Talk to any oil and gas executive and you may be privy to a hidden concern: that the industry is playing catch-up in the race to apply advanced data science — including machine learning and artificial intelligence (AI) — to its most critical problems. In an industry that frequently operates at the cutting edge of science and engineering it is perhaps surprising that there are so many opportunities still to be realized from harnessing data-driven analytics. Yet unlike other industries, oil and gas faces challenges ranging from the enormous complexity of drilling operations and the high cost of failure to the difficulty of obtaining the quantity and quality of data required to create machine learning algorithms. In this presentation, we will discuss some of the obstacles to effective use of data science and how oil and gas companies are overcoming them. Specifically, we will delve into four elements that in our experience must be in place in order to reap the benefits of digital technologies: 1) accurate problem formulation; 2) data readiness; 3) expertise availability; and 4) organizational enablement. For each element we will present real-life case examples of what steps different companies have taken to optimize them. For many executives, determining where and how to launch an effective data science initiative can be daunting. In an industry as complex as oil and gas, it makes sense to think in terms of smaller short-term initiatives that can then be broadened into more ambitious longer-term efforts. We will end our presentation with a discussion of how to structure and manage short-term projects while laying the groundwork for longer-term initiatives that, while riskier, are likely to yield broader and more lasting benefits.