Machine Learning Times
Machine Learning Times
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9 years ago
Wise Practitioner – Predictive Workforce Analytics Interview Series: Kathy Doan at Wells Fargo Bank


In anticipation of her upcoming Predictive Analytics World for Workforce conference presentation, Beyond Traditional Turnover Creating Kathy Doan imageValue by Quantifying the Impact of Attritionwe interviewed Kathy Doan, Vice President, Community Banking HR Insights & Analysis Group at Wells Fargo Bank. View the Q-and-A below to see how Kathy has incorporated predictive analytics into the workforce of Wells Fargo Bank. Also, glimpse what’s in store for the new PAW Workforce conference.

Q: How is a specific line of business / business unit using your predictive decisions?  How is your product deployed into operations?

A: We’ve done work to quantify the true impact of turnover by looking at the quality of turnover. Because all employees are different, we cannot count their turnover the same. If you think about it, what will hurt a baseball club more: losing a Cy Young award winning pitcher or losing a designated hitter? We have business leaders interested in applying this quality of turnover concept to identify areas that have high attrition risk.

Q: When do you think businesses will be ready for “black box” workforce predictive methods, such as Random Forests or Neural Networks?

A: Businesses have been ready for this and use this often with Customer Analytics. In terms of People Analytics, we’re still in our infancy stage, but we do use a base version of Random Forests through decision trees to decide whether to implement an HR program. In Marketing, we use neural networks to predict how a potential customer will respond to a mailer. It would be great to use neural networks in HR to predict how a potential candidate will respond to a job opening.

Q: Do you have suggestions for data scientists trying to explain the complexity of their work, to those solving workforce challenges?

A: Keep in mind that the business is more likely to take action on your analysis if they understand it, so explain it to them in their language. Some data scientists get bogged down in the details, which may fly over their audience’s head. Remember that senior leaders don’t want to know how a watch is made; they just want to know what time it is.

Q:  What is one specific way in which predictive analytics actively is driving decisions?

A: More companies are starting to integrate external data with their internal HR data by mining social media feeds to predict if and when an employee is going to leave the company based on their activity and content. This is something we have been exploring with text mining analytics. Though this information is valuable, we want to ensure we keep ethics top of mind with any analytic work we do.

Q: How does business culture, including HR, need to evolve to accept the full promise of predictive workforce?

A: The challenge I see is that both the HR analytic team and the business love data. However, in the middle, we have others who may be more cautious. That can cause bottleneck when trying to take action on the results of our workforce insights. One way for HR to evolve is to provide more education on analytics and have HR Business Partners be advocates for any predictive work the HR analytics teams generate.


Don’t miss Kathy’s conference presentation, Beyond Traditional Turnover Creating Value by Quantifying the Impact of Attrition, at PAW Workforce, on Tuesday, April 5, 2016 from 9:50 to 10:35 am. Click here to register for attendance.

By: Greta Roberts, CEO, Talent Analytics, Corp. @gretaroberts and Conference Chair of Predictive Analytics World for Workforce

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