July 16th 2014

Book Review of ‘Applied Predictive Analytics’ by Dean Abbott

Book Review of 'Applied Predictive Analytics' by Dean Abbott

By: Eric Siegel, Founder, Predictive Analytics World

Industry leader and author Dean Abbott will be presenting at Predictive Analytics World Boston (Oct 5 – 9) on “Data Preparation from the Trenches: 4 Approaches to Deriving Attributes.” Abbott will also run two post-conference full-day training workshops, “Advanced Methods Hands-on: Predictive Modeling Techniques” (where his book Applied Predictive Analytics will be given to attendees), and “Supercharging Prediction: Hands-On with Ensemble Models

Dean Abbott’s new book, “Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst,” published by Wiley in April, smoothly delivers the established teachings of a preeminent hands-on instructor.

This groundbreaking contribution to the field of predictive analytics provides a unique gift: A how-to that is accessible, yet quite comprehensive, taking the reader through much of the established teachings of one of the industry’s preeminent hands-on instructors. The author, Dean Abbott, is renowned as both a leading “rock star” hands-on consultant in predictive analytics, as well as a fantastic, 5-star-rated conference speaker and an acclaimed training workshop instructor. You get the best of all worlds with this particular expert: deep analytical insights, stellar execution, clear communication, and contagious enthusiasm. And he has translated these assets nicely into a book.

Abbott’s stated mission with this book (as mentioned in its “Introduction” at the end of the book) is to provide very practical guidance for executing on predictive analytics, as if chatting to someone peering over his shoulder as he works through a project. This mission is accomplished, and in doing so it accomplishes something even more significant: The book takes much of Abbott’s well-honed training agenda (do attend his in-person sessions if you can!), along with the accessibility of his casual speaking style, and translates them onto the page. As a result, this book reads in a much more conducive and engaging manner than, say, a more formally structured textbook.

Click here to access the complete article on Predictive Analytics Times.

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May 29th 2014

PAW Manufacturing Is 3 Weeks Away – Why It Matters

Predictive Analytics World for Manufacturing – June 17-18 in Chicago (www.pawcon.com/mfg), alongside the regular PAW Business event (www.pawcon.com/chicago) – focuses on real-world examples of deployed predictive analytics. Attend and hear how some of the world's largest and most forward-thinking manufacturers are tapping the power of predictive modeling to improve business outcomes. View the agenda-at-a-glance



Preparing the Manufacturing Industry for the Upcoming Data Deluge

By Bala Deshpande

The number of computing devices has far exceeded the number of humans on this planet – this milestone was in fact achieved back in 2008. Today there are an estimated 3 processors for every person in the world. Which devices in common use today use the most processors and computing power? The answer – the automobile – may surprise many people. The average car has anywhere between 50-60 microprocessors which handle everything from entertainment systems and climate control to performing advanced engine diagnostics and airbags during crash safety.  

With mobile becoming an integral part of our daily lives, the information processed by these devices becomes useful not only to the manufacturers, but also to consumers. Data from a large and growing number of embedded processors can be used in a variety of ways: at the driver level to select a more personalized, convenient and efficient mode of driving, at a macroscopic (e.g. traffic) level, information collected in this way then can be consolidated to find solutions to problems like traffic jams, helping to improve the flow of traffic should city officials want to leverage this data as well. From a manufacturing perspective, close attention to fine grain data can significantly improve vehicle quality, by potentially reducing warranty and recall issues. The U.S. government has dedicated significant research to this effort and Dr. Thomas Klier of the Federal Reserve of Chicago will be keynoting on this topic at the upcoming PAW-Manufacturing conference. 

This story is repeated across other non-consumer applications, particularly on the shop floor, from small contract manufacturers to the largest original equipment manufacturers of the industry. For example, one small company is using overhead GPS sensors to carefully account for the activities of their assembly line workers. How much time is spent by worker #1 on project #2 which requires inspection versus project #1 which requires assembly? The data generated by the sensors is captured and processed in near real time to predict machine usage, unexpected delays and downtimes and improve overall process efficiency. Once again, the chief output of this connected network is high quality data in huge volumes, which needs to be converted into useful data products. As Surendra Reddy, another keynote speaker from PAW-Mfg says "We are moving away from just creating and accessing data to more intimate, automated, knowledgeable, actionable insights about everything. This allows us to improve machine performance and the efficiency of the systems and networks that link them.  As the devices and environments around us become aware and intelligent, they will play a key role in making decisions for us or nudging us toward specific actions".

