January 6th 2016

Wise Practitioner – Predictive Analytics Interview Series: Hans Wolters at Microsoft

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Predicting User and Device Upgrade Issues Moving to Windows as a Service, at Predictive Analytics World San Francisco, April 3-7, 2016, we asked Hans Wolters, Principal Data Scientist, Windows and Devices Group at Hans Wolters imageMicrosoft, a few questions about his work in predictive analytics.

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

A: I have worked in a diverse set of domains.  The predictions ranged from the very serious –  an impending flare for patients suffering from Lupus (SLE) to the more mundane – predicting if players are about to churn out of a social game. At Microsoft, we work on variety of problems, churn prediction is one of them, but my talk at PAW will focus on predicting the Win 10 upgrade experience

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 delivers values in many ways. It helps with planning and allocating engineering resources and feature prioritization.  It also influences supply chains.

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

A: Often it is hard to quantify this, since you cannot run a true A/B experiment once the model is in production, hence an observed lift can, strictly spoken, not be credited to your model with absolute certainty. I did however in one case observe a 20% decrease in churn by accurately flagging the users at risk for churn  and having a willing partner that implemented retention strategies.

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

A: I am not sure if it is surprising, but when reasoning about user behavior it is most powerful to look at longitudinal data and then encode changes over time in an appropriate way. The other insight I found is that what users say they do and what you actually observe them doing is most often not that correlated.

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

A: One  take-away will be that relying on an insider  population for receiving early signals works well for some aspects and can be very misleading for  others.

Q: What are the most common misconceptions people have about predictive analytics?

A: I encounter most often the perception that building predictive models involves predominantly the application of the latest and greatest ML algorithms. In reality the most effort is spent on data cleaning, imputing missing values and then the engineering of great features. Training an algorithm is the least of our worries.

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Don't miss Han’s conference presentation, Predicting User and Device Upgrade Issues Moving to Windows as a Service, on Monday, April 4, 2016 at 11:20 to 11:40 am at Predictive Analytics World San Francisco. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

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December 30th 2015

Wise Practitioner – Predictive Workforce Analytics Interview Series: Frank Fiorille at Paychex, Inc.

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

In anticipation of his upcoming Predictive Analytics World for Workforce conference presentation, Balancing Privacy with Powerful Employee Churn Predictions, we interviewed Frank Fiorille imageFrank Fiorille, Senior Director of Risk Management at Paychex, Inc. View the Q-and-A below to see how Frank Fiorille has incorporated predictive analytics into the workforce of Paychex, Inc. 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: The model being presented is deployed to senior level management at Paychex across the country to identify hotspots for turnover in the next six months.  Key locations and variables are then analyzed to provide added insight for how the data interacts with current retention strategies. 

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

A: I think predicting employee engagement would be a groundbreaking model.  The topic has such an interest today and being able to tell a business unit or employer “Your engagement is going to trend up (or down) in the next sixth months because of X.” would be a really exciting way to positively impact employees at work.

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

A: In my experience some businesses and business units are there.  There are several people I have come across that trust the science and trust the experience of our team that they would deploy the best model we selected.  When you develop that level of trust with your partners, it really gives you the freedom to stretch the limits of your skills and talents.   

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

A: My advice would be to listen first.  Understanding your internal or external customer’s needs should define your actions and goals in your model.  They will listen more readily to the complexities of your process once they are sure that you’ve heard them and are responding to their needs.  On the flip side, don’t feel you need to share every complex detail with them, either.  Sometimes sharing too much information is as bad as not sharing enough. 

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

A: We presented previously on our national retention model that is being used to proactively retain clients that are at a high risk for leaving our services.  Simply going after these clients before they entertain the decision to switch to a competitor greatly impacts your ability to influence that decision.   

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

A: I think the biggest thing, and I think it is happening already today, is when we start using data to make decisions, specifically when it contradicts our gut or instinct.  I believe we are comfortable using data when it reinforces our intuition, but will quickly discard it when the two don’t agree.  I think the key evolution is when we start trusting data over intuition, not blindly or exclusively, but at certain times when our trust is such that we can let the data prove our experience wrong. 

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

A: We do have results that we will share in the presentation.  Because of the new model deployment and the unique methodology used, the results are promising but harder to quantify. 

