While there are no simple answers and no one-size-fits-all approach, there are ways to tap into the talents of data scientists and citizen data scientists.
Other changes are also taking place, Bazzell says. CIOs should recognize that, over time, "Data science will become more mainstream within traditional applications." This means that "users and non-data scientist software developers and others in citizen data science will be required to understand the art and science of data science and acquire data science skills to be effective at their jobs," he notes.
Bazzell also predicts that IT organizations will begin partnering with data science teams to develop flexible, broader, high-quality data sets for data science teams.
Leveraging Data Scientists and Citizen Data Scientists
How can an enterprise achieve best-practice results in a rapidly changing digital world? How can it leverage data scientists and citizen data scientists to maximum advantage? First, data scientists require near unfettered access to data.
"The worst thing an organization can do is tell a data scientist, 'Sorry, that data is off limits,'" E&Y's Schlesinger points out. "Data scientists are data wranglers. It is their job to find non-obvious correlations amongst data."
Second, business leaders should identify specific analytic use cases that they feel will add value-added insights to increase profitability, reduce cost or increase efficiency, and then provide the data science team with goals and value-oriented objectives. I
It's important to recognize that it's typically not the responsibility of the data scientist to identify business use cases, though they may offer valuable insights into the feasibility of a use case or what additional value an organization might gain by modifying the use case, Schlesinger explains.
As organizations turn to outside sources for data scientists, it's important to keep a few other things in focus. It's important to find a provider that offers the right match of talent and connects to an organization's value proposition.
It may also be necessary to consider a hybrid approach that delivers a more dynamic staffing model. This helps an organization flex up and down with outside talent, as needs and industry conditions change.
Yet, even with a best-practice approach, additional challenges and even unintended consequences may arise. As data sets grow larger, CIOs should recognize that shadow IT platforms may appear, and these systems could drain resources—including money devoted to expensive data scientists.
In addition, groups and partners must align on data formats and requirements to extract maximum value from data scientists. "This approach helps the business focus on creating platforms of 'right' data that adds value, rather than creating data sets of 'junk' data," Schlesinger points out.
This article was originally published on 01-26-2017