Today many organizations have millions of customer, vendor, and employee records, often with different data standards and non exact relationships. Determing who’s who can be an impossible task without the proper tools. Fuzzy matching algorithms, automated standardization across data sets, and the ability to manually identify relationships are of key importance. Our products are in place across the Fortune 500 and within government organizations to quickly, affordably, and efficiently enable identity matching. Try a free trial today or continue reading for more best practices on Identity Resolution.
Looking across and within data sources for entity matching
Merging different databases with different sources (SQL server, MySQL, Excel, ODBC etc.) and combining into a common structure is the first step in the process. Usually duplicates are between databases, but sometimes duplicates are within a single file. DataMatch can import, combine, and export to the most common database formats. Additionally DataMatch will automap similar fields from different data sources together (Which can be customized and overwritten)
A key component of eliminating duplicates is the definition of what uniquely identifies an entitys. The following best practices are key and are all included in DataMatch.
> Fuzzy logic identification of percent matches between records and setting minimum percent match thresholds by field
> Acronym identification for matching (Match International Business Machines to IBM)
> Cleaning and standardizing data prior to matching (Street to street, eliminating unnecessary syntax in phone numbers, etc.)
> Applying libraries for standardization, especially for first names (Jon, Jonathan, and John etc.) DataMatch includes over 300,000 standardization rules for names, addresses, phone numbers and company names.
Survivorship and Combining Information between Entities:
One of the critical pieces of enity resolution is survivorship. If you have duplicate records, which one should stay (survive) and which one should go?
DataMatch allows customized settings for which merged data should survive
In this example there are two duplicate records. Each has some slightly different data in the notes field. You may prefer to keep all records, but often times a single master record must be chosen to maintain data quality.
With DataMatch you can choose which record survives by choosing what field to merge on, in this case Customer Number, and ascending or descending order. If ascending the first customer number would hold priority ‘1005643’, if descending the later customer number ‘1106789’ would have priority. Note you can always change which record is a master manually in DataMatch.
Unfortunately normal duplicate removal software routines can delete vital business data.
What if you want to keep both pieces of information in the same entity record?
The best solution would be to keep all data that is different in a new field. DataMatch has this capability.
The result would be this
Note the alternate information is captured in a new field. The benefit is a single identity record, with no vital data loss. (Old customer number kept for referencing, and critical customer comments, like interest in a new product, kept)
Try the free trial on your own data set! Note please contact us to setup a non obligatory walkthrough, on your own data if you prefer.
Note DataMatch never deletes any information from the source files , all information is kept temporarily in memory where you can test different duplicate removal settings without consequence. Although you can overwrite your original source files if you choose.