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Don't miss Frank’s conference presentation, Balancing Privacy with Powerful Employee Churn Predictions, at PAW Workforce, on Tuesday, April 5, 2016 from 11:15 am to 12:00 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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December 21st 2015

Wise Practitioner – Predictive Workforce Analytics Interview Series: Jason Noriega at Chevron

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


In anticipation of his upcoming Predictive Analytics World for Workforce conference co-presentation, Open Sourced Workforce Analytics:  An Overview of 3 Algorithms for Jason Noriega imageCommon Predictive Modeling Situations, we interviewed Jason Noriega, Diversity Analytics Team Lead at Chevron. View the Q-and-A below to see how Jason has incorporated predictive analytics into the workforce of Chevron. 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: Attrition of top performers and other employees with critical skills is a major concern. At many of my prior companies and roles, many of the predictive modeling products I have developed for business units involve:

  • Identifying important variables that impact turnover;
  • Visualizing patterns of high risk for turnover;
  • Utilizing the understanding of those patterns to improve retention.

Business units have used my predictive models to

  • Improve the interviewing/hiring process;
  • Improve hiring of candidates who are most likely to stay;
  • Improve the effectiveness of people managers.

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

A: In that dream scenario, my boldest data science creations would

  • Predict the performance of job candidates before they are hired, and thus improve hiring decisions;
  • Create an employee attrition risk application with controllable variables that can be manipulated to see the impact on attrition risk scores for individual employees;
  • Predict how long it will take to fill a position, given the specific characteristics of that position;
  • Use web scraping techniques to pull public data of employees to combine with internal company data and improve predictive models of attrition;
  • Develop an interactive university hiring simulation model to predict and optimize the diversity of hires.

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

A: Although they may not know it yet, most businesses are ready for black box methods, as long as they are visualized in an intuitive display for business leaders with all levels of technical backgrounds to understand.

For example, in March 2015, I participated in an employee attrition predictive modeling competition on CrowdANALYTIX.com, in collaboration with my teammate, Nery Castillo-McIntyre, and won 1st place out of a pool of 330 data scientists. We used a black box method in order to improve predictive accuracy, visualized the predictions using Tableau, and then presented clear visualizations in the form of an interactive dashboard.

These interactive visualizations helped the client see clearly the critical variables identified by the black box method in an easy to understand format that could be quickly deployed to target individual employees for retention.

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

A: In order to take an analysis from theory to practice, decision makers need to clearly understand the data scientists’ work. For that reason, data scientists must be mindful of their audience, keep the complexity of their work to themselves but be ready to show it upon request, and explain their work in a simple way. Effective visualization is the essential key to success in this regard. 

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

A: Many companies have started using publicly available employee career history data and deriving key variables to use for predictive models. 

For instance, the data used for the employee attrition competition my colleague, Nery Castillo-McIntyre, and I won was scraped from the web.  The variables we extracted from it were highly predictive, and the client could use the patterns we found to hire prospective employees most likely to stay, and to develop targeted retention efforts for employees already onboard.

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

A: In order for the business culture to accept the full promise of predictive analytics, there must be change in three areas: people, processes, and technology.

People: The right leaders must be able, ready and willing to utilize predictive analytics to make more informed decisions. This includes analytically-minded business partners, managers who want more than mere insights into reports, and executives who drive culture change. 

Processes: Driving culture change requires a deep understanding of how HR and the business at large carry out their functions, and where in the process key predictive information is needed to make better decisions. Crucial data elements must be readily stored and accessible to be used to generate value.

Technology:  An important tool to shift the culture, this may include off-the-shelf technology, systems integration and data warehouse construction, as well as open source technology for advanced analytics.

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

 

A: For many of the companies that I have worked for in the past, examples of some of the business results that have been gained from my predictive models included:

  • Making significant improvements to the diversity of university hires;
  • Improving short tenure attrition by hiring candidates who are most likely to stay;
  • Reduced employee attrition following maternity leave.

