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What Is the Most Important Question for Data Science (and Digital Transformation)?

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 artificialintelligence (AI)/machine learning (ML) and data science make them feel beaten down playing buzzword bingo on a constantly changing board. Just when they were ramping up on ML, 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 laboratory to mature. Feeling competitive pressure, many leap ahead and adopt corporate initiatives around data science or 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 No. 1 growing job title, with 74% annual growth (followed by robotics engineer at No. 2 and data scientist at No. 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.

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