strategists comprehend this better than most other business and political leaders as we have lived in this world for decades. Ways to mitigate the disruptive economic and social impacts to these accelerations have yet to gain broad-based consensus within the policymaking institutions as evidenced by the current political climate at the national level. It is this lack of consensus that hinders our ability to mitigate the impending economic impacts of the accelerating rate of technological innovation.”
I found the concept of a digital dust bowl absolutely terrifying for America and our way of life. As rapidly as we’re developing new advanced analytic capabilities, we must be equally diligent in developing education, training and apprenticeships to ensure that everyone can understand where and how to apply these analytic capabilities. In the end, people’s “market value” will be determined in their ability to exploit these new analytic capabilities for financial, operational, organizational or market value.
Advanced Analytics Education
So the first step in the education process is to simplify the discussion; to be able to explain some complex concepts in ways that the average layperson can grasp. I’ve taken my best shot at trying to “simplify” the advanced analytics continuum so that we can spend less time talking about the different advanced analytics categories and more time understanding where and how to apply these analytics (see Table 1).
I truly believe that this advanced analytics education conversation must be driven by the business, and not left to the technology providers. The data scientists will “figure it out” because they got giant brains and excel in these types of unstable, rapidly evolving environments. But the average business users are not like that, especially with respect to technology comfort and ultimately adoption.
And we must get the business comfortable with the advanced analytics capabilities if we have any chance of helping organizations to become more effective at leveraging data and analytics – predictive, prescriptive and cognitive analysis – to power our business models (see Figure 2).
Advanced Analytics Summary
As many of your already know, I teach part time at the University of San Francisco School of Management. I teach a class called the “Big Data MBA.” Here’s a link to the book that I use to teach that class: “Big Data MBA: Driving Business Strategies with Data Science”.
The goal of that class to is educate and train tomorrow’s business leaders to embrace analytics as a business discipline; that analytics is a business tool for optimizing key operational processes, mitigate compliance and security risks, uncover new monetization opportunities and create a more compelling customer engagement. All of those things are highly dependent upon our ability to educate, train and digitally transform tomorrow’s workforce.
In an upcoming blog, I’m going to frame out an “Economic Value of Data” classroom curriculum that I will be testing in this winter’s class. The reason I want to start with data and analytics “monetization” is it’s the only way to get the business’s attention; to help the business to understand how they leverage data and analytics to “Make Me More Money.”
I’ll make y’all guinea pigs for what I plan on teaching. Welcome to my world!