Archive for March, 2016

March 25th 2016

Wise Practitioner – Predictive Workforce Analytics Interview Series: Pasha Roberts at Talent Analytics, Corp.

By: Greta Roberts, Conference Chair, Predictive Analytics World for Workforce 2016

In anticipation of his upcoming Predictive Analytics World for Workforce conference presentation, Predictive Job Maps – Modeling an Entire Workforce with a Tree of Models, we interviewed Pasha Roberts, Co-Founder and Chief Scientist at Talent Analytics, Corp. View Pasha_Roberts imagethe Q-and-A below to see how Pasha Roberts has incorporated predictive analytics into the workforce of Talent Analytics, Corp. 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: Our clients use our predictions for performance or attrition every day, in the process of recruiting new job candidates. At PAW, I will be talking about rolling these up into a full network or tree of predictions, to predict performance for the candidate’s likely future roles, after a promotion or two. For this, we’ve built several dynamic job maps, and several composite benchmarks, and will soon deploy the full integrated solution that rolls all of this together.

Q: If HR were 100% ready and the data were available, what would your boldest data science creations do?

A: I’d create a living, interactive, visual map that shows everyone at the company who is going where, and how it affects corporate value. I’d populate the map with hundreds of predictive models to optimize employee changes, and keep it fresh with ongoing research. The result would be a living guide with tangible directives for hiring, terminating, and promoting employees to grow the organization right.

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

A: Not for many years, if they are kept opaque. HR and Hiring Managers want to know what is driving selection, and that it makes sense with a management narrative that they can follow. Sometimes there is a tug-of-war between plausibility and accuracy, but fortunately the human ability to form narrative is strong.

This doesn’t mean you are limited to simple regressions. Variable selection is everything for regression models, and we often use random forests or lasso/elastic net methods to find a set of regression variables that robustly predict.

Also, you can use black-box models like Random Forests, Support Vector Machines or Neural Nets for better accuracy as long as you do the extra work to isolate key variables and trends in them, to build a story for your model users. There are methods to probe a winning black box method to identify variable importance and general directionality. This can take enough opacity off of a model to be able to give “face validity” to its users.

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

A: We often use examples from other domains, preferably their own domain. If I’m explaining a hiring model to a banker, we’ll show how it’s like using a credit score before offering a loan to someone. You don’t extend the loan (or job offer) unless there is a good probability that the person will pay the loan back (or stay on the job, or perform on the job). They get that.

Some of the technical graphs, like survival curves, work well with management users, and you can explain them; to most. Other constructs, like an AUC curve or cluster silhouette plots, are just not going to work with most. We try to win a lot of trust by the time we get to that stage.

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

A: As mentioned above, hiring decisions – choosing candidates who are likely to not terminate early, and who are likely to overachieve (or not underachieve) real business KPIs. We work with clients to identify the KPIs – they are tangible things, like sales per month, or food safety scores, or cash drawer entries. We don’t try to drive mushy middle values like engagement or happiness.

You don’t put graphs or anything fancy in front of a recruiter for hiring/promotion decision. We just deliver calibrated, color-coded color bands – blue candidates are likely to over-perform, red candidates are likely to under-perform. They just pick up that color from the system; maybe get some auto-generated behavioral interview questions and talking points tailored to the candidates, and move to the next step in recruiting.

Real analytics ultimately doesn’t show graphs or paragraphs of “insights” to the user – it just helps them make the decision.

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

A: A predictive analyst needs to earn their trust, and they need to learn to understand that their “gut” is just another form of decision-making. The HR users need to gently see that sometimes the “gut” can be more biased, and less fact-driven than rigorous analytics.

They don’t need to learn Chi-Square or Receiver Operator Characteristic – though we’ve converted several into junior analysts and gotten a few back into graduate school. Managers do need to understand that models are just trying to make decisions based on facts, the way they are, and that they need to be forever learning.

Q: Do you have specific business results you can report?

A: In one case we reduced annual attrition from 84% to 48% in 5 months. That saves the banking client over $1 million a year in replacement cost and employee lifetime value.

In another case we increased the ability to hire successful candidates (as measured by passing a Series 7 exam) by 12% – that high-volume situation saved the client over $4 million a year in replacement cost alone.

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​Don't miss Pasha’s conference presentation, Predictive Job Maps – Modeling an Entire Workforce with a Tree of Models, at PAW Workforce, on Tuesday, April 5, 2016 at 10:25 to 10:45 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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March 7th 2016

Dr. Data’s Music Video: The Predictive Analytics Rap

By: Eric Siegel, Founder, Predictive Analytics World

With today’s release of “Predict This!” – the rap video by yours truly (a former university professor trying to be a pop star) – I took the opportunity to ask a few questions of the music video’s featured character, Dr. Data.

Here is the video, followed by my interview with Dr. Data:

 

You can also access and share this video on:

 

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Info, MP3 download, lyrics & more: PredictThis.org

 

INTERVIEW WITH DR. DATA:

Eric Siegel: Please summarize your vision for this rap video.

Dr. Data: Inspired by true events, this video recounts my origin story. More than a dance video for a song about predictive analytics, this film allegorically reconfirms the power of embracing your true inner self.

Eric Siegel: A pop song about predictive analytics? Why?

Dr. Data: Enlightenment. Infotainment tastes great and it’s good for you, too. A song can make a topic some find dry or intimidating fun and accessible. The more sophisticated viewer who listens carefully to the lyrics (click for full lyrics) will learn about this critical technology.

My goal was to think outside the quadrilateral parallelogram and make the best educational predictive analytics geek rap music video ever.