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Don't miss Jason’s conference co-presentation, Open Sourced Workforce Analytics:  An Overview of 3 Algorithms for Common Predictive Modeling Situations, at PAW Workforce, on Monday, April 4, 2016, from 10:40 to 11:25 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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December 18th 2015

Wise Practitioner – Predictive Analytics Interview Series: Matthew Pietrzykowski at General Electric

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference co-presentation, Advanced Analytics and the Matthew Pietrzykowski imageCorporate Audit Function at Predictive Analytics World San Francisco, April 3-7, 2016, we asked Matthew Pietrzykowski, Senior Data Scientist at General Electric, a few questions about his work in predictive analytics.

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

A: The predictive models that tend to be generated in GE’s Corporate Audit Staff (CAS) are heavily focused on classification outcomes, forecasting and optimization. The types of models used range from logistic regression to random forest classification models. Typically, the models are built to help auditors assess whether there is evidence to support an auditable event or find the optimal or reasonable outcome.  These models tend to be of mixed data types and some are augmented with the results of text mining short form narrative fields.

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

A: In the internal audit space, predictive analytics has seen sporadic use since most of the internal audit work is retrospective with a focus on uncovering mechanisms of potential failure rather than the prediction of new cases. However we have found great potential of it with reducing false positives through targeted reviews of audit field work as well as in risk assessment.  As an example, predictive analytics is being used in executive level planning to help with auditor deployment. The model predicts business sites with a greater risk of showing audit violations.

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

A: One of the models produced predicts the risk of an auditable outcome by classifying business sites using multiple disparate data sets.  The goal was to compile different resources as potential inputs that are used in a typical audit analysis.  These data were from different sources with different schema, so the blending problem was of particular concern.   The final model predicted with a ~90% classification accuracy on test data which is a ~23% improvement over base rate.

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

A: Some of the more surprising outcomes and insights came from necessity.  Most of our data is mixed data types with continuous, categorical, and short form text fields.  Text mining the narrative fields has resulted in both insightful and more impactful overall modeling results than if the narrative fields were omitted.  As an example, we helped one of the businesses leverage their short-form narrative fields by mining them, summarizing them into semantic clouds, and aggregating the results into a summary measure over time.  This time series can then be analyzed for trends that are potential markers for risk events.   We are even seeing evidence to suggest that document term matrices can be used as differentiable attribute data in classification models.

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

A: Data science and in particular, predictive analytics, has a place in the corporate audit function. In fact, it’s a strategic part of GE CAS.  Advanced analytics is a core requirement for our auditors so that they can leverage it in a scientific manner while they are actively auditing our business sites.  The value is seen not only in risk abatement, planning, and forecasting, but it’s forcing a paradigm shift in the organization. 

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Don't miss Matthew’s conference co-presentation, Advanced Analytics and the Corporate Audit Function on Monday, April 4, 2016 at 11:20 am to 12:05 pm at Predictive Analytics World San Francisco. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

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December 14th 2015

Wise Practitioner – Predictive Workforce Analytics Interview Series: Greg Tanaka at Percolata

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

 

In anticipation of his upcoming Predictive Analytics World for Workforce conference presentation, Big Data Driven Labor Scheduling, we interviewed Greg Tanaka, CEO at Percolata.  View the Q-and-A below to see how Greg Tanaka has incorporated predictive Greg Tanaka imageanalytics into the workforce of Percolata. 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?

A: Percolata is focused on helping Specialty Retailers optimizing their sales associate in terms of their selection and scheduling.   Our solution is used by store operations to increase revenue while keeping labor budgets in check.

Q: How is your product deployed into operations?

A: Our solution consists of several components.  To get the labor demand signal, we use our plug and play sensor.  To get the labor supply signal, we have a mobile application for sales associates to give their availability.  These signals feed into our predictive machine learning algorithm to forecast the labor load during the next scheduling period.  The store manager would then review and publish the auto-generated schedule.  

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

A: Workforce management in the physical world is incredibly gut-feel vs. data driven.  As the cost of acquiring this physical world data drops, most decisions would become data-driven as with online marketing is today.  This will lead to a more productive and happier workforce. 

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

A: These are techniques that we use today.  However, our customers are business oriented, and so we wrap this technology with an action oriented solution so that our retail partners can benefit from the technology without having to know the gritty details. 

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

A: It is important to always start with the actors and actions vs. the data.  This is counter-intuitive for most data scientist, but it is critical perspective to have in order to have a solution that will positively impact the business.  

