Address: Data Quality Dots on a map improve data quality Author: Peter Benza © 2005

Input Files: Duplicate input files or records must be merged for accurate plotting. If not “dot stacking” and inaccurate aggregate summary datasets will result.

Duplicate input files or records must be merged for accurate plotting.

If not “dot stacking” and inaccurate aggregate summary datasets will result.

Duplicates Duplicates are present in virtually every database. Geo-spatial tools help you recognize and eliminate duplicates to assure data quality.

Duplicates are present in virtually every database.

Geo-spatial tools help you recognize and eliminate duplicates to assure data quality.

Data Quality: Example Customer names and addresses in small geographical require special data processing. Lets visualize (geocode) the name Jose Gonzalez across our enterprise from disparate source files.

Customer names and addresses in small geographical require special data processing.

Lets visualize (geocode) the name Jose Gonzalez across our enterprise from disparate source files.

We isolated 4 variations in our customer master. 4 customer names: Jose Gonzal es Jose D. Gonzal ez J. Gonzal ez Jose Gonzal ez

4 customer names:

Jose Gonzal es

Jose D. Gonzal ez

J. Gonzal ez

Jose Gonzal ez

Analysts Reported: After identification and removal of duplicates: 1 Customer, Not 4

After identification and removal of duplicates:

Using Best Practices: If the customer database was limited to a “trade area”, and these 4 customer records were spatially plotted, ...What should have been one dot on a map - would have been 4 dots. (An erroneous depiction.)

If the customer database was limited to a “trade area”, and these 4 customer records were spatially plotted,

...What should have been one dot on a map - would have been 4 dots. (An erroneous depiction.)

Imagine The Impact: were this actually an entire trade area! Customer metrics Market share Compliance

were this actually an entire trade area!

Customer metrics

Market share

Compliance

Customer Master: 1 Mile 1,683 Address Points (1 Mile) Multiple Input Databases? Pluralization Phonetic Matching Multiple Languages Vowel Management Initials: First, Middle, Last Same Surname Dots Address and Name Frequency First Name

Gonzale xx : 1 Mile Map Symbols “ 7” Have A “J” “ 16” End With EZ vs. ES “ 22” Families

Assessment Criteria... Pre-Directional Post Directional Suffix Name House Number Primary Address Secondary Address First, Middle, Last Gender Determination Case Management Input Files: Customer Master 1 Customer Master 2 Customer Master 3 Customer Master 4 Customer Master 5 Data Quality (Errors, Omissions) Address Elements Name Number Of Input Files Factors Considered: Phonetics Languages Match Control Data Enhancement Data Transformation Customer Keys

What Does This Mean? Simply plotting data, without cleansing the data, results in erroneous plotting – poor data quality. Data quality software is critical and visualizing helps. Each data quality function must be assessed for each client.

Simply plotting data, without cleansing the data, results in erroneous plotting – poor data quality.

Data quality software is critical and visualizing helps.

Each data quality function must be assessed for each client.

Profiling A Database* Street Address Surname First Name Middle Initial *one mile radius

Data Quality Tools... Many vendors offer data quality tools today – which one is right for your organization?

Many vendors offer data quality tools today – which one is right for your organization?