Archive for the ‘Address Standardization’ Tag

npENGAGE – Data Hygiene

npENGAGE – Data Hygiene

Good article… Thanks Mary Dempsey.

Go to this link and sign up for one of the many newsletters which focuses on different aspects of your business.  I believe the section I found this article in was Analytics – Data Hygiene, Is a bad address costing your organization?

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Wiki – Data Cleansing

Wiki – Data Cleansing

Data cleansing, data cleaning or data scrubbing is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database. Used mainly in databases, the term refers to identifying incomplete, incorrect, inaccurate, irrelevant, etc. parts of the data and then replacing, modifying, or deleting this dirty data.

After cleansing, a data set will be consistent with other similar data sets in the system. The inconsistencies detected or removed may have been originally caused by user entry errors, by corruption in transmission or storage, or by different data dictionary definitions of similar entities in different stores.

Data cleansing differs from data validation in that validation almost invariably means data is rejected from the system at entry and is performed at entry time, rather than on batches of data.
The actual process of data cleansing may involve removing typographical errors or validating and correcting values against a known list of entities.

PC Magazine’s Definition

PC Magazine’s Definition

The condition of data in a database. Clean data are error free or have very few errors. Dirty data have errors, including incorrect spelling and punctuation of names and addresses, redundant data in several records or simply erroneous data (not the correct amounts, names, etc.).

Westminster International – Data Dry Cleaning

For more detailed information about:  Data Dry Cleaning (TM)

Click Here

What is Data Dry Cleaning?

 

1.)    Address Correction/Standardization

2.)    Case Conversion to improve case-sensitive address data

3.)    Canadian or US NCOA (National Change of Address)

4.)    Duplicate Identification

NCOA – “Data Hygiene” Poll Question

If you want to learn more about NCOA before you answer the DataHygiene poll:  Click Here

Hint:  Over 40 million Americans change this every year!

Or, if you want to answer the poll question now:

Enclarity – ProviderPoint

I continue to get requests from companies who would like to have some information posted about one or more of their companies offerings or added to the blogroll.  Here is a company that specializes in the healthcare payer market:

Company Name: Enclarity

Service Offering: ProviderPoint

When Enclarity receives your provider file, it uses AcuSync to cleanse the file – identifying and replacing inaccurate, duplicate and incomplete records. It also augments the file with additional data attributes.  Enclarity’s data scientists work with you to establish business rules that govern what updates, corrections and augmentations happen automatically, and where your staff’s expertise will come into play. After those rules are applied, ProviderPoint delivers a clean provider file ready for easy integration into your systems.

ProviderPoint cleans and enhances your provider files with information from Enclarity’s Master Provider Referential Database, which uses Enclarity’s innovative AcuSync™ process to leverage thousands of referential and transactional data sources.

AcuSync uses advanced analytical and database methods to efficiently and reliably standardize, match and join data from different sources, and then produce a provider profile that contains the best available information.

If anyone would like to comment about Enclarity or any one of their service offerings, please do so.

For more information regarding Enclarity: www.enlarity.com

Netrics Matching Engine

I recently read about a Data Matching Innovator – Netrics – Positioned in Magic Quadrant for Data Quality Tools by Gartner in 2008. 

Netrics claims to provide unparalleled matching accuracy, despite inconsistencies in diverse datasets, eliminating the need for rigid standardization.  Netrics software enables successful data integration and data quality projects – by overcoming data that is inconsistent, incomplete, and incomparable – across every type of data subject area. 

This matching engine sounds very intriguing to me. Has anyone used or heard about Netrics or have information they can share about their data quality solution?

Global-Z International Formats Addresses To Each Country’s Postal System

Address Hygiene (also referred to as Address Standardization) is at the core of the Globalz unique data management technology. The Globalz process determines the accuracy of an address, and whether it’s correctable. It then corrects addresses and formats them to the unique requirements of each country’s postal system.

Address Standardization “Modules”

Each company has their own “components” or modules for address standardization, correction, and enhancement purposes – in batch and/or real time mode.

What module(s) do you currently use (or license) or would like to get more information on… please comment.

First Data Hygiene “Coder” – 19??

I think one of the first “data hygiene” coders that was commercially made available was Code1 by Group 1 Software around 1981.  My first job in 1983 was at Metromail Corporation located in Lincoln, NE and at that time we were using Code1.

Does 1981 sound about right?

Hmmm… ZIP Codes were first used by the USPS (Post Office) in 1965.  I guess between this time and the early 80’s there were a lot of “home grown” match engines out there using postal databases.

If anyone has any better “intel” please share!