Data Mining and Predictive Analytics

"Some companies have built their very business on their ability to collect, analyze, and act on data" 
– ‘Competing on Analytics’. Harvard Bus.Review Jan 2006.

Many businesses make some sort of use of their customer and market data. For some, it’s just a matter of storing and accessing customer data for record keeping and transactional purposes. Others like Google, Netflix, I.B.M. , or the Oakland Athletics make data analysis and analytics a major part of their business model. 

By analyzing past business records, data mining and analytics can help identify patterns that can support decisions that are more cost effective and efficient.  This is the specialty of what has contemporarily been dubbed the data scientist.

Do you really have a need for Data Mining and Predictive Analytics?

There’s only one way to find out how much potential value is buried in your data, and you have to start somewhere. With just a few data mining techniques you can begin to extract insight from your data that you might not otherwise achieve even after hours or years of pouring over lists and and row after row, column after column in excel.

What’s important is that you have someone that can identify the best tool for the task at hand, whether it’s a traditional experimental design involving analysis of variance, a forecast or time series analysis, a predictive model using logistic regression or decision trees, or one of the many other possible data mining tools available to a data scientist.

There are several aspects of data mining and predictive analytics that may be useful to you or your organization including Data Visualization, Predictive Modeling, Text Mining, Social Network Analysis, and Causal Inference. I discuss each of these below.

Data Visualization 

There’s more ways to gain insight from your data than just fancy models or algorithms. Data visualization allows you to transmit information to end users without the sometimes distracting  statistical terminology, complicated equations, or never ending excel sheets.

Created Using R- GoogleVis Package
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Revenues and Outlays 2003-2009

Predictive Modeling 

 My most successful analytics accomplishment to this point involves the development of a predictive models that we use to identify students that have a high risk of dropping out at WKU.  Working with my team, we’ve incorporated my model metrics into our data base/reporting/decision support system so that administrators have access to these high level analytical tools for strategic decision making.  We won an honorable mention from SAS at the recent SAS Global forum for our presentation. (see here for the paper with screenshots). We've since extended this model to predict the probability of enrollment and retention at the application stage as presented at the 2013 SAS Global Forum.

Text Mining

With Twitter, Facebook, email, online forums, open response surveys, customer and reader comments on web pages and news articles etc. there is a lot of information available to companies and organizations in the form of text. Without hiring experts to read through all of the thousands of pages worth of text available and making subjective claims about its meaning, text mining allows us to take otherwise unusable 'qualitative' data and convert it into quantitative measures that we can use for various types of reporting and modeling.

Examples: 

Mining Political Tweets

Mining Tweets related to the term 'Factory Farm'

The Mathematics Behind Text Mining

 

Tools like SAS Text Miner in conjunction with SAS Enterprise Miner are designed specifically to do this type of analysis on a much larger scale. I have used both of these tools in predictive modeling applications. R also has open source tools as well. 

Social Network Analysis

With the rise in the use of social media, data related to social networks is ripe for analysis using techniques from social network analysis and graph theory. According to International Network for Social Network Analysis, ‘Social network analysis is focused on uncovering the patterning of people's interaction’.

Social network analysis (SNA) allows us to answer questions such as who are key  actors in a network? Who are the most influential members of a network? Who seems to be acting on the peripheral? Which connections in the network are most important?  Are there key players bridging connections or information between otherwise disconnected groups? Have policies or other forces changed the overall dynamics/interaction between people in the network (i.e. has the network structure changed in any meaningful way) and does that relate to some other performance outcome or goal?

More specific applications of SNA may include Student Integration and Persistence, Business to Business Supply Chains, Seeding Strategies for Viral Marketing, and Predicting Customer Churn. The open source software R and NetDraw provide  many tools for conducting social network analysis.

Examples:

‘Using SNA in Predictive Modeling'.
‘Using Twitter to Demonstrate Basic Concepts from Social Network Analysis’
‘An Introduction to Social Network Analysis Using R and Netdraw.’  

Causal Inference

Sometimes we want to do more than just predict outcomes or identify key customer segments. Sometimes we want to know if a current practice or promotion is really having an impact on our business. In the case of an applied research setting, we want to know if a given 'treatment' has a statistically significant impact on an outcome of interest. We know that correlation does not always imply causation. In all of these cases we need statistical methodologies that will allow us to infer causation when appropriate, such as  quasi-experimental designs.

For a very technical look at these methodologies see: Causal Inference Roundup and Quasi-Experimental Design Roundup

For more information:

If you feel you can benefit from the services of a data scientist or have further questions about applied econometrics and analytics please contact me for more information or feel free to visit my blog or selected works where you can find a copy of my CV.

LinkedIn Profile:  (link)    

Selected Works Profile: http://works.bepress.com/matt_bogard/

What is a data scientist?

What is a data scientist?

“What sets data scientists apart from other data workers, including data analysts, is their ability to create logic behind the data that leads to business decisions. "Data scientists extract data, formulate models and apply quantitative analysis in a proactive manner" -Laura Kelley, Vice President, Modis.

