​Workday plots Workday People Analytics, efforts to gauge workforce performance

Workday is planning to use machine learning to better surface workforce issues for executives with simple narratives.
Written by Larry Dignan, Contributor

Workday outlined Workday People Analytics, an application that will filter through human resources data and give executives an at-a-glance view of workforce trends they need to act on.

Using artificial intelligence, machine learning and analytics, Workday People Analytics will look for enterprise employee patterns within Workday Human Capital Management. People Analytics will aim to find connections, predict the most important issue to see and explain workplace trends in a narrative powered by natural language generation.

Also: How to build a business architecture for your big data TechRepublic

Workday People Analytics, which was highlighted at the Workday Rising conference in Las Vegas, falls under a category called augmented analytics by Workday. The gist is that Workday People Analytics will dynamically generate HR analytics to surface issues such as organizational composition, diversity, hiring, retention and attrition, talent gaps and performance.

Pete Schlampp, vice president of analytics, said Workday People Analytics is built to search through millions of scenarios in workforce data to find the issues executives need to know. "We are going beyond the story to show why something is happening as well as clues on what to do," said Schlampp.

These HR narratives will be delivered via a dashboard that looks like this:


Workday People Analytics will be available by the end of 2019 with early adopters getting access in the fall of 2019. It will be an add-on for Workday HCM customers and part of Prism Analytics. Workday is using a combination of the machine learning tools Story.bi as well as its own development.

Also: Workday buys analytics startup Stories

Schlampp said that Workday People Analytics will work with Workday data sources initially, but can use Prism Analytics to connect with outside information. One area not in the first wave will be employee engagement via Slack or some other system, said Schlampp. Engagement is "something we're keeping an eye on," he said.

The approach with Workday People Analytics could also be used in other verticals such as education, but Schlampp said that financials would be one of the first places where it was applied.

Also: LinkedIn launches Talent Insights for HR analytics, talent planning

Workday skills cloud

For Workday, the biggest frontier for its HCM efforts is to use machine learning and AI to surface data that humans can't surface. To that end, Workday outlined a universal skills ontology built into Workday HCM to better understand job skills data.

The ontology uses machine learning, its own data and customer contributed data as well as government data to reduce more than 1 million user entered skills to 55,000 verified skills. Workday found that common skills can have as many as 20 synonyms. Workday has connected those skills to narrow down searches.

Workday's universal skills ontology will help enable recruiters to better find talent and connect workers and tasks.

"This will be part of HCM and shows that machine learning is fundamental to what we do and how it can apply to the talent marketplace," said Cristina Goldt, vice president of Workday HCM.

Adaptive Insights

Adaptive Insights, which is part of Workday via acquisition, said it has added a workforce planning application so customers can integrate HR with its overall corporate planning.

Also: Workday buys SkipFlag to bolster machine learning capabilities

The update will fall into Adaptive Insights Business Planning Cloud. For Workday, Adaptive Insights is part of a broader plan to extend into ERP, operational systems and analytics.

Analytics Insights workplace planning tools enable planning across finance, HR and lines of business, include headcount planning, restructuring scenarios and skills-based planning.


Photos: From facial recognition to connected toys, a trip inside the invisible big data revolution

Previous and related coverage:

There is no one role for AI or data science: this is a team effort

'How quote-to-cash works in in any ERP is not something that you can teach a data scientist in two days.'

AI: The view from the Chief Data Science Office

It's challenging to get data scientists where you need them. And if you're managing an AI project, better be prepared for handling moving targets. These are some of the results of a survey of chief data scientists and analytics officers that we recently concluded.

Knowledge graphs beyond the hype: Getting knowledge in and out of graphs and databases

What exactly are knowledge graphs, and what's with all the hype about them? Learning to tell apart hype from reality, defining different types of graphs, and picking the right tools and database for your use case is essential if you want to be like the Airbnbs, Amazons, Googles, and LinkedIns of the world.

What to do with the data? The evolution of data platforms in a post big data world

Thought leader Esteban Kolsky takes on the big question: What will data platforms look like now that big data's hype is over and big data "solutions" are at hand?

Related stories:

Editorial standards