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.
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Reflections: The Challenges of Master Data Resolution

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By David Loshin

I have worked for almost fifteen years on what would today be called master data management. I recall the first significant project involved unique identification of individuals based on records pulled from about five different sources, and there were three specific challenges:
  1. Determination of identifying attributes – specifying the data elements that, when composed together, provide enough information to differentiate between records representing different entities;
  2. Identity resolution in the presence of variation-having the right algorithms, tools, and techniques for using the identifying attribute values to search for and find matching records among a collection of source data sets; and
  3. Performance management- tuning the algorithms and tools properly to ensure (as close to) linear scalability as the volumes of data grow.
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