"They can suck data out of a server log, a telecom billing file, or the alternator on a locomotive, and figure out what the heck is going on with it. They create new products and services for customers. They can also interface with carbon-based lifeforms — senior executives, product managers, CTOs, and CIOs. You need them." - Can You Live Without a Data Scientist, Harvard Business Review.

Data scientists not only have expertise in applied research and statistics, but they are comfortable doing the programming (a.k.a. hacking) necessary to shape data into a form suitable for analysis. In addition, data scientists have practical knowledge and expertise in the field or industry they work in.

Do you need a data scientist and can you afford to have on on staff? 

Perhaps you see a need for data driven decision making in your business and you want to tap someone with this talent, but you just don’t think you can justify someone on staff full time to do this. I would first challenge this notion. You may start with a few questions and answers, and some predictive modeling, but you will find there is always more data being generated, more questions, and a constant need to tweak and improve models and forecasts.  You will find that need someone dedicated to this task either in staff or via contract. Tapping data science talent may not be as difficult as you think. You might find that you can contract with someone with this skill set on an adhoc or as needed basis.

For more information:

If you feel you can benefit from the services of a data scientist or have further questions about applied econometrics and analytics please contact me for more information or feel free to visit my blog or selected works where you can find a copy of my CV.

LinkedIn Profile:  (link)    

Selected Works Profile: http://works.bepress.com/matt_bogard/

SAS Global Forum 2013 Paper 144-2013: SAS IML Worskshop

I didn't realize until now that the hands on workshops also had accompanying papers! ( I also just noticed this year  that the same was true for the posters as well).

This paper is a great intro to SAS IML. (see my other posts with statistical programming applications in social network analysis, text mining, and maximum likelihood estimation here )

Paper 144-2013
Getting Started with the SAS/IML® Language
Rick Wicklin, SAS Institute Inc.

ABSTRACT

Do you need a statistic that is not computed by any SAS® procedure? Reach for the SAS/IML® language! Many statistics are naturally expressed in terms of matrices and vectors. For these, you need a matrix language. This paper introduces the SAS/IML language to SAS programmers who are familiar with elementary linear algebra. The focus is on statements that create and manipulate matrices, read and write data sets, and control the program flow. The paper demonstrates how to write user-defined functions, interact with other SAS procedures, and recognize efficient programming techniques.

Link: http://support.sas.com/resources/papers/proceedings13/144-2013.pdf

Propensity Score Weighting: Logistic vs. CART vs. Boosting vs. Random Forests

 I've yet to do a post on IPTW regressions, although I have been doing some applied work using them. I have found similar results comparing nerual network, decision tree, logistic regression, and gradient boosting propensity score methods in applied examples. This paper provides more robust results using simulation.

Lee BK, Lessler J, Stuart EA (2011) Weight Trimming and Propensity Score Weighting. PLoS ONE 6(3): e18174. doi:10.1371/journal.pone.0018174

“Propensity score weighting is sensitive to model misspecification and outlying weights that can unduly influence results. The authors investigated whether trimming large weights downward can improve the performance of propensity score weighting and whether the benefits of trimming differ by propensity score estimation method. In a simulation study, the authors examined the performance of weight trimming following logistic regression, classification and regression trees (CART), boosted CART, and random forests to estimate propensity score weights. Results indicate that although misspecified logistic regression propensity score models yield increased bias and standard errors, weight trimming following logistic regression can improve the accuracy and precision of final parameter estimates. In contrast, weight trimming did not improve the performance of boosted CART and random forests. The performance of boosted CART and random forests without weight trimming was similar to the best performance obtainable by weight trimmed logistic regression estimated propensity scores. While trimming may be used to optimize propensity score weights estimated using logistic regression, the optimal level of trimming is difficult to determine. These results indicate that although trimming can improve inferences in some settings, in order to consistently improve the performance of propensity score weighting, analysts should focus on the procedures leading to the generation of weights (i.e., proper specification of the propensity score model) rather than relying on ad-hoc methods such as weight trimming.”

SNA & Learning Communities

This week I'm at the CPE Student success summit. Learning communities are an ongoing theme at the conference. This article is one of the few that I've found that uses social network analysis metrics to investigate student learning communities. Are centrality measures good indicators of integration? 

Phys. Rev. ST Physics Ed. Research 8, 010101 (2012) [9 pages]

Investigating student communities with network analysis of interactions in a physics learning center

ABSTRACT

"Developing a sense of community among students is one of the three pillars of an overall reform effort to increase participation in physics, and the sciences more broadly, at Florida International University. The emergence of a research and learning community, embedded within a course reform effort, has contributed to increased recruitment and retention of physics majors. We utilize social network analysis to quantify interactions in Florida International University's Physics Learning Center (PLC) that support the development of academic and social integration. The tools of social network analysis allow us to visualize and quantify student interactions and characterize the roles of students within a social network. After providing a brief introduction to social network analysis, we use sequential multiple regression modeling to evaluate factors that contribute to participation in the learning community. Results of the sequential multiple regression indicate that the PLC learning community is an equitable environment as we find that gender and ethnicity are not significant predictors of participation in the PLC. We find that providing students space for collaboration provides a vital element in the formation of a supportive learning community."

http://prst-per.aps.org/abstract/PRSTPER/v8/i1/e010101 

SAS Global Forum Paper 089-2013 (CART)

This was a nice paper illustrating and explaining CART (classification and regression trees).

