International Institute for Analytics

Everybody is talking about it. Mine your data for insights. Use your digital exhaust to feed machine learning programs to develop algorithms that will transform your productivity. Amazon does it. Google does it. Microsoft does it. All three offer cloud services to help you get started. Shouldn’t you be doing it too?

While we are supportive of companies’ efforts to hire quantitative Ph.D.’s to practice data science, we believe that most firms are better off hiring people with other types of training and general management skills to manage analytics and data science groups. Why? Because there are a series of traits that make for effective managers of such groups, and most Ph.D.’s don’t tend to have them. We describe ten of those traits in this blog, and the reasons why they are unlikely to be found in the average doctoral degree holder. The list of traits may be useful for anyone seeking to hire a leader of analytics or data science functions-whether they are considering Ph.D.’s or not.

Is the analytics and data science market starting to settle down? Our recent analysis of job change and tenure shows an interesting new development, which certainly suggests that things are leveling out a bit.

In November, I gave the opening keynote at an event about the future of commerce at the Rotman School of Management in Toronto, where I shared four insights outlined in this blog and also heard a great anecdote from an audience member.

Everyone gets that data is the new oil in the digital economy, but not everyone gets that there is a critical difference between data as records­­—data in databases—and data as signals—data from log files, sensors, social media posts, and the like. Let me explain.

Within a two- to three-year span, Artificial Intelligence (AI) has gone from relative obscurity to an extreme level of industry attention and media coverage. As a result, organizations that barely knew how to spell “Artificial Intelligence” a few years ago are now charging full steam ahead to pursue AI initiatives. A common question that is raised is, “Why is now the time for AI?” After all, there have been bursts of hype around AI multiple times over the last few decades. Is today different? And, if so, why?

Since 2000, over 50 percent of Fortune 500 companies have been acquired, merged or declared bankruptcy. Any company that cannot or will not transform itself into a digital enterprise faces an existential threat to its existence. To compete as a digital enterprise requires starting at the top with the CEO and Board agreeing on the right digital strategy for the company and a game plan to implement it.

One of the fastest-growing areas of artificial intelligence—at least if that term is defined broadly—is “robotic process automation,” a set of capabilities for the automation of digital tasks. RPA, as it is often called, has some valuable functions, but digital-centric companies may need more intelligence and process simplification to than RPA can currently provide.

An unprecedented change is taking place with businesses facing a perfect storm of technologies (Artificial Intelligence, Internet of Things, Blockchain, behavioural / predictive techniques and digital applications), causing disruption to companies, employees and communities. The commercial and operational organisational cards seem stacked against bold innovation and change, but they don’t have to be. Five focused steps outlined in this blog can lead to a dramatic change.

When it comes to cutting-edge analytics initiatives, you’re probably more inclined to think of companies like Amazon and Netflix, which have become synonymous with the savvy use of analytics to gauge customer affinity and deliver recommendations. But pharma companies are—or could be—well equipped to leverage data and analytics to deliver an even better informed and better targeted customer experience.