Archive for April, 2017

April 25th 2017

Wise Practitioner – Predictive Workforce Analytics Interview Series: Emily Pelosi at CenturyLink

By: Greta Roberts, Conference Chair, Predictive Analytics World for Workforce 2017


In anticipation of her upcoming Predictive Analytics World for Workforce conference presentation, How CenturyLink Measures How Well Leaders Manage Their Organizations, we interviewed Emily Pelosi, HR Emily Pelosi IMAGE PAW BlogAnalytics Leader at CenturyLink. View the Q-and-A below to see how Emily Pelosi has incorporated predictive analytics into the workforce of CenturyLink. Also, glimpse what’s in store for the new PAW Workforce conference, May 14-18, 2017.

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

A: A product that we created called the Talent Index was shared with our senior leaders earlier this year, and the results contributed to goal planning and people management focus for 2017. The Talent Index is a tool we developed to measure how effectively our leaders are managing their organizations based on our core HR principles. It takes a comprehensive set of HR metrics, groups them into research-based factors, and produces a score through a series of weights and targets that reveals how closely they are aligned with our talent management practices. One aspect of the Index that helped it to be a success was the way it was designed. It was built with the end in mind, which was to give leaders a clear idea of where their people opportunities are. Leaders can look at their scores on the individual factors to identify what is driving their overall index score. Furthermore, they can look at the individual components within these sub scores to see what specific areas are drivers. This allowed our leaders to walk away with a very targeted idea of what they need to improve going forward, whether it be increasing engagement, providing more opportunities for high potential employees, or managing lower performers.

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

A: HR has historically struggled with demonstrating the value of investing in people. If more data was available on employee preferences, attitudes, and day-to-day experiences, we could have a better idea of how employees are impacted by the organization. Then, if we have a better idea of how employees are impacted by the organization, we can connect this data to financial and operations targets and make a clear connection between people processes and ROI. This is already being done by some organizations, but not many are doing it well. This is still an area in which HR can make significant progress.  

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

A: “Readiness” for these types of methods can vary between organizations based on their culture, resources, capabilities, and other factors. That being said, assuming the right systems are in place I think businesses are actually ready now. The utility of these methods is driven by the users’ ability to identify meaningful data, connect it to business-critical outcomes, and disseminate results to the movers and shakers in their organizations. In other words, if you use these methods for issues that are actually important to the business and you can articulate what your analysis means and why it matters, you can utilize more advanced workforce predictive methods.   

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

A: Early in my career I had a mentor ask me to explain a complex analysis “like I would explain it to my mom.” Now, my mom is very intelligent, but data science is not her specialty. The point was to consider the perspective of my audience. That has always stuck with me. Stay away from jargon and key words that are specific to the data analysis. You’re telling a story, so don't be afraid to get creative. Make it interesting—use analogies to help explain your work when you can, especially if you know your audience and what would resonate with them. If you can’t avoid including complex terms or details, build up to these concepts by introducing key ideas one at a time. At the end of any presentation, conversation, etc., your goal is for the audience to walk away with the 2-3 key points. Highlight these key points early on in your discussions—don’t keep the audience guessing or lead them down a winding path. 

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

A: Predictive analytics is taking the guesswork out of solving workforce challenges. It is reducing the negative impact that results from bias and decision making based on emotions and/or opinions. In HR at CenturyLink, analytics is core to decision making especially for strategic decisions that have a big impact. We’ve leveraged analytics for identifying new engagement initiatives, changing workforce policies, validating our performance process, predicting successful hires, and predicting turnover among other workforce trends. 

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

A: One of my favorite books that came out last year is called the “The Rise of HR” by Dave Ulrich, William Schiemann, and Libby Sartain (editors). A chapter written by Seth Kahan offers “12 predictions for a new world,” which proposes the challenges HR will be up against in the future. One of Seth’s predictions is that knowledge execution will become one of the most valuable assets in the world. According to his prediction, the ability to execute on knowledge will be more important than profitability, politics, and other powerful influences. This directly applies to how organizations need to evolve to accept the full promise of predictive analytics. Data has never been more accessible to organizations, and predictive analytics allows us to use this data to obtain knowledge that hasn’t been available before. Businesses that want to be successful in the future need to put predictive analytics at the epicenter of strategy and fully commit to making decisions based on these insights rather than biases and intuition.  In an ideal state, predictive analytics is a central part of strategic decision making by connecting data across multiple business units.    

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Don't miss Emily's conference presentation, How CenturyLink Measures How Well Leaders Manage Their Organizations, at PAW Workforce, on Tuesday, May 16, 2017 from 3:55 to 4:40 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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April 17th 2017

Wise Practitioner – Predictive Analytics Interview Series: Holly Lyke-Ho-Gland and Michael Sims at APQC

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of their upcoming conference co-presentation, Change Management for Holly Lyke 2Establishing a Data-Driven Culture, at Predictive Analytics World for Business Chicago, June 19-22, 2017, we asked Holly Lyke-Ho-Gland, Principal Research Lead at APQC and Michael Sims, Research Analyst at APQC, a few questions about their work in predictive analytics.