Done deal, due to a certain lack of competition.

And didn’t you yourself used to be a singing computer science professor?

Eric Siegel: Yeah, in the late 90s at Columbia University I would show up for a lecture with my keyboard and perform, for example, a rock ballad about the angst of debugging (online recordings are available). And in 1999, I published and presented on “Why Do Fools Fall Into Infinite Loops: Singing To Your Computer Science Class” (PDF of paper) at an education conference in Poland.

Dr. Data: Did your students like the songs?

Eric Siegel: Yeah, college students would much rather be at a rock concert, even a really bad one. One metric used in the above education paper was duration of applause.

Dr. Data: You, me, and Lady Gaga, we love that applause.

Eric Siegel: How many countries did you travel to for this video shoot?

Dr. Data: Well, truthfully, I was going to those countries anyway… but the video includes 10 locations across 5 continents – 6 countries plus Antarctica (did you notice the penguins?), which is a continent without any countries. Nor time zones or currency for that matter.

Eric Siegel: So, no green screen?

Dr. Data: Only for the outer space shots.

Eric Siegel: I noticed you play chess against a robot dancer in the video.

Dr. Data: More rap artists ought to also play chess with their dancers. In fact, that dancer (Claudine Quadrat) is actually a chess champ in real life, with a 1998 trophy from Kimball Wiles Elementary School to prove it.

Eric Siegel: That’s funny, cause I also was a childhood winner (at age 13, the 1982 Burlington, Vermont city chess champion of the “Booster” section, the lower of two sections across all ages).

Dr. Data: I’m glad things worked out in the end, what with the audience joining your party and your appearance on the cover of People Magazine.

Eric Siegel: It seems kind of absurd that, as Thomas Davenport and DJ Patil famously put it, data scientist is the sexiest job of the twenty-first century. Isn’t that status reserved for firefighters? But you, Dr. Data, really gave me courage with your mad flow and dance moves. I’ve always felt I was a pop star stuck in a geek’s body. And now I’ve got the moves like Jagger… errr… like his sound engineer.

Dr. Data: By the way, watch for the talented actor Nic Frantela in the video. He narrated the audiobook for Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die – and can be seen as the first of two individual men shown to transform magically into geeks (and elsewhere in the video).

Also check carefully the People Magazine cover at the end for two true-life predictive analytics gurus who frequently speak at Predictive Analytics World: John Elder and Dean Abbott.

Eric Siegel: Who are you, Dr. Data? Where do you come from?

Dr. Data: Isn’t it obvious? I’m your alter ego, dude.

Eric Siegel: You mean I’ve been interviewing myself this whole time?

Dr. Data: Gimme a hug. It’s time you embrace your inner geek.

Eric Image 2015 croppedEric Siegel, Ph.D. (aka Dr. Data) is the founder of the Predictive Analytics World conference serieswhich includes events for business, government, healthcare, workforce, manufacturing, and financial servicesthe author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die—Revised and Updated Edition (Wiley, January 2016), executive editor of The Predictive Analytics Times, and a former computer science professor at Columbia University. For more information about predictive analytics, see the Predictive Analytics Guide and follow him at @predictanalytic. Inquiries: eric@predictionimpact.com.

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March 7th 2016

Wise Practitioner – Predictive Workforce Analytics Interview Series: Geetanjali Gamel from MasterCard

By: Greta Roberts, Conference Chair, Predictive Analytics World for Workforce 2016

In anticipation of her upcoming Predictive Analytics World for Workforce conference presentation, Employee Attrition in the Knowledge Economy – The Value lies in the Details, we interviewed Geetanjali Gamel, Predictive Analytics Leader, Global HR at G Gamel imageMasterCard. View the Q-and-A below to see how Geetanjali has incorporated predictive analytics into the workforce of MasterCard. 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 are doing work to understand and predict employee attrition risk through various lenses. Insights derived from analyzing risk by key talent segments are more likely to be top-of-mind and actionable. There is interest in using this analysis to provide the business with enhanced workforce intelligence around emerging trends in talent flight risk.

Q: If HR were 100% ready and the data were available, what would your boldest data science creations do?

A: Great question! A powerful recommender engine that leverages internal and external employee data on skillsets, experience, personality, interests, working-style and engagement to double up as a dynamic career pathing tool for individuals, and team builder for managers. It could be a multi-purpose solution for   career development and internal talent pipeline building. Also the same data could be harnessed for talent assessment during acquisitions.

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

A: As the value of investing in predictive workforce analytics begins to be more widely known and proven with tangible results, the appetite for complex techniques, including such “black box” methods, will grow.

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

A: Think about who your audience is and what matters most to them. Complex and novel methods may excite data scientists, but almost everyone else wants to know “So, what do I do with this now?” and “What value does this create?”

If the ask is for strategic solutions, focus on explaining how your analysis fits in with the big picture and impacts overall results for the business. If the ask is tactical, translate your work in terms of how someone could use it in their everyday functions.

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

A: Employee attrition is one area in which businesses are leveraging predictive analytics.

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

A: Predictive workforce analytics is still in early stages of development at many companies. From both the business and HR side, it will take patience and commitment to realize the value of investing in this critical initiative. People analytics teams will have to collaborate with HR and business customers to develop a greater level of comfort among them in using data-driven insights to make better decisions.

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Don't miss Geetanjali’s conference presentation, Employee Attrition in the Knowledge Economy – The Value lies in the Details, at PAW Workforce, on Tuesday, April 5, 2016, from 3:30 to 4:15 pm. 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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