Historically manufacturing has been no stranger to either data or analytics. Statistical quality control, operations research and related technologies such as six sigma, all originated in manufacturing and could be considered the predecessors of today's predictive analytics. However much has changed since these technologies became standards. Our ability to collect far more data and in real time has increased significantly. W. David Stephenson, who is another keynote speaker at PAW-Mfg, writes that this upcoming data avalanche from the connected internet of things in a manufacturing plant can transform precision manufacturing by being able to "do things that were impossible in the past – such as identifying and removing defective products while they are still on the conveyor belt. As a result, we had to rely on approximations, such as mean time to failure and scheduled maintenance that inflated costs and often missed outlier conditions that might lead to premature failure".

The goal of PAW-Mfg is to help tie the advances in big data, analytics and visualization to persistent issues to drive orders of magnitude improvements in manufacturing performance and to prepare an "elder statesman" of industry verticals for a connected and data driven future.

Bala Deshpande is the co-chair of the inaugural Predictive Analytics World – Manufacturing conference. He is the founder and principal at SimaFore, a custom analytics developer and consulting company. He has nearly two decades of experience in data analytics and related fields. He began his career as an engineering consultant, following which he spent nearly a decade analyzing data from automobile crash tests and helping to build safer cars at Ford Motor Company. He holds a PhD in Bioengineering from Carnegie Mellon and an MBA from Ross School of Business (Michigan). 

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May 27th 2014

Vote for “Predictive Analytics” Today – Just One Click

There is only one more day to vote (SINGLE-CLICK) for my book.

My book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, was nominated for a Small Business Trends Book Award!

Show your support in just one click! VOTE HERE (before the end of May 28)

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March 5th 2014

Breakthrough: How to Avert Analytics’ Most Treacherous Pitfall

This article will make you feel better. And you do need to feel better, if you are one of the many of us who practice analytics—or who must consume and rely on analytics—and find ourselves carrying tension in our shoulders or sometimes losing sleep.


The fear stems from a well-known warning of tragic mishap: "If you torture the data long enough, it will confess," as stated by University of Chicago economics professor Ronald Coase. There is a general sense that math could be wrong and that analytics is an art.


As John Elder of Elder Research put it, "It's always possible to get lucky (or unlucky). When you mine data and find something, is it real, or chance?" How can we confidently trust what a computer claims to have learned? How do we avert the dire declension, "Lies, damned lies, and statistics"?


There is a simple, elegant solution from Elder—but first, let me further magnify your fear: Even the very simplest predictive model risks utter failure. Mistaken, misleading conclusions are in fact terribly easy to come by.


A conclusion drawn about one single variable—even without the use of a common multivariate model (such as log-linear regression)—can go awry. In fact, one of the more famous such analytical insights, "an orange used car is least likely to be a lemon," has recently been debunked by Elder and his colleague Ben Bullard at Elder Research, Inc.


Big data, with all its pomp and circumstance, can actually mean big risk. More data can present more opportunities to inadvertently discover untrue patterns that appear misleadingly strong within your dataset—but, in fact, do not hold true in general. To be more specific, "bigger" data could mean longer data (a longer list of examples, which generally helps avert spurious conclusions), but also could mean wider data (more columns—more variables/factors per example). So, even if you are only considering one variable at a time, such as the color of each car, you are more likely to come across one that just happens to look predictive in your data by sheer chance alone. This peril that arises when searching across many variables has been dubbed by John Elder vast search.


Dr. Elder puts it this way: "Modern predictive analytic algorithms are hypothesis-generating machines, capable of testing millions of 'ideas.' The best result stumbled upon in its vast search has a much greater chance of being spurious…  The problem is so widespread that it is the chief reason for a crisis in experimental science, where most journal results have been discovered to resist replication; that is, to be wrong!"


A few years ago, Berkeley Professor David Leinweber made waves with his discovery that the annual closing price of the S&P 500 stock market index could have been predicted from 1983 to 1993 by the rate of butter production in Bangladesh. Bangladesh's butter production mathematically explains 75 percent of the index's variation over that time. Urgent calls were placed to the Credibility Police, since it certainly cannot be believed that Bangladesh's butter is closely tied to the U.S. stock market. If its butter production boomed or went bust in any given year, how could it be reasonable to assume that U.S. stocks would follow suit? This stirred up the greatest fears of PA skeptics, and vindicated nonbelievers. Eyebrows were raised so vigorously, they catapulted Professor Leinweber onto national television.


Crackpot or legitimate educator? …


Read the full article by Eric Siegel on the Predictive Analytics Times


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February 2nd 2014

Predictive Analytics’ New Wave


I was honored to have my book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die, covered in this article in GARP.org.  Here is an excerpt from the article.