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

A: In our case, it is about our predictive forecasting engine.  We pull in many different data sources like our sensor data, Point of Sales, weather, marketing calendars, etc.  By doing this, we are able to come up with a very accurate demand forecast. 

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

A: People management has historically been very touchy-feely vs. most other areas of the business mainly because acquiring this kind of data was expensive if not impossible.  As the cost of sensors drops, and this physical data becomes much more available, HR and operations departments should be open to piloting this kind of technology to see if it can help the business.

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

A: Our customers typically get a 10% revenue increase with the same labor budget by staffing the right sales associate and the right number of associates at the right time.  

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Don't miss Greg’s conference presentation, Big Data Driven Labor Scheduling, at PAW Workforce, on Tuesday, April 5, 2016 from 10:00 to 10:20 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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December 11th 2015

Wise Practitioner – Predictive Workforce Analytics Interview Series: Michael Li at The Data Incubator

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

In anticipation of his upcoming Predictive Analytics World for Workforce conference pesentation, Finding Top Data Scientists for Your Organization: Optimize the Hiring Process Michael Li imagewith Analytics, we interviewed Michael Li, CEO at The Data Incubator.  View the Q-and-A below to see how Michael Li has incorporated predictive analytics into the workforce of TheData Incubator. Also, glimpse what’s in store for the new PAW Workforce conference.

Q: What is the specific business problem you are solving?

A: There is global demand for highly skilled data scientists across multiple industries and use cases.  While data scientist roles are diverse, the skill-sets required are quite standardized — how to leverage free open-source technology for predictive analytics.  Analytics tools can have a steep learning curve leaving managers to struggle for ways to deeply and quickly train their data science employees.  We’ve found practical, extensive mini-projects force students  to get their hands dirty and produce tangible results.

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

A: The biggest untapped big data use case in HR is around hiring analytics. Most hiring is done solely based on the judgement of a single manager’s hiring experience even though making a bad hire is one of the most costly mistakes a company can make. Leveraging the data from the thousands of similar hires a company has made over the years can help inform managers to make better hiring decisions.

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

A: The predominance of certain models has more to do with a cultural familiarity (linear regression is taught in business school) than with how hard to understand a model truly is. One of the reasons companies invite us in to train their employees (both managers and individual contributors) is to broaden their organization’s familiarity with more advanced modeling techniques like Random Forests and Neural Networks. In the end, these supposed “black box” methods are not actually that much more opaque than so-called “white box” methods. Firstly, using tools like differential analysis and feature importance can give a lot of visibility on how more advanced models work. Secondly — and more importantly — our supposed understanding of how “white box” methods like general linear modeling work is more limited than many realize. In a multi-factor model, it’s hard to keep track of the hundreds of potential correlations amongst inputs — a fact that is often lost in the beguiling simplicity of a linear model. We emphasize both these lessons in our curriculum.

Q: Do you have suggestions for data scientists trying to explain their work to non-technical stakeholders?

A: Thanks for asking this question, Greta! The best advice about explaining data science to non data scientists I can give is to not explain models but to tell stories. Humans relate to stories — not math. Business stakeholders will often accept your analysis (as long as your results are reasonable) and do not care about the subtleties of statistical modeling. However, they will probe to make sure you have thought “outside the data” — looking at the limitations of your model or thinking about factors you were not able to consider — to make sure that your model makes sense in a broader business context.

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

A: HR is still a very gut-driven decision-making culture. To a certain extent, this is absolutely appropriate — there will always be a healthy room for human intuition when dealing with individuals. However, individual hiring managers need to realize that they are only directly involved in a relatively few number of hiring decisions at an organization and that those cases might not be representative of what will come. Ultimately, we have to be humble about the limits of our own personal experience and look to the data for more.

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Don't miss Michael’s conference presentation, Finding Top Data Scientists for Your Organization:  Optimize the Hiring Process with Analytics, at PAW Workforce, on Tuesday, April 5, 2016 from 11:15 to 11:35 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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December 2nd 2015

Wise Practitioner – Predictive Workforce Analytics Interview Series: Kathy Doan at Wells Fargo Bank

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

 

In anticipation of her upcoming Predictive Analytics World for Workforce conference presentation, Beyond Traditional Turnover Creating Value by Quantifying the Impact of Attrition, we interviewed Kathy Doan, Vice President, Community Banking HR Insights & Kathy Doan imageAnalysis 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.

