What is the most important question for Data Science (and Digital Transformation)
With so many buzzwords surrounding AI and machine learning, understanding which can bring business value and which are best left in the lab to mature is difficult. While machine learning offers significant power in driving digital transformations, a business must start with the right questions and leave the math to the development teams.
By Polly Mitchell-Guthrie, VP of Industry Outreach and Thought Leadership, Kinaxis.
Deep learning, active learning, transfer learning, reinforcement learning, name-your-preferred-flavor-learning, AutoML, heuristics, stochastic gradient descent – does this list send a shiver of excitement up your spine? Have you just completed a boot camp or graduated with your degree in data science, computer science, machine learning, etc., so you’re armed and ready to sling some code and build one of these models?
Consider for a moment a different perspective, that of someone far up your leadership chain, the corporate executive. You may feel that they don’t understand what you do. You’re probably right. Because for most of them, these lists of what is trending in AI/ML and data science make them feel beaten downplaying buzz word bingo on a constantly changing board. Just when they were ramping up on machine learning, suddenly everyone is referring to AI, and they can’t sort out exactly how the two are related, let alone what to do about it.
Because as leaders, their challenge is to decide which new buzzwords bring business value and which are best left in the lab to mature. Feeling competitive pressure, many leap ahead and adopt corporate initiatives around data science or artificial intelligence/machine learning (AI/ML), even as they are still trying to figure out their meaning. To keep it simple, I often bundle these buzzwords and call them “fancy math,” and as passionate as I am about their power to make a positive impact on business, I also believe that starting with math misses the point.
Digital transformation is a strategic imperative for business today, but math-driven technology alone will not drive transformative change, which also requires a strong business vision and strategy. The most strategic step is to set the vision and identify the highest priority problems to solve, which helps people understand the “why.” The most successful initiatives clearly communicate what McKinsey calls a “change story.” Once the business problems are well-framed, I encourage executives to leave the math under the hood for data scientists, because which math method to use is an important but tactical decision. Leading with math amounts to letting the tail wag the dog.
But McKinsey also found that those organizations with successful digital transformations also are more likely to use fancier math. This probably explains why LinkedIn’s 2020 Emerging Jobs Report cites AI Specialist as the #1 growing job title, with 74% annual growth (followed by Robotics Engineer at #2 and Data Scientist at #3). Math can indeed move the world, but it is imperative to give it a chance to succeed. You, newly-minted data scientist, are in hot demand, but it will take a lot more than just hiring you to actually impact the business. Because as these McKinsey consultants write in the Harvard Business Review, Building the AI-Powered Organization requires many other core practices to ensure the adoption of your work. And their research shows that only 8% of organizations are doing what it takes to make that happen.
Einstein is said to have quipped:
If the venerable math genius prioritized problem formulation over math, shouldn’t the rest of us mere mortals?
First set the vision and frame the business problems and desired capabilities, which is the hardest step, because business is prone to describe symptoms (e.g.
