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Data quality processes have improved a lot in last one decade. Earlier data processing consisted of cleaning names and addresses, however now its scope has widened a lot. In this paper, the newer definition of data quality has been discussed. According to the new definition, data quality processing includes all inclusive data management with profiling, cleansing, parsing, matching and monitoring information. Similarly, the paper also explores the new concept of Identity Resolution. Identity Resolution now comprises fraud detection, customer service and security screening. It also has match functionality under its scope. This study seeks to establish the relationship between Identity Resolution and Data Quality. It also works towards integrating the two for enhancing performance and reliability. Data Quality is very important for all the organizations including supply chain companies. The companies need to enforce quality standards on data to ensure their compliance with business rules for financial data, customer and asset data types. Data Quality also forms the backbone of Customer Data Integration and Master Data Management. The paper also discusses the various uses of Identity Resolution. Identity Resolution has become important for a wide range of activities including Anti-Money Laundering (AML) and Customer Relationship Management (CRM). Identity Resolution is also helpful in discovering the duplicities in the data and for relationship linking. It is also very important for accurate matching, which is essential for the working of a number of applications.
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