[WEBINAR] How to Seamlessly Cleanse and Enrich Your Customer Data

Blog Administrator | Address Quality, Address Validation, Address Verification, Analyzing Data, Analyzing Data Quality, Data Enrichment, Data Management, Data Migration, Data Quality | , , , , ,

Your contact data is perhaps your organization’s most valuable asset. But how good is your data?

At least 25 percent of most companies’ data is probably inaccurate, according to industry analyst Gartner. Bad data is likely caused by the fact that contact data is always in flux – as people move, get married, retire or pass away. This poses a tremendous challenge when migrating data from different sources.… 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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Centricity and Connections: Clearing the Air

Blog Administrator | Address Quality, Analyzing Data, Customer Centricity, Data Quality, Record Linkage | , , , ,

By David Loshin

There are opportunities for adjusting your strategy for customer centricity based on understanding the grouping relationships that bind individuals together (either tightly or loosely). And in the last post, we looked at some examples in which linking customer records into groups was straightforward when the values to be compared and weighted for similarity are exact matches. When the values are not exact, it introduces some level of doubt into the decision process for including a record into a group.

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