Melissa Data and Semarchy Partner to Integrate Data Quality and Master Data Management

Blog Administrator | Address Quality, Analyzing Data, Analyzing Data Quality, Data Cleansing, Data Enrichment, Data Governance, Data Integration, Data Management, Data Quality, MDM, Press Release | , , , , ,

Melissa Data Enrichers Enable Clean, Global Contact Data for Semarchy Users; Webinar Demonstrates Best of Breed Strategy for Fast, Optimized MDM Operations

Rancho Santa Margarita, CALIF – November 12, 2014 – Melissa Data, a leading provider of contact data quality and data integration solutions, today formally announced its partnership with Semarchy, a developer of innovative Evolutionary Master Data Management (MDM) software and solutions.… Read More

Melissa Data, OrderCup and General Medical Team to Present Insight on Data Quality Challenges of International Customer Addresses

Blog Administrator | Address Quality, Address Validation, Address Verification, Analyzing Data, Analyzing Data Quality, Data Cleansing, Data Quality, Global Address Verification, Global Data Quality, International Address Verification, Press Release | , , , , ,

Featured at ‘Growing Global’ Conference, Ecommerce Leaders Discuss Strategies to Reduce Shipping Costs and Fraud Through
Better Customer Data

 

Rancho Santa Margarita, CALIF. – July 9, 2014 – Melissa Data, a leading provider of contact data quality and integration solutions, today announced it is hosting a panel discussion focused on the data quality challenges of global customer data and addressing, slated for July 16 at 2:30 p.m.… Read More

Managing Customer Connectivity

Blog Administrator | Analyzing Data, Analyzing Data Quality, Customer Centricity, Data Management, Data Quality | , , , , , , , , ,

By David Loshin

At the end of our last entry, we had come to the conclusion that standardization of potentially variant data values was a key activator for evaluating record similarity when looking to group customer records together based on any set of characteristic attributes. From an operational standpoint, this activity is supported using data quality tools that can parse and standardize data.
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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.

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Reactivity vs. Proactivity

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

By David Loshin

In the past few months, we have looked at technical approaches to data quality and the use of data quality tools to parse, standardize, and cleanse data. In this next series of posts, it is time to look at harnessing the power of these tools and techniques to support a data quality management program. Most organizations are relatively immature when it comes to addressing data quality issues.
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