September 14th 2017

Wise Practitioner – Predictive Analytics Interview Series: Feras Batarseh at George Mason University – George Washington University

By: Jeff Deal, Program Chair, Predictive Analytics World Healthcare

In anticipation of his upcoming conference presentation at Predictive Analytics World for Healthcare NewFeras Batarseh York, October 29 – Nov 2, 2017, we asked Feras Batarseh, Research Assistant Professor, George Mason University – George Washington University, a few questions about incorporating predictive analytics into healthcare. Catch a glimpse of his presentation, Evaluating the Quality of State's Healthcare Using Big Data Analytics, and see what’s in store at the PAW Healthcare conference in New York City.

Q: In your work with predictive analytics, what area of healthcare are you focused on?

A:  In this upcoming talk, the focus will be: healthcare policy. The goal is to shed light on how data & predictive analytics can drive better decision making and provide better insights to policy making in healthcare and potentially other domains.

Q: What outcomes do your models predict?

A: The model that will be presented at PAW predicts the quality of service (QoS) of healthcare at states, cities, and counties across the country. The goal is to evaluate service, and find correlations of quality to certain policies and practices.

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

A: We are injecting predictive analytics at multiple federal agencies – and as an educational institution (GMU), we aim to spread the good news that the US government can 'function' better through data science. Therefore, and based on that simple notion, a number of experts collaborated and published the following book to highlight ''policy making through data science'', available through this link.

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

A: Multiple use cases developed – I will cover some of them in my talk.

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

A: In a nutshell, there are many insights buried in the data which can be used to provide better overall patient experience. Certain states can highly improve their QoS in healthcare by applying certain successful practices. For example, states that 'encourage & promote' preventive care, tend to have 'healthier' citizens.

Q: What areas of healthcare do you think have seen the greatest advances or ROI from the use of predictive analytics?

A: Hospital service improvements, bed-side care, patient re-admissions, better policy making, and other metrics for success.

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

A: Through data science, the American public can be involved in healthcare policy making. Predictive analytics can empower the government to transforming from a government of bureaucracies and 'black-box' policy making to a government of data-driven processes; a difficult pursuit, but a compulsory one.

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Don't miss Feras’ presentation, Evaluating the Quality of State's Healthcare Using Big Data Analytics, at PAW Healthcare on Monday, October 30, 2017, from 2:40 to 3:00 pm. Click here to register for attendance.

 

By: Jeff Deal, Conference Chair, Predictive Analytics World Healthcare

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September 12th 2017

Wise Practitioner – Predictive Analytics Interview Series: Anasse Bari, New York University

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Wall Street and the New Data Paradigm at Predictive Analytics World for Financial in New York, Oct 29-Nov 2, 2017, we asked Anasse Bari, University Professor ofAnasse Bari Computer Science at New York University, a few questions about his work in predictive analytics.

Q: Harvard Business Review proclaimed that data scientist is the Sexiest Job of the 21st Century. In your role at New York University, how do you prepare your students to become data scientists?

In the graduate predictive analytics course I teach, my aim is to equip the next generation of data scientists for a successful profession in data analytics by teaching them the algorithms and tools they need to discover hidden similarities in data, effectively mine decisions’ rules, and ultimately, predict the outcomes of specific events.

To do so, I tend to take a more practical approach by using real life cases, such as large-scale targeted marketing or gene expression microarray data classification of cancer.

However, taking one data science class or knowing programming languages, such as R or Python, does not necessarily make one a data scientist. Nonetheless, it can expose one to the complexities of the subject and motivate one to learn more. Becoming a data scientist is a long journey that involves a steep learning curve, hard work and curiosity. 

In my opinion, what distinguishes a data scientist is the possession of a unique combination of technical skills, critical thinking and communication skills.   

A data scientist should have a passion for analyzing data. They should not only have a solidified understanding of data engineering principles, of supervised and unsupervised learning algorithms, and of large scale frameworks to process millions of streamed rows of data, but they must also have substantial data-gathering and -cleaning skills, and must have accumulated applied data science experience.