Predictive Analytics' New Wave

By Katherine Heires, Risk News (published by the Global Association of Risk Professionals)

Fascination with the future is part of human nature. In a commercial or financial context, accurate predictions have major business implications; in finance, many quantitative processes and innovations are designed to deliver such insights for competitive advantage.

One doesn't need a crystal ball — or sophisticated software, for that matter — to measure the demand for the latest in predictive-analytics tools or to understand how and why it is accelerating. Predictive analytics can be seen everywhere from the micro level, as in credit scoring on loan applications, a technique banks have been employing since the 1940s; to the macro analyses that regulatory bodies are developing to assess systemic risks. Financial and nonfinancial enterprises alike aspire to mine Big Data for patterns and insights that can foretell, and create opportunities out of, market trends or customer behaviors, while also taking emerging risks into account.


An ever-widening array of entities — in advertising, health care, insurance, energy, and even government agencies like the Securities and Exchange Commission — have embraced predictive analytics in some form…

A former Columbia University professor of computer science, Eric Siegel is a consultant and author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die, one of a number of titles indicating a growing level of popular and business interest in the field. Founder of the Predictive Analytics World conferences, he notes that in 2009 he hosted two conferences, and 10 are on the calendar for 2014. "The two new predictive analytics verticals we will focus on this year will be manufacturing and health care," he says.

Click here for the complete article (PDF, reproduced with permission).

Click here for the article on GARP.org (paid access only).

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January 28th 2014

Personalization Is Back: How to Drive Influence by Crunching Numbers

Personalization Is Back: How to Drive Influence by Crunching Numbers

By: Eric Siegel, Ph.D., Founder, Predictive Analytics World
Originally published at information-management.com

Standard predictive analytics does not directly address what is the greatest challenge faced by marketing and healthcare: Across large numbers of individuals, deciding who to treat in a certain way.

Yes, you heard me correctly. Predictive analytics still needs a certain tweak before it’s designed to optimize organizational activities.

Let’s take a step back. The world is run by organizations, which serve us as individuals by deciding, for each one, the best action to take, i.e., the proper outgoing treatment:

TREATMENTS: Marketing outreach, sales outreach, personalized pricing, political campaign outreach, medication, surgery, etc.

That is, organizations strive to analytically decide whom to investigate, incarcerate, set up on a date, or medicate.

Organizations will be more successful, saving more lives or making more profit—and the world will be a better place—if treatment decisions are driven to maximize the probability of positive outcomes, such as consumer actions or healthcare patient results:

OUTCOMES: Purchase, stay (retained), donate, vote, live/thrive, etc.

In fact, the title of my book itself includes a list of such outcomes: Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.

But this book title—and predictive analytics as a field in general—may lead you astray by implying the best way to improve the probability of these actions (or, alternatively, the probability of averting them, in the case of the latter two, lie and die) is to predict them. However, predicting an outcome does not directly help an organization drive that outcome. Instead, treatment decisions are optimized when organizations predict something completely different from outcome or behavior:

WHAT TO PREDICT: Whether a certain treatment will result in the outcome.

Click here to read the full article in Predictive Analytics Times.


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January 13th 2014

Journal of Marketing Analytics review of “Predictive Analytics”

I was honored to have my book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die reviewed by Richard Boire (of the Boire Filler Group) in the Journal of Marketing Analytics. Here is an excerpt from the review.

This book is really the first book on data mining or predictive analytics that attempts to communicate the impact of predictive analytics to our society at large. Historically, the rationale for not reaching out to the general audience was that data mining and predictive analytics were specialized areas of expertise that would only be of interest to its practitioners and academics. There was no real sense of its tremendous significance within our everyday lives and more importantly, the benefits that were conferred by this discipline. The knowledge/information revolution has changed the paradigm and how we view this new discipline. This book does an excellent job in reinforcing the growing impact of this discipline as the author, Eric Siegel, in what is often referred to as a very dry topic, transforms it into a discipline with wide appeal and interest among all sectors of society.

Examples abound throughout the book in all sectors as the author explores the impact of predictive analytics on everyday facets all of us face during the course of our normal day…

This book is a must read for the normal lay person presuming there is interest in how society can best use information in our evergrowing Big Data world. At the same time, the seasoned practitioner will appreciate the real-world examples.

Click here to read the full review at Journal of Marketing Analytics (paid access only).

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December 9th 2013

The Winning Formula to Being a Kaggle Data Scientist


The Winning Formula to Being a Kaggle Data Scientist

Is there a formula to be a data science "guru"? If so, what does it include? Is the most significant factor education, experience or pure talent?

