Data matching and data mastering are two crucial steps in data governance and data quality management, as these enable organizations to have a unified and reliable view of the data. 

Data Matching:

Data matching, also known as record linkage or data deduplication, is the process of identifying and linking similar or duplicate records from different data sources or within a single data source. The primary goal of data matching is to ensure data accuracy and consistency by eliminating redundancy and ensuring that each unique entity is represented only once in a dataset.

Key aspects of data matching include:
  • Similarity Assessment: Data matching algorithms assess the similarity between records based on various attributes or fields, such as names, addresses, phone numbers, and more. Common techniques for measuring similarity include string similarity metrics like Jaccard similarity or edit distance.
  • Scalability: Data matching can be a complex and resource-intensive task, especially when dealing with large datasets. Efficient algorithms and techniques are used to handle scalability issues.
  • Probabilistic Matching: In cases where perfect matches are not possible due to data discrepancies or errors, probabilistic matching algorithms are used to calculate the likelihood that two records are a match based on the available information.
  • Blocking: To improve efficiency, data matching often involves a blocking or indexing step, where records are grouped into smaller subsets before the actual matching process. This reduces the number of record comparisons required

Data Mastering:

Data mastering, also known as data consolidation or data integration, is the process of creating a single, authoritative, and comprehensive version of data from various sources. The primary goal of data mastering is to ensure that all data elements related to the same entity are integrated and harmonized to provide a consistent and accurate view of that entity.

Key aspects of data mastering include:
  • Data Integration: Data from different sources are brought together and integrated into a single, unified view. This involves mapping and transforming data to ensure that it conforms to a common data model or schema.
  • Data Cleansing: Data mastering often includes data cleansing or data quality improvement activities to correct errors, standardize formats, and remove inconsistencies.
  • Data Enrichment: Additional information may be added to the data during the mastering process, such as geolocation data, demographic information, or other relevant details to enhance the dataset’s value.
  • Hierarchy Management: Data mastering can involve managing hierarchies, especially in scenarios where data represents complex relationships, such as organizational structures or product hierarchies.
  • Version Control: A version control system is often used to track changes and updates to the mastered data, ensuring that a historical record of changes is maintained.
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Our team has years of experience integrating data management solutions to analytics and data warehouse projects in many notable organizations within Australia and has the depth of knowledge and expertise that can help speed up your data and analytics project or journey. Contact us so we can discuss your specific requirements and where we can add value.


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