Data Cleaning Information
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Information On Data Cleaning Experiment
This material appeared originally in an article written at Columbia University. No author or date was listed.
Merge/Purge is very interesting because every large repository of data has numerous duplicate information about entities that are difficult to cull together without an intelligent "equational theory" that matches complex records. Columbia University is developing an in-house data cleaner with what is referred to as a "rule programming" approach that is easy to program (relatively), while giving goog results in locating duplicate data, particularly in databases with extensive amounts of data. This is an especially important procedure for direct marketing companies and for fraud detection programs. It is also important for a variety of data mining applications. To find out more about this topic and how NW Database Services can help with your data cleaning project, go to our data cleanup page.
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Many very large organizations have learned that database data is always exceptionally "dirty." As a result, data cleaning is an important step in database management. Data cleaning is now a significant business in the database world. Columbia U. studied the merge and purge, aka "Dedupe and Consolidatioan Analysis situation for the Citicorp's Technology Office. Also, the data cleaning program was tested with data in the Washington state Child Welfare Department, also known as OCAR. data.
Testing showed excellent results with the C.U. data cleaning program. It yielded better (meaning more accurate) results in less time on inexpensive computer systems than results on large mainframe systems. The preliminary analysis by the Child Welfare Department demonstrates accuracy was much better using what is referred to as a single scan clustering for data cleaning. Getting extremely accurate quantitative measurements are not easy since problems from real business situations are more difficult to prioritize. Do this constraint, Columbia used data that was statistically generated to get a better and more objective measure for the data cleaning performance.
OCAR researcher technitions state the results were excellent. OCAR results report initial tests for database record matching accuracy were better than expected. The first test using sample data resulted in 97% accuracy; this is a considerable improvement over previous agency results. The previous best results were in the 90% range for accuracy. FYI: Accuracy is defined as a percentage of merged records that are correct from teh total number merged records in the database. This highly accurate outcome was determined by staff researchers reviewing all the data from the database following the data cleaning procedure. To find out more about this topic and how NW Database Services can help with your data cleaning project, go to our data scrubbing page.
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The Columbia U. data cleaner is now used for daily operations by the OCAR. Personnel are pleased with the system particularly the "rule based equational theory." This allows for easy modifications and recompiling when new information about the database is determined. This flexibility allows for rapid alterations in the program to allow for data changes. To find out how we can help with your data cleaning project, go to our data scrubbing page.