‘Augmented Analytics’ Promise More User-Friendly Data

New tools ‘talk’ to users, help them get to insights quicker

By Dave Zielinski November 6, 2018
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​The analytics software that helps human resource leaders analyze the vast amount of workforce data generated in their organizations has evolved significantly in recent years. But many analysts are still struggling to extract actionable insights from the data.

A new breed of smart software called "augmented analytics" promises to give leaders a more user-friendly way to analyze and draw conclusions from people data. Augmented analytics combine artificial intelligence (AI) capabilities to comb through large, disparate HR datasets to identify trends or track important metrics. Then, using natural-language-processing technology—which communicates key messages in easily understood ways—the tools deliver prioritized findings in a conversational form.

Pleasanton, Calif.-based human capital management software company Workday recently acquired Stories.bi, a company that specializes in augmented analytics. Pete Schlampp, vice president of analytics at Workday, said augmented analytics have these capabilities:

  • Automated pattern detection technology that looks for important changes in datasets, such as a change in a trend or workforce metric over time. It can scour databases and then push personalized insights to leaders based on metrics they value most.
  • "Graphing" tools that can find important connections across disparate HR datasets or other organizational databases.
  • The ability to explain trends or key findings in a simple way. Executives no longer have to analyze multiple spreadsheets or dashboards to figure out what their data is telling them. "It delivers a narrative that helps them determine where to focus their attention across a broad domain of data," Schlampp said.

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Augmented Analytics in Action

Consider an executive who wants to measure how many new hires voluntarily leave the company. An augmented analytics tool might deliver its findings in this message: "Your new-hire voluntary turnover rate increased largely because of workers in Boston leaving due to opportunities for higher compensation."

In another case, a recruiting leader might request data on quality of hire or how new recruits are performing on the job. Augmented analytics might produce results like the hypothetical numbers below:

  • High-Performing New Hires: Of the 52 people you hired in the last six months, 3.2 percent have been assessed as high-performers. (Target is 3 percent.)
  • Regrettable Attrition of New Hires: Twelve people left voluntarily in the last six months, and 7.1 percent of those departures are regrettable. (Target is 9 percent.)
  • Time to Hire: For the 52 people you hired in the last six months, it took an average of 28 days to hire. (Target is 25 days.)

"The challenge many HR functions have is they use dashboards featuring a lot of workforce metrics, but those metrics are often moving up or down at the same time," said Brian Kropp, group vice president in the HR practice at research firm Gartner. "It's difficult to develop a picture in totality about what is happening across those different metrics. The best augmented analytics tools can aggregate those data flows to be able to tell leaders, 'Here are the top-level stories you should focus on in your data, and here are the major factors driving them.' "

User-Friendly Data Analysis

SAP SuccessFactors is a pioneer in delivering insights about data in user-friendly ways. Its Headlines tool performs the same steps that a data scientist might but in an automated way that generates findings in the form of headlines that are easy for leaders to comprehend.

Examples of such headlines might be "Pay for performance ratios aren't consistent with targets" or "Termination rate is significantly higher than average."

"The idea is to make it easier for HR leaders to consume analytics so they're not just trying to make sense of a collection of data on different spreadsheets or dashboards," said Mick Collins, global vice president of workforce analytics and planning at SAP SuccessFactors.

Analytics Case Study

BBVA Compass, a banking company in Birmingham, Ala., used a form of augmented analytics from vendor Visier to study and reduce voluntary turnover in the organization. The bank found that turnover was highest for one specific revenue-producing job in branches in certain geographies.

BBVA applied an algorithm to determine the probability of exit of current employees in that key role, said Ian Cook, Visier's vice president of people solutions. The algorithm examined all of the variables of individuals in that role who left the organization in recent years, Cook said, then grouped those variables—which might be compensation, the influence of a direct manager, date of last raise—by how much they might explain whether someone may leave the company.

A list of workers deemed to have a strong probability of exit was then shared securely with branch managers on a recurring basis. To help keep top performers on board, regional HR business partners were assigned to the high-turnover branches to interview the employees and their managers and to create retention action plans. The analytics initiative contributed to a 44 percent annual reduction in turnover in that key role, according to Morgan Turnipseed, director of people analytics at BBVA.

Limitations of Algorithms

Despite the benefits of augmented analytics, Kropp cautions employers not to put too much faith in the predictive capability of algorithms that they employ. While most algorithms can help leaders make better decisions, they can't deliver complete or definitive insights on their own, he said.

Kropp cited the case of one company he knows that used algorithms to measure employee engagement. One of the algorithm's key findings was that employees were arriving later to the office and leaving earlier over a measured period of time, suggesting a potential engagement issue.

"The company spent months working on re-engagement strategies," Kropp said. "But the reality was a major highway that many workers took to work was experiencing significant amounts of construction, causing many of those delays. The algorithm told them they had an engagement problem when they really had a traffic problem."

Dave Zielinski is a freelance business writer and editor in Minneapolis.

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