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Be a Data Detective
 The Investigative Mentality You’ve probably heard it before –...
Wise Practitioner – Predictive Analytics Interview Series: Dr. Patrick Surry of Hopper
 In anticipation of his upcoming keynote conference presentation, Buy...
Predicting Employee Flight Risk: My Take
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The Key to Modelling Success -The Variable Selection Process (Part 1)
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Predictive Analytics World in Color [Infographic]
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Space Alien Eager to Convey Thoughts on Data Science
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Defining Measures of Success for Predictive Models
  Excerpted from Chapters 2 and 9 of his...
Overstatement of Results in Predictive Analytics
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The Biggest Lever to Success in Predictive Analytics
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Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Field Cady at Think Big Analytics
  In anticipation of his upcoming Predictive Analytics World for...
Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Jeffrey Thompson of Robert Bosch, LLC
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Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Kumar Satyam of PricewaterhouseCoopers, LLP
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Uplift Modeling: Predictive Analytics Can’t Optimize Marketing Decisions Without It
Thank you for your interest in the white paper,...
Predicting Rare Events In Insurance
  As we all know, predictive analytics is a...
Python, Predictive Analytics & Big Data oh my!
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Wise Practitioner – Predictive Analytics Interview Series: Thomas Schleicher of National Consumer Panel
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Leveraging Open Data: Improve Customer Experience and Drive New Market Opportunities
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From Code to Reports with knitr & Markdown
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Predictive Analytics Optimizes Prices and Markdowns for Retail
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Wise Practitioner – Predictive Analytics Interview Series: Delena D. Spann of US Government
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Infographic – PAW SF
5-Minute Recap
  In San Francisco this past March and April,...
Wise Practitioner – Predictive Analytics Interview Series: Dr. Patrick Surry of Hopper
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Wise Practitioner – Predictive Analytics Interview Series: Viswanath Srikanth of Cisco
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Wise Practitioner – Predictive Analytics Interview Series: Jack Levis of UPS
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Trust in Analytics Work: Why it’s Needed and How to Build It
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Guiding Principles to Build a Demand Forecast
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Wise Practitioner – Predictive Analytics Interview Series: Arcangelo Di Balsamo of IBM
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Wise Practitioner – Predictive Analytics Interview Series: Dean Abbott of Smarter Remarketer
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Predictive Analytics in Sports
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5 Things I Learned at Predictive Analytics World for Workforce
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Predictive Analytics as a Strategic HR Solution
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Visualizations Get Some Snap from R Shiny
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What Programming do Predictive Modelers Need to Know?
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Wise Practitioner – Workforce Predictive Analytics Interview Series: John Callery at AOL
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Analytic Professionals — Share your views: Participate in the Rexer Analytics 2015 Data Miner Survey
  Data Analysts, Predictive Modelers, Data Scientists, Data Miners,...
Charlie Batch and the Cost of Obfuscation
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Predictive Analytics for Insurance Risk: A New Level of Data Scrutiny-Part 2-Development and Implementation
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Is Big Data Better?
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Wise Practitioner – Workforce Predictive Analytics Interview Series: Chad Harness at Fifth Third Bank
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Building the Optimal Retail Assortment Plan with Predictive Analytics
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Using Advanced Clustering Techniques to Better Predict Purchasing Behaviors in Targeted Marketing Campaigns
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Wise Practitioner – Workforce Predictive Analytics Interview Series: Patrick Coolen of ABN-AMRO Bank
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Using Predictive Analytics to Predict and Manage Business Travel Burnout
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Wise Practitioner – Workforce Predictive Analytics Interview Series: Scott Mondore at Strategic Management Decisions, LLC
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Wise Practitioner – Predictive Analytics Interview Series: Bob Bress of Visible World
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Wise Practitioner – Workforce Predictive Analytics Interview Series: Holger Mueller of Constellation Research, Inc.
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The Imminent Future of Predictive Modeling
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Using Statistics and Visualization as Complementary Validation
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Predictive Analytics for Insurance: A New Level of Data Scrutiny
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The Three-Legged Stool of an Analytics Project
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Defining Measures of Success for Predictive Models
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Effective Framing of Predictive Analytic Projects
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Wise Practitioner – Predictive Analytics Interview Series: Richard Boire of Boire Filler Group
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Wise Practitioner – Predictive Analytics Interview Series: Sarah Holder of Duke Energy
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Infographic – Predictive Analytics World by the Numbers
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Wise Practitioner – Predictive Analytics Interview Series: Mohamad Khatib of Nielsen
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A Critical Step Toward Organizational Data Maturity: Thinking in Terms of Distributions!
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It is a Mistake to…. Answer Every Inquiry
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Wise Practitioner – Predictive Analytics Interview Series: Dominic Fortin of TD Insurance
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5 Ways Retail Predictive Analytics helps Fashion Retailers Maximize Gross Margin
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Wise Practitioner – Workforce Predictive Analytics Interview Series: Pasha Roberts at Talent Analytics
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Predictive Modeling Skills: Expect to be Surprised
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Big Data: To Analyze or Not to Analyze
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Wise Practitioner – Predictive Analytics Interview Series: Bryan Guenther of RightShip
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Wise Practitioner – Predictive Analytics Interview Series: Aaron Lanzen of Cisco
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Wise Practitioner – Predictive Analytics Interview Series: David Schey of Digitas
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It is a Mistake to…. Extrapolate
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Wise Practitioner – Workforce Predictive Analytics Interview Series: Carl Schleyer of 3D Results
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The Trouble with Numbers
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Wise Practitioner – Predictive Analytics Interview Series: Josh Hemann of Activision
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Practical Predictive Modeling: Quick Variable Selection
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It is a Mistake to…. Discount Pesky Cases
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Wise Practitioner – Workforce Predictive Analytics Interview Series: Scott Gillespie, Managing Partner of tClara
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Want to Improve Your Prototype-to-Production Analytics Process? Embrace Thinking Inside the Box
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Using Decision Trees in Variable Creation: Minimizing Information Loss-Part 1
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Predictive Analytics turns Multi Channel Retailing into Omni Channel Retailing.
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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...
Wise Practitioner – Predictive Analytics Interview Series: Dean Abbott, Smarter Remarketer
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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...
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...
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
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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...
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...
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,...
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
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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.
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 at 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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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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