Moreover, a data scientist should also be a creative strategist who can design solutions that can generate actionable insights.  To help my students foster a passion for analytics, I ask them to complete a semester long group project as part of their coursework where they have to adopt the cross-industry standard process for data mining (CRISP-DM) and design a data analytic that addresses a problem of their choice. Every semester I am impressed by the creativity of my students. Some projects that have been completed include:

  • Predicting rent prices in New York City using online restaurant reviews

  • Recommending courses to students with the aim of maximizing their success rates

  • Extracting predictive features that make a song popular

  • Using open data to forecast hot spots in NYC at a given time using biking data to potentially help ease congestion in the city

Most of these projects, including their approaches and results, have been presented to wide audiences at major conferences.

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

Access to information has always been the sine qua non to being successful on Wall Street. Investors base their decisions on traditional data sources, such as quarterly earnings reports, financial statement filings to the U.S. Securities and Exchange Commission (SEC), and sometimes the so-called “expert networks.”

My research on predictive analytics in finance involves developing an evidence-based decision support framework to model financial markets. The framework mines data sources to generate an array of hypotheses and their associated evidence. In one part of our study, we build models that can predict earnings and post-earnings of stock-price movements by probing the wisdom of crowds using alternative data sources.

Alternative Data (“alt-data”) is a relatively new term in the world of investment banking. It refers to data collected from non-traditional financial data sources to obtain meaningful insights about the entity in question beyond what is easily available from traditional sources. In investment banking, for instance, data scientists mine satellite images of shopping mall parking lots as an alternative data source to predict revenue numbers of the business entity in question.

The notion of the wisdom of crowds originated from Aristotle’s “doctrine of the wisdom of the multitude.”  It refers to the idea that large groups collectively can make smarter decisions than individual experts. This principle was adopted in many fields, such as behavioral economics, politics and physiology. In the world of predictive analytics, collective judgment can help in making accurate predictions. I believe that a crowd becomes wise and powerful when it is diverse. Individual opinions that are diverse tend to emerge from a wise crowd whose insights can lead to good predictions.  However, a crowd might lose its wisdom when its members are influenced by each other’s ideas.  It is important to know who is in the crowd. In one experiment, the predictive models we developed that mined alternative data sources from the wisdom of online crowds yielded better stock market predictions than the Wall Street consensus. The Street consensus is usually derived from forecasts made by a crowd of analysts who provide research coverage on a specific company or market segment. However, this crowd is inter-influential, which does not always yield accurate predictions.

We can now prove that movements in the stock market are driven by alternative data sources, such as ontologies extracted from news articles, collective opinion mining of micro-blogs, online search trends and the wisdom of customer crowds. Alternative data promises access to new information that has the potential to add value to the traditional investment process. Predictive analytics can help in interrogating alternative data sources and connecting the dots to help the investor make better decisions.

Q: How does predictive analytics deliver value to hedge funds and Wall Street firms? What is one specific way in which it can actively drive decisions or operations?

PA is already reshaping the financial landscape. Trading for the most part has become automated, and portfolios can be automatically generated. The next wave is about deploying predictive models that can connect the dots from different alternative data sources to provide an edge to the decision-making process for investors. Shipping data, for instance, has been used to forecast Apple iPhone sales. Since most Apple products are produced in China, it was discovered that there is a correlation and causality between Apple shipment numbers and iPhone sales.

In another scenario, image recognition algorithms were applied to the car park data of a major US retailer. These algorithms structure raw images into a data matrix of average numbers of cars per parking lot on a given day. It was discovered that the percentage change in car count could in fact be a predictive feature of the revenue of the retail store in question. Similarly, many hedge funds are applying data classification algorithms to satellite photos of agricultural fields to predict crop yields. In health care, analyzing drug trial data by geography, gender and demographics can help healthcare providers target the right client base.

