10 tips for getting started with machine learning

5. Build a data science training curriculum

Not everyone who practices data science is going to be a data scientist or require a black belt in the craft. "You're not going to find enough of these people so you're going to have work out how to train them,” Olley said, noting that he has a person responsible for “upskilling” his IT staff in data science. Elsevier has also leveraged Coursera for help. Olley at least recommends that CIOs create a refresher course in probability and statistics, with a final exam candidates must pass to prove their mettle. Gartner advises you to identify AI knowledge and talent gaps and develop a training and hiring plan to build out your capabilities.

6. Endorse data science and ML platforms

Companies getting up to speed with AI and ML or that are uncertain about how to tackle a data science problem can dump their data in data science platforms such as Kaggle. There teams of data scientists, statisticians, quants, software programmers and others who love tackling tough problems gather to compete on corporate business challenges.

7. Watch out for "derived data"

If you are going to share your algorithms with a partner understand that they are seeing your data. He said that doesn’t sit well for informatics companies like Elsevier, which is keen on protecting its data, which it views as a competitive advantage. “Your data is the new currency,” Olley said. “You must understand strategically what you want to keep and what you're happy to share and treat it like money.

8. Don't always try to solve the whole problem

A health-care organization could try to build an algorithm that replaces all primary care physicians, who are hard to see without an appointment scheduled far in advance. Or it could solve a piece of the problem by writing an algorithm that at least can discern whether a person just needs an aspirin versus more serious treatment. As Olley said: "Solve the little bits of the problem. Get more data. Build over time."

9. Don’t overthink your data models

It’s more important to get the right training sets than perfect the data models. Don’t turn just anyone loose with data, which can lead to bad data models really quickly, Olley said. “The biggest challenge is showing people the art of the possible and really freeing them up to think about what this stuff can do ... and then scaling that out."

10. Educate the CEO and board about AI

So you’re data science pilots show promise. As CIOs, you should look to promote AI and ML as a means to influence the CEO's strategy for its potential to disrupt markets and remake existing business models, according to Gartner. After all, successful machine learning operations may be the key to your organization’s future.