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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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November 29th 2015

A Rogue Liberal: Halting NSA Bulk Data Collection Compromises Intelligence

By Eric Siegel

This Newsweek article, originally published in Newsweek’s opinion section and excerpted here, resulted from the author’s research for a new extended sidebar on the topic that will appear in the forthcoming Revised and Updated, paperback edition of Eric Siegel’s Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (coming January 6, 2016). Preorder today to delve more deeply on this topic.

 

I must disagree with my fellow liberals. The NSA bulk data shutdown scheduled for November 29 is unnecessary and significantly compromises intelligence capabilities. As recent tragic events in Paris and elsewhere turn up the contentious heat on both sides of this issue, I'm keenly aware that mine is not the usual opinion for an avid supporter of Bernie Sander (who was my hometown mayor in Vermont).

But as a techie, a former Columbia University computer science professor, I’m compelled to break some news: Technology holds the power to discover terrorism suspects from data—and yet to also safeguard privacy even with bulk telephone and email data intact. To be specific, stockpiling data about innocent people in particular is essential for state-of-the-art science that identifies new potential suspects.

I'm not talking about scanning to find perpetrators, the well-known practice of employing vigilant computers to trigger alerts on certain behavior. The system spots a potentially nefarious phone call and notifies a heroic agent—that's a standard occurrence in intelligence thrillers, and a common topic in casual speculation about what our government is doing. Everyone's familiar with this concept.

Rather, bulk data takes on a much more difficult, critical problem: precisely defining the alerts in the first place. The actual “intelligence” of an intelligence organization hinges on the patterns it matches against millions of cases—it must develop adept, intricate patterns that flag new potential suspects. Deriving these patterns from data automatically, the function of predictive analytics, is where the scientific rubber hits the road. (Once they’re established, matching the patterns and triggering alerts is relatively trivial, even when applied across millions of cases—that kind of mechanical process is simple for a computer.)

 

Newsweek Image croppedIt may seem paradoxical, but data about the innocent civilian can serve to identify the criminal. Although the ACLU calls it “mass, suspicionless surveillance,” this data establishes a baseline for the behavior of normal civilians. That is to say, law enforcement needs your data in order to learn from you how non-criminals behave. The more such data available, the more effectively it can do so.

Here's how it works. Predictive analytics shrinks the unwieldy haystack throughout which law enforcement must hunt for needles—albeit by first analyzing the haystack in its entirety. The machine learns from the needles (i.e., known perpetrators, suspects, and persons of interest) as well as the hay (i.e., the vast majority that is non-criminal) using the same technology that drives financial credit scoring, Internet search, personalized medicine, spam filtering, targeted marketing, and movie, music, and book recommendations. This automatic process generates patterns that flag individuals more likely to be needles, thereby targeting investigation activities and more productively utilizing the precious bandwidth of officers and agents. Under the right conditions, this will unearth terrorists who would have otherwise gone undetected.

This increasingly common practice also drives other crime fighting functions. Today's law enforcement organizations predictively investigate, monitor, audit, warn, patrol, parole, and sentence…

CONTINUE READING: Access the complete article in Newsweek, where it was originally published.

 

Eric Image 2015 croppedEric Siegel, Ph.D. is the founder of the Predictive Analytics World conference series—which covers both business and government deployment—the author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, Revised and Updated Edition (Wiley, January 2016), and a former computer science professor at Columbia University.

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November 25th 2015

Wise Practitioner – Workforce Predictive Analytics Interview Series: Jonathon Frampton at Baylor Scott & White Health

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


In anticipation of his upcoming Predictive Analytics World for Workforce conference presentation, Visualizing Organizational Movement for Opportunity Identification, we interviewed Jonathon Frampton, Director, People Analytics & Reporting at Baylor Scott & Jonathon Frampton ImageWhite Health. View the Q-and-A below to see how Jonathon Frampton has incorporated predictive analytics into the workforce of Baylor Scott & White Health. 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 in a state of constant iteration/development with our deliverables and since most of our current work shows up in presentation form much of the final use is masked to us.  This is actually a point of current focus for our team, capturing our “#wins” as we call them when our data / inference are used for the greater good.  Much of our current deployment focus around the enablement of our HR business partners and directors.  This team has been very quick to run with our results and distribute them into the workforce as needed.