In the world of finance, one key lesson is the influence of psychology on the behavior of financial markets. Many investors can be “irrationally exuberant” when making financial decisions. While in some cases, irrational exuberance can reap considerable returns for hedge fund managers, it does not always prepare them for unfavorable outcomes. Predictive analytics does not have emotions, or sensitivities. Hence, relying on an ensemble of predictive models that can learn from the wisdom of crowds as a decision advisor can help mitigate irrational exuberance among investors.

Q: What is the main take-away from your research?

Extracted insights from alternative data sources can provide a competitive imperative for investment strategies in the short and long term. There is a consistent predictive correlation between opinion mining scores based on the wisdom of crowds as seen in news articles, Twitter and other data sources, and the movements of financial markets.

Collecting more data, however, does not necessarily lead to a better prediction. As data scientists, our reliance on big data needs to be supported by heightened critical thinking. While the parallels between financial markets and indicators from alternative data sources are certainly exciting with regard to making better predictions, one must be cautious. Conclusions must be drawn with discretion because correlation, if taken out of context, can be misleading, and correlation is not synonymous with causation. Standing on the forefront of the data science revolution, we must tread critically before drawing conclusions.

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Don't miss Anassi’s conference presentation, Wall Street and the New Data Paradigm, on Tuesday, October 31, 2017 at 11:40 am to 12:00 pm at Predictive Analytics World for Financial in New York. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

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July 31st 2017

Wise Practitioner – Predictive Analytics Interview Series: Steve Weiss at LinkedIn

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, The Sprint for Teaching Data Science: LinkedIn Learning, Analytics and the New Era Steven Weiss PAW BLOGof Just-In-Time Skills Training at Predictive Analytics World for Business New York, Oct 29-Nov 2, 2017, we asked Steve Weiss, Content Manager, Data Science and Business Analytics at LinkedIn, 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: At LinkedIn, (among other things) we track employment supply and demand, toward predicting where market opportunities will be. We want to help people with their career prospects, and we want to help employers find the best candidates to fill job openings. At LinkedIn Learning, we track specific skills-demand in order to gain insight we can apply toward creating the online learning courses that will—in aggregate—help the most people. That can mean a fairly broad variety of course topics—some courses will be extremely high-demand and others will be fairly narrow and focused on very vertical skills or topic coverage—but the overall aim is to provide a robust set of very helpful job skills for the present and near-future. Provide the training people need now, but also what they’ll need over the next 18-36 months. Predictive analytics help us skate to where the puck will be, not just where it’s at this moment. Things just move too fast in job markets to play it any other way.

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

A: Given the high production values we use in our training courses, we don’t have unlimited resources with which to create our content at LinkedIn Learning; we’re continuing to add production resources and personnel to aggressively stay ahead of demand of LinkedIn members, including all of our Enterprise clients (we have accounts with virtually the entire Fortune 500, along with thousands of corporate clients globally). So we need to choose which course topics to cover very carefully. Predictive analytics helps take the guesswork out of the process, although to be clear, we definitely rely on the hard-won market knowledge and network-based wisdom of our content managers to flight-test the theories borne of data-driven insights. And vice versa…

As one example: when I came aboard as content manager for data science and business analytics two years ago, tasked with building a course library where none had existed in that overall category, the common wisdom at Lynda.com (this is right before we were officially, operationally integrated with LinkedIn as LinkedIn Learning) was that we were over-represented with Excel courses, and perhaps under-represented in topics like Qlik and Mathematica. But analyzing the top 50,000 listed skills pulled from LinkedIn’s 400 million users (now over 500 million, BTW), and measuring those against the most in-demand skills sought by all the companies doing recruiting and in-house training via LinkedIn, showed us the opposite: That Excel, perhaps unsexy as it might seem to hardcore data scientists, was still easily the tool of choice for a surprisingly large number of people doing data analytics work. The data showed us that—as always in an ever-dynamic tools market—certain tools, languages and platforms were doing sometimes surprisingly better than others. Which doesn’t mean those products and topics are on the way out by any means, but the data can help us refine our product roadmap strategies. 