Q: In your work with predictive analytics, what behavior or outcome do your models predict?

A: The organizations we study use predictive analytics to forecast just about anything: Michael Sims 3consumer behavior, employee turnover, exchange rates, etc. For example on of our study participants was able to pinpoint trends in attrition by the employee tenure and potential.

Q: How does predictive analytics deliver value at your organization – what is one specific way in which it actively drives decisions or operations?

A: Predictive analytics allows organizations to make decisions that are a) better informed and b) less prone to cognitive bias. In short, predictive analytics promotes objectivity. Another participant of this study was able to improve its understanding of its customers by integrating quantitative trends as context its traditional qualitative customer feedback. The trends helped decision makers understand what feedback was related to an actual impact on the overall customer experience. 

Q: Can you describe a quantitative result, such as the predictive lift of your model or the ROI of an analytics initiative?

A: Another study participant used language dictionaries and step-wise regression to see if internal social media use could predict employee engagement scores—with the explicit goal of using real-time social media data to create an understanding of real-time employee engagement. The study was a success and the language dictionaries were able to account for approximately 48 percent of the variation in engagement scores.

Q: What surprising discovery or insight have you unearthed?

A: The most surprising thing that we have found is the continued struggle to effectively adopt data-driven decision making in organizations. Though organizations continue to invest in data and analytics capabilities, they still indicate that establishing a data-driven culture continues to be among their greatest challenges. Often, this is a result of poor integration and communications between the business and the analytics sides of the house.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.

A: When building a data-driven organization, don’t start with sophisticated tools and technology; begin by creating a data-driven culture. Like any other shift in how an organization operates, a well thought out change management plan is necessary to ensure you can garner the benefits of your investment in data-driven decision making.

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Don't miss Holly and Michael’s conference co-presentation, Change Management for Establishing a Data-Driven Culture on Wednesday, June 21, 2017, from 10:00 am to 12:45 am at Predictive Analytics World Chicago. Click here to register to attend. Use

By: Eric Siegel, Founder, Predictive Analytics World

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April 3rd 2017

Wise Practitioner – Predictive Analytics Interview Series: Natasha Balac at Data Insight Discovery, Inc.

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of her upcoming conference co-presentation, Identifying Unique Gamer Types Natasha Balac Blog Page IMAGEUsing Predictive Analytics, at Predictive Analytics World San Francisco, May 14-18, 2017, we asked Natasha Balac, CEO and Founder of Data Insight Discovery, Inc., a few questions about her work in predictive analytics.

Q: In your work with predictive analytics, what behavior or outcome do your models predict?

A: It varies, as we work with clients across many verticals from condition based maintenance to forecasting sales.  One great example that we will present at PAW is in the exciting world of Marketing.  This area has been one of the fastest and most electrifying adopters of predictive analytics methods, with countless reports of significant lift and ROI over more traditional approaches.  With the onset of Big Data, we can now utilize even larger and more diverse data to optimize data-driven marketing decisions.

Q: How does predictive analytics deliver value to your clients – what is one specific way in which it actively drives decisions or operations?

A: The ability of companies to sharply focus marketing and PR efforts has improved tremendously with predictive analytics.  The insights provided by predictive analytics reveal the specific pattern of characteristics and behavior profiles of customers most likely to buy a particular product.  Predictive analytics methods take in a wide variety of data, and systemically enable refined customer segmentation and customer persona optimization.  This enables clients to approach customers with a more tailored and personalized message, and focuses marketing resources on the customers more likely to respond.

Q: Can you describe a quantitative result, such as the predictive lift of your model or the ROI of an analytics initiative?

A: Non-disclosure agreements prevent us from sharing specifics regarding projects, but numerous projects showed several orders of magnitude lift in sales through the application of predictive analytics models.  The outcomes from the models allowed the clients to segment and approach the right groups of customers with the right message.

Q: What surprising discovery or insight have you unearthed in your data?

A: Our predictive analytics work for one client produced results that suggested significant, unexpected demographics were being overlooked.  These insights lead to a change in how the client targeted customers.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.

A:  Customer behaviors and preferences can be quite surprising, non-intuitive and difficult to predict.  Building personalized offers, and delivering engaging, personalized consumer experiences is the key to successful, optimized marketing campaigns.  Utilizing sophisticated segmentation and data-driven insight allows you to target the right customer with the right message consistently.

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Don't miss Natasha’s conference co-presentation, Identifying Unique Gamer Types Using Predictive Analytics on Tuesday, May 16, 2017 at 11:45 am to 12:00 pm at Predictive Analytics World San Francisco. Click here to register to attend

By: Eric Siegel, Founder, Predictive Analytics World

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