Workday has announced at a Workday Rising 2018 conference that it has developed a Workday People Analytics application based on predictive analytics and machine learning algorithms, in addition to embedding machine learning algorithms to enable organizations to optimally manage their workforce skills, now generally available within its core software-as-a-service (SaaS) application portfolio.
Pete Schlampp, vice president of Workday Analytics, says the goal is to leverage natural language analytics software to enable human resource (HR) managers and line of business (LOB) executives to collaboratively identify workforce issues involving, for example, a lack of diversity before they spin out of control.
In general, HR organizations are increasingly being held accountable by the board of directors for managing talent. Layering a machine learning framework on top of the core Workday HR application portfolio makes it possible for HR teams to become more proactive versus simply tracking key performance indicators, says Schlampp.
Workday People Analytics incorporates business intelligence (BI) and augmented analytics software that Workday gained when it acquired Stories.bi earlier this year. That software is designed to return results in the form of a natural language-based narrative that is easier for HR professionals to consume than, for example, a spreadsheet.
“It’s a lot more dynamic approach,” says Schlampp.
At the same time, Workday is embedding what it describes as Skills Cloud functionality within its HR applications. HR teams are also being tasked to determine what levels of skills truly exist within the organization. The machine learning algorithms make it simpler to assess the organization for the number of certifications its employees may hold and who might be eligible to take on new roles based on their experience.
Eventually, machine learning algorithms and other forms of artificial intelligence will not only empower HR professionals, they should reduce much of the human bias that goes into hiring decisions today. The challenge, of course, is organizing all that data required in a way that it can be consumed by AI models. Because of that issue, Workday is making a case that it is in a better position to organize data and train models simply because its (SaaS) applications are already employed by over 31 million users.
Naturally, it may require some cultural adjustments for some organizations to get used to working with a more proactive HR department. But given that most managers are all too human, a little extra machine help in most organizations isn’t going to make anything worse than it already might be.
