From Talk to Tangible. A Real-World Guide to Machine Learning.
Part 2: Education
In the last post, An Introduction, I shared how organizations are at different maturity stages in their adoption of Machine Learning — some are learning about it, some are formulating use-cases, some are piloting, and some who are already in production are seeking to improve their Machine Learning capabilities or user adoption. In this post, Education, I want to share why Machine Learning is happening now, and the common organizational and technical elements that lead to successful Machine Learning initiatives.
Why Machine Learning is happening
At the highest level, gaining a strategic advantage is becoming increasingly difficult for all organizations. Using Porter’s cost leadership, differentiation and focus approach, we can easily name multiple competitors operating within the same competitive approach today. Due to this, many companies are calling for new capabilities to get out and ahead of their competitors and customers — competing is less about what’s happening today, and more about anticipating and acting on future changes in the market before your competitors. In addition, many organizations want a more comprehensive understanding of their customers and employees, again to learn and act before competitors. These new competitive paradigms are elevating the use of Machine Learning and other Artificial Intelligence capabilities.
From a technical perspective, we are seeing the confluence of multiple key drivers behind the rise of Machine Learning. I love the analogy that for decades we’ve had the algorithms that represented a rocket ship sitting on the launch pad, but without sufficient fuel. Now with the digitizing of everything, we have the data and therefore the fuel to power our rocket ship. Additionally, the rise of low cost computing platforms, such as Amazon Web Services, and advances in chip processing speeds have made Machine Learning more accessible and efficient for many organizations.
Making Machine Learning successful
Alone, the greatest technical implementation of Machine Learning won’t anchor its use into day-to-day work. Successful integrations also require supportive leadership, strong technical teams, and a growth-mindset culture. One executive sponsor tends to be enough for a Proof of Concept, but not for a full rollout. There needs to be another exec to share the risk, think CIO and VP Sales as sponsors. Furthermore, organizations are more likely to succeed in their ML endeavors when there’s a combination of data science expertise as well as strong data, automation and workflow capabilities. Finally, there needs to be a corporate commitment to the journey of adopting Machine Learning, as well as a culture of experimenting and failing fast.
Where we can go from here
Today, supply chain companies are using Machine Learning to reduce inventory and stock outs, banks are identifying fraud, the travel industry is building price optimization algorithms, and maybe your competitor has already started their journey. In my next post, I will walk you through defining your use-cases and pinpointing where Machine Learning can accelerate your strategy.
