Improving Identity Resolution and Matching via Structure, Standards, and Content

Blog Administrator | Analyzing Data, Analyzing Data Quality, Data Cleansing, Data Enhancement, Data Enrichment, Data Integration, Data Management, Data Profiling, Data Quality, Duplicate Elimination, Record Linkage | , , , ,

By David Loshin

One of the most frequently-performed activities associated with customer data is searching – given a customer’s name (and perhaps some other information), looking that customer’s records up in databases. And this leads to an enduring challenge for data quality management, which supports finding the right data through record matching, especially when you don’t have all the data values, or if the values are incorrect.
Read More

Clean Data is Good Data

Blog Administrator | Analyzing Data Quality, Data Cleansing, Data Enrichment, Data Integration, Data Management, Data Quality | , , , , ,

By Elliot King

The cliché is as old as computing itself–garbage in, garbage out. And that cliché is as true now as ever, if not more so. Unfortunately, with information flowing into companies from so many sources including the Web and third-party providers, mistakes should not just be expected; they are basically inevitable.
Read More

Enter the Contact Zone: Where Data Integration and Data Quality Are Simplified

Blog Administrator | Data Enhancement, Data Enrichment, Data Integration, Data Management, Data Profiling, Data Quality | , , , , , , , , ,

By Joseph Vertido

For many, the concepts of data integration and data quality are separate and have no commonality. But in reality, when you combine them – they create a partnership that excels. Where data quality leaves off, data integration begins, and vice versa.
Read More

Validation, Standardization, and Correction: Tool or Process?

Blog Administrator | Address Check, Address Correction, Address Standardization, Address Validation, Data Enrichment, Data Governance, Data Integration, Data Management, Data Profiling, Data Quality | , , , , ,

By David Loshin

There are all sorts of tools associated with
address standardization, cleansing, and validation. As an example, the USPS
has a certification program for software vendors, referred to as CASS
(Coding Accuracy Support System)™ certification. According to their
website,

CASS enables the Postal Service™ to evaluate the accuracy of address matching software programs in the following areas:

(1) five-digit coding
(2) ZIP + 4/ delivery point (DP) coding
(3) carrier route coding
(4) DPV®
(5) DSF2®
(6) LACSLink®
(7) eLOT®
(8) RDI™ products

CASS allows vendors/mailers the opportunity to test their address-matching software packages and, after achieving a certain percentage of compliance, to be certified by the Postal Service.

Read More

Achieving “Proactivity?”

Blog Administrator | Analyzing Data Quality, Data Enrichment, Data Management, Data Profiling, Data Quality | , , , ,

By David Loshin

Standardizing the approaches and methods used for reviewing data errors, performing root cause analysis, and designing and applying corrective or remedial measures all help ratchet an organization’s data quality maturity up a notch or two. This is particularly effective when fixing the processes that allow data errors to be introduced in the first place totally eliminates the errors altogether.
Read More