Data Mining

You can get more value from the data you are storing in your Oracle database or data warehouse just by applying data mining to solve business problems. Oracle Data Mining, embedded in Oracle Database, can sift through massive amounts of data and find hidden information — valuable information that can help you better understand your customers and anticipate their behavior. Armed with this information, you can build a close relationship with and understand your customers, which helps you to:
  • Better retain customers and avoid churn
  • Profile customers and understand what products customer is likely to buy
  • Maintain and improve profit margins
  • Reduce customer acquisition costs
  • Target profitable customers with the right offer
Oracle Data Mining can also find patterns hidden in scientific, government, manufacturing, medical, and other types of data. Applications of data mining in these areas include:
Financial Services
Telecommunications Retail
Database Marketing campaigns
Insurance, Government Life Sciences
Churn Churn Loyalty More targeted mail Reduce the cost of investigating suspicious activity Discover gene and protein targets
Fraud Lifetime value Market-basket analysis Cross-sell Up-sell   Identify leads for new drugs
Cross-sell Cross-sell Cross-sell      
Loan default (credit risk)          
By being embedded in the Oracle Database, Oracle Data Mining facilitates extracting business intelligence from large volumes of data for production applications. It eliminates off-loading data to external special-purpose analytic servers for data mining and the subsequent scoring of even larger volumes of data. The data, data preparation, data mining, and scoring all exist within the database, which greatly simplifies the data mining process. In addition, overall application security is increased since data need never leave the secure database environment.
Key Features
  • Data Mining engine embedded in Oracle Database to eliminate data movement
  • Runs on multiple platforms
  • Built on Oracle Technology
  • Wide range of data mining algorithms for Classifications and Predictions:
    • Naïve Bayes (NB)
    • Adaptive Bayes Networks (ABN)
    • Support Vector Machines
    • Decision Trees (Oracle Data Mining)
    • Enhanced k-Means and Orthogonal Partitioning Clustering (O-Cluster), for identifying naturally occurring groupings within a data population
    • Association Rules performs “market basket analysis” to find commonly co-occurring items or event
    • Nonnegative Matrix Factorization (NMF) is useful for reducing a large dataset into representative attributes. Oracle Data Mining uses NMF and SVM algorithms to mine unstructured text data.
    • Bioinformatics analytics-Sequence matching and alignment (BLAST). Sequence alignment is one of the most commonly used bioinformatics tasks.
    • Attribute Importance algorithm finds the attributes that have the most influence on a target attribute.
  • PL/SQL and Java API interfaces for integrating data mining into enterprise applications
  • Scoring Engine installation option for deploying models to other databases
  • New Oracle Data Miner GUI with wizards to generate model code and simplify development of data mining applications
  • Reduce customer churn
  • Determining most profitable customers
  • Maintain and improve profit margins
  • IT costs due to specialized analytical software- separate upgrades, high license costs, etc
  • Improved fraud detection
  • Managing disparate systems
  • Must deal with data movement
  • Quickly identifying customer issues
Frequent Asked Questions
Oracle lacks the mindshare in Data Mining technology?
Oracle Data Mining was released in early 2004. At that time, access to ODM's data mining functionality was limited to PL/SQL or Java API and users had to write programs. We later added add-in JDeveloper wizards, to accelerate the process. Consquently, customer adoption and implmentation was slow to ramp up. In November 2004, Oracle has released a graphical user interface, Oracle Data Miner that customers can download from OTN. The beta release had been available for approximately 6 months.
All Oracle Data Mining's customers leverage the Oracle Database and the majority of them store and manage customer or "business" related data. Some store and manage scientific and technical data. Hundreds of customers have purchased the Oracle Data Mining option to the Oracle Database Enterprise Edition but it is difficult to determine which customers are using which features of the database. As of November 4, 2004, there were 1,200 messages posted on 440 topics on the Oracle Technology Network (OTN) Oracle Data Mining Discussion Forum. This cannot be interpreted as all active users as the discussion forum includes customers and prospective customers, however, it does provide some indication of the level of activity of Oracle Data Mining.
Customers who have been actively using ODM's predictive algorithms include:
Walter Reed Medical Center
Ecudorian Tax Ministry
NYC Comptroller's office
Oracle CRM Applications, Marketing Online
Oracle is too expensive.
Oracle's two main competitors in the Data Mining software space are SAS and SPSS, but Oracle Data Mining is a fraction of the cost compared to these two competitors. Both SAS and SPSS start at about $100K for 5 users while Oracle Data Mining is $20K per CPU.
We don't have data warehouse yet.
You don't have to have a data warehouse to mine the data. Based on your application, you can determine which variables you need to store for mining and then build the data warehouse in parallel.
We don't have any data mining expertise.
Oracle Data Mining is easy to use, especially when leveraging Oracle skills in house. Also, we have Oracle Consultants to help you. Oracle Data Mining simplifies and automate data mining so that end users don't have to be data mining experts.

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