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

A: We use our analytics to test and verify emerging topics, so say, two years ago Apache Spark was only beginning to emerge as a listed skill being used by LinkedIn members or as a skill being sought after by employers. I knew that was about to change, but wasn’t going to show up in all the data we use. So what’s interesting is to go ahead and line up a course on Spark for Data Science, as a strategic content development decision, and wait to see Apache Spark appear and then grow in our skills/demand analysis. When, one or two quarters later you see the demand manifesting in the numbers, it’s a great feeling: you release the course, you see the numbers reflecting heavy course usage (in terms of views and number of viewers), and customer feedback thanking you for being ahead of the curve and providing forward-looking skills training for people to grow their careers. And you hit the Go button on more courses in the same area—as we’ve been doing for a while now for Spark—and you check against your competition, who maybe haven’t jumped into this part of the market yet, and you’re receiving feedback from enterprise clients who are telling your corporate sales team that they’ll adopting LinkedIn Learning due in part to the fact that you’ve got the kind of Apache Spark coverage they need, right now… and it feels great.  You’re using analytics to win for the user.

And if it doesn’t… if that demand you suspected was going to materialize doesn’t actually arrive—and fortunately this hasn’t happened yet—it provides a sanity check. Makes you examine your on-the-ground resources and other data-gathering techniques and troubleshoot them. Predictive analytics can make you look really smart, but it also keeps you humble.

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

A: As mentioned previously an awful lot of people are using Excel to do limited-scale data science. That—along with the increase in numbers of people listing “data science” and related topics among skills they’re offering, pursuing, or recruiting for—suggests the entire field is growing. Put another way, it suggests that data science isn’t just for data scientists any longer. So this in turn suggests that there’s a growth market for entry-level data science topics across the board, and that where you used to focus on filling topic-area needs (for emerging areas) beginning at the practitioner level (intermediate and up,  through advanced-level course), you now need to build out those learning paths at the lower end as well, since it’s beginning to appear that many people are refocusing their IT skills (or are entering anew) on the so-called “data science and analytics” career market. That’s part of what makes this job so fun: you get a chance to learn where things might be headed—from the data you’re collecting and analyzing—and then you get to develop and test hypotheses about what it all means, and how you adjust your product strategies to improve the lives of your customers.

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

A: We’ll learn more about the ways LinkedIn and LinkedIn Learning are constantly developing analytics tools and reporting, such as our monthly LinkedIn Workforce Reports. And I’m looking forward to providing insights about the first round of results for the LinkedIn Economic Graph Research program. 

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Don't miss Steve’s conference presentation, The Sprint for Teaching Data Science: LinkedIn Learning, Analytics and the New Era of Just-In-Time Skills Training on Monday, October 30, 2017 at 11:20 am to 12:00 pm at Predictive Analytics World New York. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

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July 28th 2017

Wise Practitioner – Predictive Analytics Interview Series: Emilie Lavoie-Charland at The Co-operators

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of her upcoming conference presentation, Which Predictive Model Will Best Help Increase Retention? at Predictive Analytics World for Business New York, Oct 29-Nov 2, 2017, we asked Emilie Emilie Lavoie-CharlandLavoie-Charland, Research & Innovation Analyst at The Co-operators, 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: As I frequently work with our marketing team, I predict the client’s reactions to the actions taken by the agents (those who sell insurance products). These reactions can range from accepting an up-sell (upgrading a product) or a cross-sell (purchasing a new product) to being unsatisfied and wanting to leave the company. I also have the opportunity to work with underwriting teams; on those occasions, I can predict the duration and/or cost of claims.
 
Q: How does predictive analytics deliver value at your organization – what is one specific way in which it actively drives decisions or operations?
 
A: We highly encourage our agents to use the predictive insight that we provide, but are fully aware that our predictions have limitations (agents have face to face conversation with our clients while we work face to face with the data). Our work currently supports the agents by providing them prescriptive insights into the possible next best interactions with our clients. My team (Business Intelligence) strives towards the democratization of analytics, and this is where true value can be delivered: when those asking questions can do the predictive analytics and understand the trends. 
 
