Keeping the Humans in Machine Learning

In last week’s blog, I talked about how machine learning (ML) can help with data integration. Much of the feedback I received concerned how to implement ML for data integration, but more broadly within the context of analytics in general. Machine Learning can boost your analytics efforts by helping computer systems learn in a manner that simulates human learning. However, the technology you use to implement ML is not nearly as important as the thought process behind your implementation.

Technically, ML is no different from any other analytics technology. There’s a method to it: ingest the data into a data lake or other large data store, transform it for analysis, build and train a model, and refine that model to help it learn and provide more accurate data for analysis. However, along the way, you’ll run into pitfalls that have nothing to do with technology, but with how the technology is leveraged to gain a deeper understanding of the business, and how—and by whom—the goals for the ML initiative are defined.

There are a few steps you can take to ensure that your ML initiative gets off, and stays, on the right footing. I’ll discuss these steps in terms of ML, but they really apply to any analytics effort.

Let the Business Lead the Way

Technology is useless if it doesn’t provide the outcomes you seek. Usually, when a tech project fails, it’s not the fault of the technology itself. Rather, it’s the fault of the people who defined the goals for it. As with any technology, the outcomes for your ML initiative should always be defined by the business. You can design and train the most intricate ML models, but they’ll be useless if they don’t provide the information that business owners need to do their jobs. Bottom line: actionable insights from ML/analytics initiatives can be gained only if the business leads the efforts.

Communicate the Power of the Technology

Again, the business must lead. Go all out on investing in the technologies you need to integrate ML in your analytics initiative. However, it is critical that business users understand the technology—not the nuts and bolts of the higher math behind it, but the general idea behind what you’re trying to achieve technically, and how you’re going about it at a high level. Communicate the power of the technology—be a champion for it and stress the importance of the business objectives behind it, and how the ML technology will enable you to meet them.

Keep the Dream in Mind

The defining feature of ML technology is how it enhances the predictive capabilities of analytics tools. Combined with emerging prescriptive capabilities of leading analytics software, ML-enhanced analytics can provide you with a powerful roadmap for future success. However, with any project, you’ll always have that person who says, “It can’t be done; we don’t operate that way. It’ll be too hard to change.” Don’t buy it. You can do what you want, if you want it badly enough. Just because you haven’t done it in the past doesn’t mean you can’t do it now. Trust the numbers and commit the resources to make it happen.

Pair Technology and Humans for Optimal Outcomes

As a corollary to what I just said, however, don’t forget the human element. Although ML enables you to go beyond human understanding to get the insights you need to achieve better outcomes, humans still play a large part in any ML/analytics endeavor. The combination of human experience and ML-augmented insights will help you achieve optimal outcomes. Trust the numbers, to be sure, but also trust the human business users who have a deep knowledge of your markets, customers, products, and services. By capitalizing on the connection between humans and machines you can truly achieve the outcomes you seek—and move beyond them to optimized operations and market leadership.

I’d love to hear what you think. Please comment here, connect with me on LinkedIn and Twitter, or email me at

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