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Eric Siegel Discusses Predictive Analytics and Civil Liberties on KCRW’s Radio Show
Last week on “To the Point,” an NPR-syndicated radio...
Wise Practitioner – Predictive Analytics Interview Series: Dean Abbott, Abbott Analytics
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Wise Practitioner – Predictive Analytics Interview Series: Elpida Ormanidou of Walmart
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Future of Analytics: Big Data Integration, Transforming Organizations and Processes, Providing Speed and Foresight
With Analytics being a buzzword, most business executives have...
Haystacks and Needles: Anomaly Detection
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Auditing the Data When Deploying Predictive Analytics Solutions
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Leveraging Dark Data: Q&A with Melissa McCormack
Melissa McCormack,Research Manager at predictive analytics research firm Software...
Wise Practitioner – Predictive Analytics Interview Series: Nephi Walton, M.D., Washington University/University of Utah
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Using Predictive Modeling Algorithms for Non-Modeling Tasks
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Wise Practitioner – Predictive Analytics Interview Series: Greta Roberts of Talent Analytics
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Wise Practitioner – Predictive Analytics Interview Series: George Savage, M.D., Proteus Digital
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From Human Screen to Machine: Predictive Analytics Helps Avoid a Major Point of Hiring Failure
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Predictive Analytics in Health Care: Helping to Navigate Uncertainties and Change
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The Power of Predictive Analytics for Retail Replenishment
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Voice of the HR Profession: “Charts and Graphs are Hard to Follow”
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Wise Practitioner – Predictive Analytics Interview Series: John Cromwell, M.D., University of Iowa Hospitals & Clinics
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Creating the All-important Analytical File-The Key Step in Building Successful Predictive Analytics Solutions
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Wise Practitioner – Predictive Analytics Interview Series: Linda Miner, Ph.D., Southern Nazerene University
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It is a Mistake to…. Accept Leaks from the Future
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Wise Practitioner – Predictive Analytics Interview Series: Marty Kohn, M.D. of Jointly Health
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What’s the Government’s Role in Big Data Surveillance?
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Wise Practitioner – Predictive Analytics Interview Series: John Foreman of MailChimp
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Is Predictive Analytics Insidious? National Radio Interview
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5 Reasons Predictive Analytics World for Workforce is Different – And Better
If you follow the workforce analytics space, you may...
Should Employee Analytics “Go Fishing” or Solve Business Problems?
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Wise Practitioner – Predictive Analytics Interview Series: Sameer Chopra of Orbitz
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Wise Practitioner – Predictive Analytics Interview Series: Jack Levis of UPS
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Connecting the Experts with the Data Scientists
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Why analysts should master public speaking
Industry leader and consultant Geert Verstraeten serves as program...
Defining the Target Variable in Predictive Analytics- A Not so Easy Process
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Book Review of “Applied Predictive Analytics” by Dean Abbott
Industry leader and author Dean Abbott will be presenting...
Recognizing and Avoiding Overfitting, Part 1
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Webinar: Towards Solving Employee Attrition: Cost Modeling
Presented by: Pasha Roberts, Chief Scientist, Talent Analytics, Corp....
It is a Mistake to…. Listen Only to the Data
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The Data Audit Process (Part 1)-The Initial Step in Building Successful Predictive Analytics Solutions
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10 Practical Actions that Could Improve Your Model
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The Great Analytical Divide: Data Scientist vs. Value Architect
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Employee Churn 202: Good and Bad Churn
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Why Overfitting is More Dangerous than Just Poor Accuracy, Part II
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Predictive Analytics is the Answer to Smart Fulfillment and Omni-Channel Retailing
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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
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5 Ways to Become Extinct as Big Data Evolves
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It is a Mistake to…. Ask the Wrong Question
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What Role can Network Analysis play in Business Intelligence?
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The Data Behind Data Scientists: Top Kaggle Performers
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It’s Predictive Analytics, not Forecasting!
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A Good Business Objective Beats a Good Algorithm
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Retail Predictive Analytics for Price Optimization & Markdown Management
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It is a Mistake to…. Rely on One Technique
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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.
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The Role of Analysts After Model Deployment
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How predictive analytics will power the internet of things
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Prediction Isn’t Just About Stocks. Predictive Persuasion
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The Greatest Power of Big Data: Predictive Analytics
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It is a Mistake to… Focus on Training Results
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7 Ways Predictive Analytics Helps Retailers Manage Suppliers
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Why Don’t We Talk about Deployment?
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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
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Predictive Analytics: “Freakonomics” Meets Big Data
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Yet Another Big Data Article
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Predictive Analytics Helps Solve Retail Allocation Challenges
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Why (some) Predictive Analytics will Move to the Cloud
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Wise Practitioner – Predictive Analytics Interview Series: Philip O’Brien & Tom Kern of Paychex
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Wise Practitioner – Predictive Analytics Interview Series: Vikash Singh
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Deathwatch: Five Reasons Organizations Predict When You Will Die
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Wise Practitioner – Predictive Analytics Series: Brett Cohen of AOL
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The NSA, Link Analysis and Fraud Detection
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Top 10 Analytic Mistakes–Today #0: Lacking Relevant Data
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How Predictive Analytics Has Transformed Inventory Management in Retail
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Big Data is Not Enough
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Predictive Analytics & Retail
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The Computer Knows Who You Are
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Interview With Predictive Analytics Author Eric Siegel
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Do Predictive Modelers Need to Know Math?
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Five Reasons Siegel’s Predictive Analytics Book Matters to Experts
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The Future of Prediction: Predictive Analytics in 2020
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The Future Directions for Text Analytics
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How the Obama Camp Analytically Persuaded Millions of Voters
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What We Should Take Home From Predictive Analytics Conferences
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Three Ways to Get Your Predictive Models Deployed
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Why Predictive Modelers Should be Suspicious of Statistical Tests
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Inside the Secret World of the Data Crunchers Who Helped Obama Win
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By: Chad Brooks, BusinessNewsDaily Contributor
Originally published at businessnewsdaily 

