It's easy to focus too much on building a data science research team loaded with Ph.D.s to do machine learning at the expense of developing other data science skills needed to compete in today's data-driven, digital economy. While high-end, specialty data science skills for machine learning are important, they can also get in the way of a more pragmatic and useful adoption of data science. That's the view of Cassie Kozyrkov, chief decision scientist at Google and a proponent of the democratization of data-based organizational decision-making.
To start, CIOs need to expand their thinking about the types of roles involved in implementing data science programs, Kozyrkov said at the recent Rev Data Science Leaders Summit in San Francisco.
For example, it's important to think about data science research as a specialty role developed to provide intelligence for important business decisions. "If an answer involves one or more important decisions, then you need to bring in the data scientists," said Kozyrkov, who designed Google's analytics program and trained more than 15,000 Google employees in statistics, decision-making and machine learning.
But other tasks related to data analytics, like making informational charts, testing out various algorithms and making better decisions, are best handled by other data science team members with entirely different skill sets.
There are a variety of data science research roles for an Organization to consider and certain characteristics best suited for each. Most enterprises already have correctly filled several of these data science positions, but most will also have people with the wrong skills or motivations in certain data science roles. This mismatch can slow things down or demotivate others throughout the enterprise, so it's important for CIOs to carefully consider who staffs these roles to get the most from their data science research. Here is Kozyrkov's rundown of the essential data science roles and the part each plays in helping organizations make more intelligent business decisions. Data engineers are people who have the skills and ability to get data required for analysis at scale. Basic analysts could be anyone in the organization with a willingness to explore data and plot relationships using various tools. Kozyrkov suggested it may be hard for data scientists to cede some responsibility for basic analysis to others. But, in the long run, the value of data scientists will grow, as more people throughout the company are already doing basic analytics. Expert analysts, on the other hand, should be able to search through data sets quickly. You don't want to put a software engineer or very methodical person in this role, because they are too slow. "The expert software engineer will do something beautiful, but won't look at much of your data sets," Kozyrkov said.
