Predictive Analytics-A future Insight of Data Analysis
Predictive analytics encompasses a variety of techniques from statistics and data mining that analyze current and historical data to make predictions about future events.Such predictions rarely take the form of absolute statements, and are more likely to be expressed as values that correspond to the odds of a particular event or behavior taking place in the future.
In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.
It is been used with the applications involving CRM,Cross-Selling,Direct marketing ,Collection analytics not only that even helps to detect Fraud detection in credit card Apps.
The statistical techniques used in Predictive Analytics are as follows
· Regression Techniques
· Linear Regression Model
· Discrete choice models
· Logistic regression
· Time series models
Apart from these Statistical Techniques there are some Machine learning techniques are used such as Neural Networks and k-nearest neighbours
The tool used to help with the execution of predictive analytics are SAS, S-Plus, SPSS and Stata and For machine learning/data mining type of applications, KnowledgeSEEKER, KnowledgeSTUDIO, Enterprise Miner, GeneXproTools, Clementine, KXEN Analytic Framework, InforSense
are some of the popularly used options.
WEKA is a freely available open-source collection of machine learning methods for pattern classification, regression, clustering, and some types of meta-learning, which can be used for predictive analytics.
Recently Business Objects has announced a partnership with SPSS , a worldwide provider of predictive analytics software, announced the companies have entered into an original equipment manufacturer agreement in which Business Objects will offer its customers the ability to use SPSS predictive analytics data mining technology as part of the market-leading Business Objects™ XI platform.
Users of Business Objects XI with predictive analytics data mining technology will be able to leverage business predictions to make more informed decisions that can help generate revenue, control expenses, and mitigate risk.
Today SAS, the leader in business intelligence, has significantly enhanced its award-wining SAS Enterprise Miner, SAS Text Miner, and SAS Forecast Server software, bringing predictive analytics to their highest level yet.
The newest release of SAS Enterprise Miner improves productivity through added interactive advanced visualization and new analytics. Fifteen new analytical tools improve the resulting predictive models, which can mean significant savings for customers with proactive marketing departments such as in retail or banking.
With innovative new modeling algorithms, including gradient boosting, partial least squares and support vector machines, SAS Enterprise Miner users can build more stable and more accurate models and thus make better decisions faster and with more confidence.
Posted on March 8th, 2008 by Guest
Filed under: Business Intelligence
Sifting through large volumes of data is like searching for a needle in a haystack. Until and unless you seem to vaguely know what to look for in that haystack, it sometimes becomes difficult to get good findings. I have seen this especially with Clementine. Also, with SPSS, its better to start out with a set of hypotheses that you need to look out for. These sets are built through domain knowledge and experience of handling large sets of data.
I have noticed in the construction industry, the need for such predictive analysis is becoming more and more pertinent.
Since more often than not, the industry works with an unorganized sector as vendors, being able to control & deliver by means of having a “heads up” is quite a challenge.
A large community exists which need some specific standards and interventions to empower their otherwise lateral growth.