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The Great Analytical Divide: Data Scientist vs. Value Architect
In the analytics space, it is quite common for...
Employee Churn 202: Good and Bad Churn
Our prior article on this venue began outlining the...
Why Overfitting is More Dangerous than Just Poor Accuracy, Part II
In part one, I described one problem with overfitting...
Predictive Analytics is the Answer to Smart Fulfillment and Omni-Channel Retailing
Over the past 5 years there have been several...
Employee Churn 201: Calculating Employee Value
Much has been written about customer churn – predicting...
Why Overfitting is More Dangerous than Just Poor Accuracy, Part I
Arguably, the most important safeguard in building predictive models...
5 Ways to Become Extinct as Big Data Evolves
The need to adopt sophisticated data analytics has become...
It is a Mistake to…. Ask the Wrong Question
(Part 4 (of 11) of the Top 10 Data...
What Role can Network Analysis play in Business Intelligence?
Network analysis is an emerging Business Intelligence technique that’s...
The Data Behind Data Scientists: Top Kaggle Performers
Kaggle, an online platform that hosts data analytics competitions,...
It’s Predictive Analytics, not Forecasting!
This is my final article for this year. It’s...
A Good Business Objective Beats a Good Algorithm
Predictive Modeling competitions, once the arena for a few...
Retail Predictive Analytics for Price Optimization & Markdown Management
There is no doubt that price is one of...
It is a Mistake to…. Rely on One Technique
(Part 3 of 11 of the Top 10 Data...
The Musings of a (Young) Data Scientist
I quit my job as a Mathematical Statistician after...
Big Data Continued…
Big Data is not a singular concept but rather...
How to Calculate the Optimal Safety Stock using Retail Predictive Analytics.
In a perfect world, a retailer knows exactly how...
The Role of Analysts After Model Deployment
Last month I made the case for discussing model...
How predictive analytics will power the internet of things
Recently, Nissan Motor announced that they will
Prediction Isn’t Just About Stocks. Predictive Persuasion
Prediction isn’t just for the stock market. Trading is...
The Greatest Power of Big Data: Predictive Analytics
Every day’s a struggle. I’ve faced some tough challenges...
It is a Mistake to… Focus on Training Results
(Part 2 of 11 of the Top 10 Data...
7 Ways Predictive Analytics Helps Retailers Manage Suppliers
One of the most challenging aspects of the retail...
Why Don’t We Talk about Deployment?
The Cross Industry Standard Process for Data Mining...
Understanding Predictive Analytics: A Spotlight Q&A with Eric Siegel, author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
This BeyeNETWORK spotlight features Ron Powell's interview with Eric...
Predictive Analytics: “Freakonomics” Meets Big Data
While writing my book, Predictive Analytics: The Power...
5 Reasons to Not Care About Predictive Analytics
Technology: complex and alienating, or promising and fascinating?...
Yet Another Big Data Article
It's like an irritating fly buzzing around your head...
Predictive Analytics Helps Solve Retail Allocation Challenges
We’ve already discussed why predictive analytics works so...
Why (some) Predictive Analytics will Move to the Cloud
A 2012 Gartner survey of over 1300...
Wise Practitioner – Predictive Analytics Interview Series: Philip O’Brien & Tom Kern of Paychex
In anticipation of their upcoming conference presentation at Predictive...
Wise Practitioner- Predictive Analytics Interview Series: Vikash Singh
In anticipation of his upcoming conference presentation at Predictive...
Deathwatch: Five Reasons Organizations Predict When You Will Die
Who benefits by predicting your behavior? Organizations do—companies, government...
Wise Practitioner – Predictive Analytics Series: Brett Cohen of AOL
In anticipation of his upcoming conference presentation at
The NSA, Link Analysis and Fraud Detection
The recent leaks about the NSA's use data mining...
Top 10 Analytic Mistakes–Today #0: Lacking Relevant Data
Mining data to extract useful and enduring patterns remains...
How Predictive Analytics Has Transformed Inventory Management in Retail
Every retailer wishes to have the right product at...
Big Data is Not Enough
Big data is the big buzz word in the...
Predictive Analytics & Retail
Predictive analytics is a hot topic. It has certainly...
What do we see in Predictive Models?
Predictive Analytics is one of the hottest careers on...
The Computer Knows Who You Are
There's a surprising twist. While some question whether the...
Interview With Predictive Analytics Author Eric Siegel
We live in an era of Big Data and...
Do Predictive Modelers Need to Know Math?
Predictive analytics is just a bunch of math, isn’t...
Five Reasons Siegel’s Predictive Analytics Book Matters to Experts
My new book—Predictive Analytics: The Power to...
The Future of Prediction: Predictive Analytics in 2020
Good morning. It's January 2, 2020, the first workday...
The Future Directions for Text Analytics
At the Boston Text Analytics World, held on October...
How the Obama Camp Analytically Persuaded Millions of Voters
Elections hang by a thinner thread than you think...
What We Should Take Home From Predictive Analytics Conferences
Why should one go to a predictive analytics conference?...
Three Ways to Get Your Predictive Models Deployed
We all know that given reasonable data, a good...
Why Predictive Modelers Should be Suspicious of Statistical Tests
Well, the danger is really not the statistical test...
Inside the Secret World of the Data Crunchers Who Helped Obama Win
In late spring, the backroom number crunchers who...

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The work of interpreting data to help decision-makers goes back some 5,000 years to...

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  • In the analytics space, it is quite common for many organizations to have a team of data miners who are now referred to as data scientists and a team of business users who are often referred to as value architects. It has been a common practice ever since the first direct marketing models were produced […]

  • Our prior article on this venue began outlining the business value for solving “the other churn” – employee attrition. We introduced the “quantitative scissors” with a simple model of employee costs, benefit, and breakeven points. The goal was to create a robust mental model for the cost of employee attrition. In this entry, we will […]

  • In part one, I described one problem with overfitting the data is that estimates of the target variable in regions without any training data can be unstable, whether those regions require the model to interpolate or extrapolate. Accuracy is a problem, but more precisely, the problems in interpolation and extrapolation are not revealed using any […]

  • 2 months ago
    Predictive Analytics is the Answer to Smart Fulfillment and Omni-Channel Retailing

    Over the past 5 years there have been several trends that have changed the way retailers operate their businesses. Many of them have to do with how consumers use technology to make a purchase. Pure e-commerce retailers have gained momentum causing brick-and-mortar retailers to re-think and re-design their online channels. Meanwhile, mobile technology has surged […]

  • Much has been written about customer churn – predicting who, when, and why customers will stop buying, and how (or whether) to intervene. Employee churn is similar – we want to predict who, when, and why employees will terminate. In many ways, it is smarter to to focus inward on employees. For one thing, it […]

  • Arguably, the most important safeguard in building predictive models is complexity regularization to avoid overfitting the data. When models are overfit, their accuracy is lower on new data that wasn’t seen during training, and therefore when these models are deployed, they will disappoint, sometimes even leading decision makers to believe that predictive modeling “doesn’t work”. […]

  • 3 months ago
    5 Ways to Become Extinct as Big Data Evolves

    The need to adopt sophisticated data analytics has become widely apparent to businesses recently, and the necessity of adopting “Big Data” analytics approaches is only becoming more evident. Gartner’s report on Big Data Adoption in 2013 found that 64 percent of organizations have already invested in or plan to invest in Big Data technology, and […]

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