DataFlux

Pattern analysis is a technique typically used to determine if the data values in a field are in the expected format.  For example, some fields like a phone number or a product identifier have an expected pattern.  Pattern analysis quickly validates that the data in a field is consistent across the data source - and meets your expectations.

DataFlux data profiling solutions let you analyze underlying data, then start building correction and validation rules. For any data, DataFlux will help you turn your analysis into an actionable business process.

Consider a pattern report for North American phone numbers. There are many valid phone number formats, but all valid formats consist of three sets of numbers (three numbers for area code, three numbers for exchange, four numbers for station). These sets of numbers may or may not be separated by a space or special character. Valid patterns might include:

• 9999999999
• (999) 999-9999
• 999-999-9999
• 999-999-AAAA
• 999-999-Aaaa

In these examples, "9" represents any digit, "A" represents any upper case alpha (letter) character and "a" represents any lower case alpha character. Now, consider the above pattern report on a phone number field.

The majority of the phone data in this field contains valid phone numbers for North America. There are, however, some data entries that do not match a valid phone pattern. DataFlux data profiling solutions let you drill through a report like this to view the underlying data. You can then right-click on the data in question to start building correction and validation tools in the data quality portion of DataFlux solutions - helping you turn your analysis into an actionable business process. DataFlux also makes it easy to cover all the formats to a standard format enabling future analysis and reporting.