Q: Can you describe a quantitative result, such as the predictive lift of your model or the ROI of an analytics initiative?
 
A: The survival retention model, which will be described in my presentation at PAW, identified a 10% of our population with a true-lift of 5.6, meaning that contacting the clients in this segment increased their retention by 5.6 units versus not contacting them. But all other segments developed by the survival retention model had true-lifts near 0. When we switched to a true-lift retention modeling, that will also be presented; we were able to identify 60% of our population where it was worth to make a contact.
 
Q: What surprising discovery or insight have you unearthed in your data?
 
A:  In the univariate analysis of our survival retention model, both the gain and the loss of an insurance product in the past year increased the churn risk of the clients. This was surprising as we had previously observed that the more products a client had, the less likely the client was to churn! In other words, we would have expected that the gain of a product would increase the number of products owned by the clients and therefore decrease the risk of churn. As I want to keep some content for my presentation, you will have more insight into this surprising discovery if you attend!

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

A: It’s easy to build a simple predictive model based on solid statistical theory! But if you want your predictive analytics to bring value to your organization, you must identify an area where your predictions will be used and make sure that your model is statistically correct as well as performant for the area where it will be used!

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Don't miss Emilie’s conference presentation, Which Predictive Model Will Best Help Increase Retention? on Monday, October 30, 2017 from 3:55 to 4:40 pm at Predictive Analytics World New York.  Click here to register to attend

By: Eric Siegel, Founder, Predictive Analytics World

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June 16th 2017

Wise Practitioner – Predictive Analytics Interview Series: Andrew Burt at Immuta

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, Regulating Opacity: Solving for the Conflict Between Andrew Burt PAW Blog imageLaws and Analytics at Predictive Analytics World for Business New York, Oct 29-Nov 2, 2017, we asked Andrew Burt, Chief Privacy Officer & Legal Engineer at Immuta, a few questions about his work in predictive analytics.

Q: In your work with predictive analytics, what challenges do you most frequently encounter?

A: I consistently come across three types of problems faced by data science teams large and small: access, collaboration, and governance issues. On the access side, data scientists have an incredibly difficult time getting to the data they need—either because of IT architecture issues or institutional issues, where different teams “own” different data sets and have varying incentives to make their data available. The truth is, though, that there are a huge number of reasons why data scientists have a hard time getting access to the data they need. On the collaboration side, data scientists need to work in groups, centralizing their shared knowledge and working towards a common goal. This can be incredibly difficult as well, thanks to distributed teams and high turnover rates.  

Q: And as a lawyer, how do you see the governance challenges come across?

A: Regulatory concerns limit predictive analytics in ways that data science teams and lawyers frequently don’t realize. As organizations move from a business intelligence framework, where analysts were the primary end consumer of data, to a machine-based framework, where machine learning models themselves are replacing analysts in a number of ways, new governance issues are arising that are challenging the way data science gets done.

I’ll cite just one example: the EU’s General Data Protection Regulation, or GDPR, which can impose fines of up to four percent of global revenue, can require that meaningful information be available about the logic of machine learning models, which consumers can have a right to access. Before, you could ask the business intelligence analyst what she or he was doing with the data if you needed to. Increasingly, though, we’re going to need to ask the models themselves, and that requires an entirely different framework for governing and supervising how predictive analytics are applied.

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

A: A host of ways, from consumer identification and retention efforts, to streamlined decision making from the bottom of organizations all the way up to their c-suites. I think what’s most fascinating—and most powerful—about the current state of predictive analytics is the move towards automation. Data science is really eating entire organizations in the sense that data science teams’ products are becoming cross-vocational; you can have one data science team, for example, building models that span multiple areas of expertise, covering logistics and manufacturing to even medical diagnostics. There’s one fascinating example of some researchers at Mount Sinai Hospital in New York, who were able to use unsupervised deep learning to diagnose a range of patients, though no one fully knew how or why the diagnoses were accurate.  