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

A: I LOVE this question!  We focus on creating a more efficient experience for our people leaders, as such an area ready for an end to end predictive / prescriptive solution would handle the entire process how our workforce is staffed and how mobile employees are deployed.  A solution that would only create, approve and source positions as they become predictively necessary given parameters that include elements of productivity and employee satisfaction would free up our leaders time to focus on our patient care.  Taking it a step further allows for thinner more qualified slate of candidate to be presented in a timely manner, given that our recruiting force would know well ahead of time what pipelines need to be tapped and ready!

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

A: I am not sure that the businesses like the business of people will ever be 100% ready, or even that they should be.  It is one thing to set a model churning our trades at a nanoseconds pace, but can you imagine making a decision on someone’s future as quickly?  That being said, if the focus of workforce predictive analytics was less on the true HR work and focused on people enablement actions the adoption rate would be much quicker. 

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

A: This is a great question and one I am sure many are grappling with.  Like many we are pretty young in this game, so I can speak to how we are currently seeing success in this area.  We have gone with an internal consultant model for our organization.  Our group does sit within HR, but we have an individual (consultant) wholly focused on taking the work of our analysts and educating, training and enabling our HR professionals to gain full value of it.  This has been a huge bonus for us as the street flows in both directions and has turned into a wonderful quantitative / qualitative feedback loop of their commentary flowing in our direction feeding increasingly relevant results to theirs.

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

A: As we are a very young group we do not yet have any direct results from a predictive product driven decision as the majority of our deliverables have been inferential in nature allowing our business partners and operations teams to infer the predictive nature of the data.  This is by design as the idea of jumping from a finally standardized set of HR KPIs directly into predicting results can (and should) be quite overwhelming for our customers, however we are rolling out bits and pieces as I type so look for great things soon.

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

A: I think the (re)evolution has already begun, but it is far from over.  What we need is the consistent delivery of increasingly complex results from HR data.  I think we (HR) will follow the same trajectory seen by marketing in recent years with adoption following an exponential curve after hitting a “tipping point” a number of years in.  Additionally a strong focus on educating our operational customers on the uses and values of our people insights would speed adoption rates and create more of a pull effect.

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Don't miss Jonathon’s conference presentation, Visualizing Organizational Movement for Opportunity Identification, at PAW Workforce, on Monday, April 4, 2016, from 10:40 to 11:25 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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November 18th 2015

Wise Practitioner – Workforce Predictive Analytics Interview Series: Ben Waber of Humanyze

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


In anticipation of his upcoming Predictive Analytics World for Workforce conference presentation, Using Wearables and Big Data to Reinvent Management, we interviewed Ben Ben Waber imageWaber, CEO at Humanyze. View the Q-and-A below to see how Ben has incorporated predictive analytics into the workforce of Humanyze. 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: Global services divisions use our technology to create and test new workplaces.  Using a combination of wearables and digital data analytics, we identify what behaviors lead to higher performance and how new workplace designs will impact those behaviors.  Our customers then use our technology to A/B test these changes before rolling them out to the entire organization.

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

A: Automate org charts, compensation, and hiring while putting HR in charge of setting up A/B tests to shape these systems.

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

A: These methods are another input to decision making, and with limited understanding they will be of limited usefulness.  As People Analytics divisions become the norm over the next 10 years, we'll see more advanced methods become more common.

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

A: If your explanation wouldn't make sense to a random person on the street, you need to simplify.

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

A: Predicting what org charts will positively impact performance and retention.

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

A: Today companies make decisions about their people after an executive reads an article about what a cool company like Google does.  We need to move from that model to one closer to marketing: have a good idea, validate and predict with current data, test.

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

A: We have used people analytics to improve loan sales in a major multinational bank by over 10%, over a billion euros in additional revenue a year.

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Don't miss Ben’s conference presentation, Using Wearables and Big Data to Reinvent Management, at PAW Workforce, on Monday, April 4, 2016 from 3:05-3:25 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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