089-2013  Using Classification and Regression Trees (CART) in SAS® Enterprise Miner™ for Applications in Public Health


"They (CARTs) are typically model free in their implementation. Howbeit, a model based statistic is sometimes used for a splitting criterion. The main idea of a classification tree is a statistician’s version of the popular twenty questions game. Several questions are asked with the aim of answering a particular research question at hand. However, they are advantageous because of their non -parametric and non- linear nature. They do not make any distribution assumptions and treat the data generation process as unknown and do not require a functional form for the predictors. They also do not assume additivity of the predictors which allows them to identify complex interactions. Tree methods are probably one of the most easily interpreted  statistical techniques. They can be followed with little or no understanding of Statistics and to a certain extent follow the decision process that humans use to make decisions. In this regard, they are conceptually simple yet present a powerful analysis (Hastie et al 2009)."

Interesting SAS Global Forum 2013 Papers

I recently noticed almost all of the papers for SASGF13 are posted. Instead of browsing the conference materials (which are larger than my local telephone directory- such a huge conference) I decided to start by browsing the papers (which can be found in the proceedings). I can then refer back to this post when I start trying to actually map out which sessions I'll go to. (and I can get back to the papers for the sessions I miss)

Most of the sessions and papers I typically like are in the area of Statistics and Data Analysis or Data Mining and Text Analytics. These sessions and papers offer direct applications in SAS that I can immediately take back to my job and implement.  There are often other papers throughout  Pharma, Operations Research, and Financial Services that also can be really helpful.

Below are the titles and links to papers that I've found so far. Yes this seems like a lot, but its only a small portion of the total proceedings. There are tons of other sections related to business intelligence, data management, and programming. I'm not sure I'll fit all of the sessions I've found into a 3.5 day conference schedule.

Business Intelligence Applications
044-2013 A Data-Driven Analytic Strategy for Increasing Yield and Retention at Western Kentucky University Using SAS Enterprise BI and SAS® Enterprise Miner™

Operations Research

152-2013 Using SAS® to Measure Airport Connectivity: An Analysis of Airport Centrality in the US Network with SAS/IML® Studio
 

Pharma:

163-2013 Quantile Regression in Pharmaceutical Marketing Research

Statistics and Data Analysis

425-2013  Price and Cross-Price Elasticity Estimation Using SAS®

430-2013  Chi-Square and t-Tests Using SAS®: Performance and Interpretation

433-2013  A Multilevel Model Primer Using SAS® PROC MIXED

436-2013 Missing No More: Using the MCMC Procedure to Model Missing Data

447-2013  Segmentation and Classification Analysis Using SAS®

448-2013 Estimating Censored Price Elasticities Using SAS/ETS®: Frequentist and Bayesian Approaches

454-2013  The Box-Jenkins Methodology for Time Series Models

456-2013  Exploring Time Series Data Properties in SAS®

458-2013 Nontemporal ARIMA Models in Statistical Research

501-2013  Finding the Gold in Your Data: An Overview of Data Mining (David Dickey)

504-2013  Estimating Harrell's Optimism on Predictive Indices Using Bootstrap Samples

Data Mining and Text Analytics

085-2013  Using Data Mining in Forecasting Problems

088-2013  Demand Forecasting Using a Growth Model and Negative Binomial Regression Framework

089-2013  Using Classification and Regression Trees (CART) in SAS® Enterprise Miner™ for Applications in Public Health

093-2013 Using the Boosting Technique to Improve the Predictive Power of a Credit Risk Model

096-2013  Incremental Response Modeling Using SAS® Enterprise Miner™

097-2013  Estimates of Personal Revenue from Credit and Sociodemographic Information Combining Decision Trees and Artificial Neural Networks (ANN)

099-2013  Replacing Manual Coding of Customer Survey Comments with Text Mining: A Story of Discovery with Textual Data in the Public Sector

Posters and Videos (papers included)

215-2013 A Case Application of Propensity Score Matching in MTM Outcomes Evaluation at Retail Pharmacy

220-2013 Propensity Score-Based Analysis of Short-Term Complications in Patients with Lumbar Discectomy in the ACS-NSQIP Database

246-2013 Using Text Analysis to Gain Insight into Organizational Change

251-2013  Analysis of Change in Sentiments towards Chick-fil-A after Dan Cathy’s Statement about Same-Sex Marriage Using SAS® Text Miner and SAS® Sentiment Analysis Studio

269-2013  Predicting Application Review Rating with SAS® Text Miner