Global News Network: Big Data Analytics

Just finished with a program Interview on GNN, entitled Big Data Analytics which covers Business Intelligence and Predictive Analytics, it will be aired on Channel 8, Destiny Cable/Sky Cable on Tuesday July 30 from 10 am to 11 am. Mr. Toti Casino is the host and current president of the Philippine Computer Society (PCS)

Predictive Analytics World Conference 2013

Cross-Industry, Cross-Vendor Sessions

The only conference of its kind, Predictive Analytics World delivers vendor-neutral sessions across verticals such as banking, financial services, e-commerce, entertainment, government, healthcare, high technology, insurance, non-profits, publishing, and retail.

And PAW covers the gamut of commercial applications of predictive analytics, includingresponse modeling, customer retention with churn modeling, product recommendations, online marketing optimization, behavior-based advertising, fraud detection, insurance pricing and credit scoring.

Why bring together such a wide range of endeavors? No matter how you use predictive analytics, the story is the same: Predictively scoring customers and other organizational elements optimizes business performance. Predictive analytics initiatives across industries leverage the same core predictive modeling technology, share similar project overhead and data requirements, and face common process challenges and analytical hurdles.

The Cross-Vendor Summit:

• Meet the vendors and learn about their solutions, software and services
• Discover the best predictive analytics vendors available to serve your needs
• Learn what they do and see how they compare.

Valuable Colleagues:

• Mingle, network and hang out with your best and brightest colleagues
• Exchange experiences over lunch, breaks and the conference reception, connecting with those professionals who face the same challenges as you.

Conference scope

Predictive Analytics World's sessions cover business applications of predictive analytics, including:

• Marketing and CRM (offline and online)
  • Response modeling
  • Customer retention with churn modeling
  • Acquisition of high-value customers
  • Direct marketing
  • Database marketing
  • Profiling and cloning
• Online marketing optimization
  • Behavior-based advertising
  • Email targeting
  • Website content optimization
• Product recommendation systems
• Insurance pricing
• Credit scoring
• Fraud detection

Predictive Analytics book by Eric Siegel

by Albert Anthony D. Gavino

For Predictive Analytics newbies, this would be a good book, it discusses analytics in a more simpler way and how companies are using analytics to their advantage.

the book is more on applications instead of the technical know how on predictive analytics.

Predictive Analytics book by Eric Siegel

In this rich, entertaining primer, former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction:

• What type of mortgage risk Chase Bank predicted before the recession.


• Predicting which people will drop out of school, cancel a subscription, or get divorced before they are even aware of it themselves.


• Why early retirement decreases life expectancy and vegetarians miss fewer flights.


• Five reasons why organizations predict death, including one health insurance company.


• How U.S. Bank, European wireless carrier Telenor, and Obama's 2012 campaign calculated the way to most strongly influence each individual.


• How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy!.


• How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job.


• How judges and parole boards rely on crime-predicting computers to decide who stays in prison and who goes free.


• What's predicted by the BBC, Citibank, ConEd, Facebook, Ford, Google, IBM, the IRS, Match.com, MTV, Netflix, Pandora, PayPal, Pfizer, and Wikipedia.

Buy the Book:

    
    

What are Lift Charts?

by Albert Anthony D. Gavino

Lift Charts help us evaluate data mining models.

You can tell from the chart that the ideal line peaks at around 40 percent, meaning that if you had a perfect model, you could reach 100 percent of your targeted customers by sending a mailing to only 40% of the total population. The actual lift for the filtered model when you target 40 percent of the population is between 60 and 70 percent, meaning you could reach 60-70 percent of your targeted customers by sending the mailing to 40 percent of the total customer population.

a Lift Chart Example from Microsoft Business Intelligence

HR analytics

Today offices have become smarter, even the HR department has become more evil complete with devil tails  collecting our data from time to time. Take this for example

Predictive Analytics on Employee data

• what days do employees take sick leaves?
• can we predict if an employee gets sick more often to predicting resigning?
• how about predicting on what months female employees get pregnant or give birth
• or what common illnesses are present during the month of June, are employees filing for the common cold sickness?

Now consider yourself an HR executive, and have all these rich big data at your fingertips, you can connect this data with Facebook Data and LinkedIn Data, employees who have undergone training, would they post it on facebook or post it on LinkedIN, are your employees joining professional organizations? how do you track you employees whereabouts? Are they drinking on friday nights? or are they watching movies with friends and families?

a Call Center Employee

IBM Products:

IBM SPSS Modeler and IBM predictive analytics have the power to analyze your employee data.

IBM SPSS Data Modeler

My Review on IBM SPSS Data Modeler

Usability and interface:

Overall the software is very easy to use with, you can data mine without knowledge of SQL syntax or scripts used on tables, its also easy to work with Excel and SPSS files, its also compatible with SAS files.

Tools 

Auto Classifier and Auto Cluster modes are very helpful for the lazy data miner who would like to compare three or more models accuracy based on your data marts or data sets

a lot of models to choose from CHAID, C5 decision trees to Logistic Regression, Cox Regression and Generalized Linear Models and Generalized Mixed models, there are also models accustomed to the financial sector such as the Recency, Frequency and Monetary node, otherwise known as RFM node.

it also features the Anomaly node for Fraud detection, very useful for credit card loans and risk default detection.

I still have to see what SAS and SAP has to offer in terms of its extensive models available with their predictive analytics software, some do say they are the number one in business intelligence but each software has its strengths and weaknesses depending on the user.

Data modeling Tips for the newbie

What Tips can we advise a newbie on data modeling?

Here are some simple advices

Create and plan your Data Warehouse, Data Structure and Architecture

1. Scope and Plan your data. Data architecture is relevant for you to plan way ahead of data troubles such as data integrity and compatibility issues. Plan what files you will be dealing with like flat files to cubes.
2. Have a background in Statistics. Knowing a little bit of your normal curves, testing for normal distributions will do help but you have to keep on reading for complex models such as Neural Networks and Bayesian Networks.
3. Know a little bit of scripting, learning SQL can help in extracting, transforming and loading your data, ETL would be a good way to go, a little bit of select statements such as "select from table where customerid = 200"
4. know your business objectives, do you want models that you can use to have leverage over your competitors, or do you want customer relationship management
5. Lastly, execute your model, your boss would be happy if you are able to deploy your complex model and show it to the board of trustees. 

Overall, data mining is not as simple as it seem but at least you can get pointers from these :)

Happy DAta Mining!