Every business has a treasure trove of data, from customer and transaction information to manufacturing and shipping statistics. The key is figuring out how to use past data to better the business’ future.

One strategy is for companies to use predictive analytics. This involves combing through past information to derive models and analyses that help project future outcomes. The goal is to learn from past mistakes and successes in order to know what to change and what to replicate.

Predictive analytics can be applied to all aspects of an organization. It can help in figuring out what customers want and don’t want, and also be applied to a business’ operations to maximize efficiency. It can help a business fend off problems before they even become an issue down the road.

Eric Siegel, a former Columbia University professor and founder of Predictive Analytics World, defines the data analysis method as The Power to Predict Who Will Click, Buy, Lie or Die.

“Predictive analytics is the technology that learns from data to make predictions about what each individual will do — from thriving and donating to stealing and crashing your car,” Siegel said in an interview earlier this year. “For business, it decreases risk, lowers cost, improves customer service, and decreases unwanted postal mail and spam.”

In order to harness this data, businesses have a number of predictive analytics tools and software at their disposal.

Predictive analytics tools and software

In order to actually apply predictive analytics to a business or organization, specialized software is needed. Offered by a wide variety of vendors, including IBM, SAP and SAS, predictive analytics software is what crunches the collected data to determine the specific answers a business is looking for.

While each software offering has different capabilities and user interfaces, the premise is the same. The software works by first analyzing all the information a company collects. This includes everything from sales and customer information to employee productivity and social media data.

The software then plugs that data into predictive models. Using specially created algorithms, the models are able to project future trends and problems, based on that past behavior.

For businesses, the models can help predict various consumer trends to help drive supply and marketing decisions, as well as employee productivity trends to help improve efficiency.

While predictive analytics software used to only be an option for larger organizations, recent developments to the software have made it more accessible to small businesses. These software options, which are available from vendors — such as Emanio and Angoss — are sold at a more affordable price and can be run from any personal computer or laptop computer, instead of needing to be installed directly to a company’s server.

Examples of predictive analytics

Originally used by large retailers and financial institutions, predictive analytics is being used today by businesses in every industry and of all sizes, with an eye on getting a jump on the competition.

According to IBM, businesses can use predictive analytics in a number of different ways, including:

  • Uncover hidden patterns and associations
  • Enhance customer retention
  • Improve cross-selling opportunities through personalized offers and experiences
  • Maximize productivity and profitability by aligning people, processes and assets
  • Reduce risk to minimize exposure and loss
  • Extend the useful life of equipment
  • Decrease the number of equipment failures and maintenance costs
  • Focus maintenance activities on high-value problems
  • Increase customer satisfaction

While researching how companies were using predictive analytics to improve their organization, consulting firm Accenture uncovered several specific examples, including how Best Buy figured out that less than 10 percent of its customers were responsible for nearly 45 percent of its sales. That led to a redesign of their stores to better suit buying habits of their customers.

Accenture also found that the Italian restaurant chain Olive Garden used predictive analytic models to project food and staffing needs, which has led to a more efficient business.

The popularity of predictive analytics with businesses has led to other types of organizations using the software. For examples, healthcare firms are using predictive analytics to predict how certain drugs and therapies will be received by patients, and help doctors better detect early warning signs for life threatening diseases and illnesses.

Other organizations using predictive analytics are governmental bodies. They are using the software to help prevent crime, deliver social services and overall better serve the needs of its residents. For example, the city of Chicago used predictive analytics to help curb a lost garbage receptacle problem. The city found that the lost and stolen cans directly correlated to when streetlights were out.

Moving forward businesses and organizations not using predictive analytic software to help drive their decisions are going to find themselves in the vast minority.

By: Chad Brooks, BusinessNewsDaily Contributor
Originally published at businessnewsdaily

Follow Chad Brooks on Twitter @cbrooks76 or BusinessNewsDaily @BNDarticles. We’re also on Facebook & Google+.

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