[WEBINAR] When Good Data Goes Bad: How to Quickly Identify and Easily Fix Bad Data

The impact of bad data isn't hard to miss. The signs: marketing efforts with poor response rates, undeliverable mail, email bounce-backs, low customer retention, decreased revenue - the list goes on. According to TDWI, bad data costs U.S. businesses an estimated $611 billion annually. The main cause of bad data? Contact data is always in flux. Roughly 40 million (1…

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Melissa Data’s MatchUp for SQL Server Solves Duplicate Customer Records for Data Integrators

Powerful Data Quality Tool Consolidates Duplicates into Single Golden Record; Objective Data Quality Score Uniquely Determines Most Accurate Customer Information Rancho Santa Margarita, CALIF - May 8, 2014 - Melissa Data, a leading provider of contact data quality and integration solutions, today announced its TechEd 2014 exhibit will feature new matching and de-duplication functionality in the company's MatchUp Component for…

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Melissa Data and Scribe Software Partner to Enhance Data Migration with Data Quality Services

"Partnership Extends the Value of Intelligent Customer Data, Delivering Cost-Effective and Easy Integration Across the Enterprise Rancho Santa Margarita, CALIF - April 30, 2014 - Melissa Data, a leading provider of contact data quality and data integration solutions, today announced its strategic relationship with Scribe Software to deliver better access to business-critical data and power a 360-degree view of the…

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Devising the Strategy, Making the Plan

By David Loshin The three steps that I suggested in my last post about where to begin with data quality are truly meant to help determine where to begin, but also to guide the development of a longer term strategy and plan. Let me recall the three steps, but this time put them into the long-term perspective: Solicit data quality…

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Assembling a Data Quality Management Framework

By David Loshin There are two dominating questions that I am asked over and over again when people are in process to create a program for data quality management. The first is "how can you develop a business justification for a data quality program?" and the second is "how do we get started?" We are currently working on a task…

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

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…

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Performance Scalability

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…

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Reflections: The Challenges of Master Data Resolution

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: Determination of identifying attributes - specifying the data elements that, when composed together, provide enough…

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Data is Born in Business Processes

By Elliot King One of the most critical factors affecting the health of an organization is the relationship between business processes and the IT infrastructure. Data is created through businesses processes and the ways that these operations are designed and implemented have an enormous impact on the quality of the data, from acquisition through application, and retention. In short, corporate…

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Cross-Column Consistency

By David Loshin In my previous entry, I provided an example of a cross-column consistency rule in which we asserted that the date stored in the data attribute called END_DATE had a date that is later in time than the date stored in the data attribute called BEGIN_DATE. Abstractly, this represents a dependency between two data values related to the…

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