But more to your question: one of our customers was using drone images to manage a large infrastructure project in a remote area, and had serious problems getting that data to data scientists and analysts involved in that project. So they used our platform to provide proper access to, and governance of, their data. And even though the consumers of the data were dispersed all over the world, in multiple regulatory jurisdictions, they were able to perform an infrastructure monitoring and upgrading effort that they would have had to complete in person only a few years ago (and at great cost).

Q: When it comes to specific laws and legal trends, what should data scientists be aware of?

A: I mentioned the EU’s GDPR, but what we’re really seeing is a wave of new efforts to regulate data and the way it’s used. And that last part is crucial—restrictions on how data can be used is the wave of the future from a regulatory standpoint. It used to be that regulations on data focused on security and access. But in a world where our data is increasingly available, and where we generate so much of it, regulations are going to assume that our data is accessible as a baseline, and move to focus on regulating how it’s used. And that’s exactly what the GDPR does, as well as China’s new “cybersecurity law,” among other examples. These new purpose-based restrictions can be hard to enforce with many of today’s data science tools.

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

A: The key problem confronting predictive analytics is really transparency. We’re in a world where data science operations are taking on increasingly important tasks, and the only thing holding them back is going to be how well the data scientists who train the models can explain what it is their models are doing. More on that during my talk!

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Don't miss Andrew’s conference presentation, Regulating Opacity: Solving for the Conflict Between Laws and Analytics on Tuesday, October 31, 2017 from 4:15 to 5:00 pm, at Predictive Analytics World New York.  Click here to register to attend

By: Eric Siegel, Founder, Predictive Analytics World

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June 9th 2017

Wise Practitioner – Predictive Analytics Interview Series: Jack Levis at UPS

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming keynote conference presentation, UPS’ Road to Optimization, at Predictive Analytics World for Business New York, Oct 29-Nov 2, 2017, we asked Jack Levis, Senior Director, Process Management at UPS, a few questions Jack Levisabout his work in predictive analytics.

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

A: In order to plan drivers’ day, we predict where deliveries are going to occur as well as how long it will take a driver to complete his / her route.  This opens the door to planning, execution, and analysis tools which we created.  However…  This is NOT the end game.  There is more than prediction. 

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

A: Based on what was described above, UPS reduced 85 million miles driven per year.

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

A: As mentioned, looking forward with predictive data and planning tools, we reduced 85 million miles driven per year while also offering new services to customers.   

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

A: Prediction is NOT an end game.  Optimization is… By adding optimizations (prescriptive analytics) to our predictive models, UPS reduced an ADDITIONAL 100 million miles driven per year.  This totaled to a 185 million mile reduction annually.  The prescriptive analytics alone is reducing cost of between $300M to $400M annually. 

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

A: I will discuss the different types of analytics and how UPS has used each.  I will point out how prescriptive analytics will find solutions that are not readily apparent and often counter intuitive.  I will also go through some best practices.

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Don't miss Jack’s keynote conference presentation, UPS’ Road to Optimization on Tuesday, October 31, 2017 at 1:10 to 1:55 pm at Predictive Analytics World New York. Click here to register to attend

By: Eric Siegel, Founder, Predictive Analytics World

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May 26th 2017

Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Richard Semmes at Siemens PLM

By: Bala Deshpande, Conference Co-Chair, Predictive Analytics World for Manufacturing 2016

In anticipation of his upcoming Predictive Analytics World Manufacturing Chicago, June 19-22, 2017 conferenceRichard Semmes presentation, Closing the Loop with Predictive Product Performance, we interviewed Richard Semmes, Senior Director, R&D at Siemens PLM. View the Q-and-A below for a glimpse of what’s in store at the PAW Manufacturing conference.

Q: What are the challenges in translating the lessons of predictive analytics from other verticals into manufacturing?

A: The objective for predictive analytics in manufacturing is really to enable actionable business decisions that impact the way you design, build, or service your products.  The most successful practitioners of predictive analytics in manufacturing use continuously updated data from many sources throughout their supply chain.  The biggest challenges center on data ETL, aggregation, and continuous updates.

