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

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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

Managing Unique Customer Identities with Master Entity Indexes

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By David Loshin

In the past few entries in this series we have basically been looking at an approach to understanding customer behavior at particular contextual interactions that are informed by information pulled from customer profiles.

But if the focal point is the knowledge from the profile that influences behavior, you must be able to recognize the individual, rapidly access that individual’s profile, and then feed the data from the profile into the right analytical models that can help increase value.… Read More

Melissa Data and Blu Sky Form Strategic Alliance to Solve Healthcare Data Management Challenges

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Rancho Santa Margarita, CALIF- January 14, 2014 – Melissa Data, a leading provider of global contact data quality and integration solutions, today announced its strategic alliance with Blu Sky to solve growing data management challengesin healthcare markets. Melissa Data offers a comprehensive platform for data integration and data quality, and Blu Sky provides data capture technologies optimized for EpicCare software deployments used to administer mid-size and large medical groups, hospitals, and integrated healthcare organizations.… Read More

Performance Scalability

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By David Loshin

In my last post I noted that there is a growing need for continuous entity identification and identity resolution as part of the information architecture for most businesses, and that the need for these tools is only growing in proportion to the types and volumes of data that are absorbed from different sources and analyzed.

While I have discussed the methods used for parsing, standardization, and matching is past blog series, one thing I alluded to a few notes back was the need for increased performance of these methods as the data volumes grow.… Read More