Everyone can become a data scientist.
That’s the somewhat radical view of Alan Jacobson, the chief data and analytic officer at Alteryx, a company that sells data analytics software to many of the Fortune 500.
Jacobson says that while he frequently hears executives complain about being unable to hire people with data science experience, let alone machine-learning skills, these executives are ignoring the amazing human resource already sitting inside their own organizations. These businesses could develop that talent if only they would invest a bit of time and money to teach their employees data science skills.
“Most of the data science applied in the business world is within the reach of most knowledge workers,” Jacobson recently told me. “It is a lot easier to teach an accountant some data science than teach a data scientist accounting.”
Although Alteryx markets itself as a software platform for “advanced data analytics,” it can really be thought of as a kind of education software, Jacobson argues. “We can see the upskilling happen,” he says. “When they first start using the product, they are just finding data and preparing data. Seven months later, they are building models that are delivering real value.”
Alteryx, based in Irvine, Calif., offers its customers data analytics courses in different formats, some simple online tutorials and others more like full university courses. Using the company's software, people can either deploy pre-built models or construct new ones using relatively light coding methods, including simple algorithms in the mathematics program R and Python machine-learning libraries.
Jacobson is a big believer that the best way to get business value from data science and machine learning is to put tools directly in the hands of domain experts. In a study Harvard Business Review undertook with Alteryx, it found that 63% of companies rely on centralized IT and data analytics teams to deliver advanced data analytics and yet almost none of them reported being happy with that solution. “You can’t ask the right questions if you don’t actually understand the data,” Jacobson says. “You need to know what you are seeing in order to ask better questions.”
Many companies are afraid to push this powerful technology into their employee ranks because they are too focused on the risks of something going wrong, Jacobson says. He says there are only a handful of business use cases in which a model must be robust enough to make fully automated, mission-critical decisions day in and day out—think a credit scoring model that a bank may use or an algorithm to triage patients in a hospital. It might make sense to ensure those models are built by a central team of data science and machine-learning experts, in close consultation with the domain experts. There may also be a lot of organizational, and even regulatory, oversight of those kinds of algorithms.
But as important as those models are, Jacobson says, they are rare. The vast majority of use cases for advanced analytics, he says, are one-offs: An analysis of why sales in a particular geography have fallen in the past quarter, for instance. These models are designed to give human decision-makers greater insight in a particular moment, not to fully automate the lifeblood of the business, and they are basically disposable. “Once you have an answer, the model is not applicable anymore,” he says.
Teaching domain experts to build their own machine-learning models is also, Jacobson argues, one of the best ways to avoid the pitfalls and ethical issues around deploying A.I. For instance, one problem with many machine-learning models is that they can find spurious correlations in data. Domain experts are more likely to sniff out those nonsensical inferences, he says, than data scientists without any deep knowledge of that particular business area.
Jacobson also says that many concerns organizations have about a lack of data governance and oversight when everyone in the organization is empowered to build their own models is overblown. He sees this as no different than letting people use other kinds of software, like accounting programs or ERP systems that track supply chains. “At the end of the day, your tax expert is creating the tax filing and no one from the IT department is checking their work,” he says.
What’s more, by using a central software system for building models, such as Alteryx, he says organizations actually have more insight into what their teams are doing and more control than when every employee is creating their own spreadsheet in Microsoft Excel and storing it on their desktop hard drive.
As John Lennon and Yoko Ono once sang: power to the people. And with that, here’s the rest of this week’s news in A.I.
Jeremy Kahn
@jeremyakahn
jeremy.kahn@fortune.com