Software Advice, which researches and compares business intelligence software, tackled this question with a study to examine the top analysts within the world’s largest data scientist community, Kaggle.

Kaggle is the largest and leading host of predictive analytics competitions, offering companies the chance to tap into its community of more than 100,000 analysts in order to undertake various big data challenges. I wrote about Kaggle in Chapter 5 of my book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. The study analyzed the top 100 Kaggle users (as of October 2013) to learn more about what these data superstars have in common.

Interesting study results:

Education: Over 80 percent of the top 100 performers have a Master’s degree or higher, and 35 percent have a Ph.D. The top 21 performers all have an M.S. or higher: 9 have Ph.D.s and several have multiple degrees (one member even has two Ph.D.s).

Background/Disciplines: Analysts come from a broad variety of educational backgrounds, with computer science and mathematics as the top areas of study. While most of the areas of study centrally involve quantitative skills, a few surprising programs surfaced, such as philosophy and law.

Where in the World: These “data wizards” hail from all over the globe, with 29 countries represented in the top 100 performers group. The United States has the most members in this list (30), followed by Russia (nine) and India (six).

Sticktoitiveness: The number of contests entered also correlates with a higher chance of winning competitions and becoming a member of the top Kaggle prize-winners.

The Prize Winning Group

In the end, the study concludes that the skills necessary to be one of these elite Kaggle performers can be developed by growth in any one of multiple disciplines, with various levels of study. The name of the game is persistence and a high level of activity in the community.

Read more about this study here.

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December 2nd 2013

Announcing the inaugural PAW Healthcare

Announcing the inaugural PAW Healthcare

Attend Predictive Analytics World for Healthcare, coming to Boston, October 6-7, 2014, and witness today's rapidly emerging movement to fortify healthcare with big data's biggest win: the power to predict. The premier cross-vendor networking event, this conference assembles the industry's leaders to deliver case studies and expertise, revealing how predictive analytics:

  • Improves patient care
  • Reduces costs
  • Brings greater efficiencies to the healthcare industry

Predictive analytics addresses today's pressing challenges in healthcare effectiveness and economics by improving operations across the spectrum of healthcare functions:

Personalized medicine. Per-patient prediction and analytically enhanced diagnosis drives individual clinical treatment decisions

Insurance. Predictively guided decisioning combats risk and renders insurance more equitable and profitable

Hospital administration. Analytics detects and recoups loss due to fraud and waste

Healthcare marketing. From medical suppliers to healthcare screening service providers, the performance of industry enterprises hinges on analytically targeted marketing

Drug development. Analytics advances pharmaceutical engineering, testing, and other processes

Much more. Other applications include predicting per-patient disease progression, mortality risk, availability of clinical trial participants, consumer prescription adherence, and more.

Who should come? PAW Healthcare provides unique learning and networking opportunities for physicians, medical researchers, administrators, marketers, and analytics professionals from:

  • Major medical centers
  • Information system companies
  • Pharmaceutical organizations
  • Medical device manufacturers
  • Medical insurance providers
  • Dental insurance providers
  • Clinical laboratories

Click here for more information

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November 18th 2013

The power to predict who will click, buy, lie or die.

Product Margins

I was honored to have my book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die reviewed by  Shakthi Poornima in Product Margins. Here is an excerpt from the review.

The power to predict who will click, buy, lie or die

Working in the field of Big Data means taking into consideration not hundreds or thousands, but millions, billions, or even bigger datapoints.  And underneath all that data, lies unparalleled potential. Just imagine being able to predict one’s location up to multiple years beforehand by using GPS data (Microsoft), or being able to predict one’s risk of death in surgery (Riskprediction.org.uk). That’s what the book, “Predictive Analytics: The power to predict who will click, buy, lie or die” is about. It covers building applications in marketing, health care, fraud, finance, human resources., etc by a variety of parties — companies, banks, governments, even universities. Everyone has an interest in data.

…overall, the examples in the book are well-researched. What was interesting to me was the possibility of taking the predictions from various studies to building new products.s For example, Orbitz found that Mac users book more expensive hotels. “Orbitz applies this insight, altering displayed options according to your operating system” (p.81). A different study found that one’s inclination to buy online varies by the time of day:  8pm for retmail, late night for dating, 1pm for finance, and so on. Combining the insights from both studies can come in handy for marketing a new product, or starting an A/B test for that product. The potential for meshing various different types of data grows as different applications are developed around same or similar datasets, and as these datasets grow in size.

Click here to read the full review at ProductMargins.com.

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