Three Machine Learning Misconceptions CEOs Should Know

There is an epidemic in machine learning due largely to a few core misconceptions. As CEO, your team is giving you a “false confidence” that they have it under control. But when you really dig in to the misconceptions, you are going to find out that they don’t, and to compound the situation they are big-timing the very folks who can help them.

Machine learning done correctly can be incredibly impactful to your business, and there is a growing sense of urgency in the C-suite around how to capitalize on it, quickly. But to do this, CEOs must take the time to dig in and confront the misconceptions head-on. CEOs need to give machine learning the attention it deserves and get involved in the strategy and approach themselves. This is not an initiative they can blindly hand-off to their team of “ML experts” if they want a good outcome.

As the Executive Chairman of an ML company, I have the privilege of working with a team of actual ML experts, led by one of the most prolific ML researchers in the world. What is amazing to me is how many times our experts get big-timed by teams in larger organizations that think they know ML. These “Big-Timers” lecture you that their organization is so big and established that of course they know what they are doing and have this problem solved. Time and again, I’ve learned that almost everybody thinks they know ML, and they are almost always mistaken.

Misconception #1: I’ve got a team who knows ML

What you probably have is a team who can work with simple neural networks, which are single layered algorithms where both the input data and output data are labeled, e.g. translating English to French. Smart, technical people are usually able to produce these results. DO NOT mistake them for ML experts. In many cases these people have only just started working in machine learning.

Because most enterprises don’t realize that these are relatively easy projects to do, their success leads them to think they’ve “cracked the code” on ML. Emboldened by this success, enterprises start giving demonstrations in leadership and board meetings across the globe. This is where the problems that give rise to the epidemic start. The more these demos are seen, the more CEOs and leadership teams think of questions they would like to see answered by the ML technology. These questions get harder and harder because they are complex. The data gets harder and harder because there is less of it (and it is not always labeled), and the answers get harder and harder because now you need real ML experts to solve these questions and deal with the data.

Even when I started implementing ML, I certainly didn’t have the right team. My initial success quickly turned to disappointment, lost time and lost money when I finally found out that the ML team I had could not make the jump to deep learning, which requires the ability to build multi-layered neural networks to solve these more complex questions and data. So review your ML team’s degrees, lab experience, number of years dealing with all types of questions and data, and programming experience, which includes coding and leveraging GPU power.

Misconception #2: The ML projects my team are performing will scale to achieve true business IMPACT!

Let’s assume you have the right ML team. The next step is to make sure they are working on projects that will deliver real business impact and scale across your enterprise. Real business impact is defined by only two things: increasing revenue or decreasing costs. If the project is not doing either, you are funding a very expensive science experiment. A well-known example is the AlphaGo algorithm, which beat the ancient Chinese game “Go.” As cool as this was (there is a Netflix documentary on it), it did not create business impact (remember: increase revenue or decrease cost). As CEOs, this is what makes our blood boil: spending money doing things that can’t scale and won’t have any business impact. So before your team spends too much time on any ML projects ask yourself these questions:

1. Does this project solve a top three question you or your top customers want answered?

2. Does this project help the company to increase revenue or reduce costs?

3. Does this project create a unique data set for your company?

If you cannot answer “YES” to at least one of these questions, congratulations! Like I did, you are officially funding a science experiment.

Misconception #3 – My Data is Ready to Go!

This is my favorite misconception because it is such an easy mistake to make, and I made it. I ran a transaction processing business where we made money off of processing the data correctly. So of course my data was good. I could not have been more wrong. My data was not accessible, sizable, usable, understandable, or maintainable. This was a major problem, because without data, there is no ML!

What I found was that my high-powered, high-cost ML team was having to clean data much like the janitor cleans the office each night. This is not glamorous work but it has to be done (my prediction is that over the next five years the same amount of big dollars that were spent installing source data entry systems like SAP, Oracle, or Cerner/Epic in the Healthcare space will be spent getting data out of the systems and making use of it).

I share this to make you all feel better because trust me, my situation is not unique. On the data, please dig in and have your team walk you through the following questions:

1. Is our data accessible? (When is the last time you tried to download 10 files from your systems? Not very often, because it is frowned upon to take your data anywhere and there are security systems built around making sure this does not happen. If this is going to work, you need to be able to get a ton of data to the cloud so it can be used...)

2. Is our data sizable? (In ML, you need a lot of data to get the ML technology to learn. The more data, the better. At the end of the day, you want the ML technology to perform at human levels and this cannot be achieved without a sizable data set).

3. Is our data usable? Is your data clean? Is there junk in the fields or even if there is not junk in the fields, is it quality data?

4. Is our data understandable? (Developing human level performance ML technology is all about training the technology with data. If your team does not know what the data means in each field and cannot communicate it, then it is pretty hard to train a person on the data, let alone a piece of technology.)

5. Is our data maintainable? Do we have a process and a designated team to maintain the data? (Many companies spend a ton of money and time cleaning data but forget to create a plan for dealing with new data and keeping their clean data current. Bottom line: you want to reliably produce the data set in an ongoing manner.)

Companies that will win in ML will develop a Data Science Culture that tackles these five questions.

Summary

Learn from my mistakes. After early success, I had “false confidence” and ran head first into each of these three misconceptions. The experience almost made me a skeptic about ML because I blamed the ML technology for the issue versus my lack leadership for not digging in. Do yourself a favor and take a shortcut to ML business impact by confronting these misconceptions head-on. If you do, your team will be able to substantiate their confidence when they say they have ML covered.