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

A: Our models predict the performance of mechatronic products.  We use predictive analytics to connect real world IoT data to the Digital Twin models of the virtual world.  That allows manufacturers of physical goods to proactively manage their businesses by better understanding what is going to happen in their factories as well as their products in the field.

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

A: Predictive analytics serves to find issues with products we did not know existed.  We use predictive models to understand the correlation between product features and product performance.  We use that insight to proactively manage those products in the field as well as optimizing the product through design changes. 

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

A: In one instance, we trained models using environmental data as well as IoT data from a very complex machine that produces other products.  The trained model was able to show us the environmental and job characteristics that had the best correlation to job failure.  That information can be used to warn the operator that there is increased risk of failure and it can be used to improve the machines to better handle those adverse situations.

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

A: The extent to which environmental data should be taken into consideration when creating predictive models.  While it is obvious that weather and other environmental state can influence product performance, the extent to which including environmental conditions helps discover product feature correlations is significant.

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

A: You don’t need an army of data scientists to reap the benefits of predictive analytics in your business.​

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Don't miss Richard’s conference presentation, Closing the Loop with Predictive Product Performance, at PAW Manufacturing, on June 20, 2017 from 1:30 to 2:15 pm. Click here to register for attendance. 

By: Bala Deshpande, Founder, Simafore and Conference Co-Chair of Predictive Analytics World for Manufacturing.

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May 2nd 2017

Wise Practitioner – Predictive Analytics Interview Series: Edward Shihadeh at Auspice Analytics, LLC

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, How to Revolutionize Your Model Optimization, at Predictive Analytics World for Business Chicago, June 19-22, 2017, we asked Edward Shihadeh, Chief Data Officer at Auspice Analytics, LLC, 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: Although we predict a variety of different outcomes, we specialize in predicting retention, calculating the probability that individuals in a university, business, or program will stay or leave. Because the costs of acquiring a customer far exceed the costs of retaining one, this focus allows us to bring great value to our clients.

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

A: We employ our analytics not only in businesses and universities but also in the public policy arena. Working with a major city whose homicide rate was recently 2nd in the country, we used our methodology—which entails not just specialized modeling but also sophisticated appends and measurement of both individual and contextual data—to provide metropolitan police with lists of individuals at risk of committing murder, so that the police could intervene. The result: murder in this city has been driven down by 50% since 2012, translating to 100 lives saved from a tragic end and 100 potential offenders whose lives were turned around.

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

A: We work with a major university for whom the leading predictive analytics solution in the university retention market had failed—leaving the university at risk of losing millions of dollars. By applying our methodology and including contextual (e.g., supra-individual) and behavioral data and measures, we created a model that was more than 99% accurate in predicting the overall percentage of students who would return the next year and 96% accurate in predicting which specific students were at risk of leaving the university. This resulted in millions in savings for this university.

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

A: The example above, in which the leading predictive analytics solution in the university retention market failed to deliver acceptable results, documents two important findings. First, this industry can serve our clients far better by offering custom solutions than by applying off-the-shelf models. Second, it demonstrates the enormous increase in predictive power that we can gain from including contextual and behavioral data guided by behavioral and social science.

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

A: Whether we work in business, health care, financial services, our models predict human behavior; As our successes illustrate, big leaps in the power of predictive analytics come not just from model optimization but also from a sophisticated understanding of the role carefully-selected individual and contextual data, and carefully-crafted measures, will play in predicting outcomes. These gains come not from mindlessly appending data, or blindly applying the latest statistical technique, but from drawing on behavioral and social science to identify data and measures that truly increase predictive power. In other words, it's about the carpenter, not the hammer. 

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Don't miss Edward’s conference presentation, How to Revolutionize Your Model Optimization on Monday, June 21, 2017 from 3:30 to 4:15 pm at Predictive Analytics World Chicago. Click here to register to attend

By: Eric Siegel, Founder, Predictive Analytics World

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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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