Master data management (MDM) is the process of creating and maintaining a single, consistent, and accurate view of an organization’s data assets. MDM helps to ensure data quality, reduce data duplication, improve data governance, and enable data-driven decision-making.
One of the key challenges of MDM is how to match and merge records from different sources that refer to the same entity, such as a customer, a product, or a location. This is also known as entity resolution or record linkage.
Matching and merging records involves several steps:
Data profiling: An analysis of the source data’s structure, content, quality, and relationships is called data profiling. Identification of data problems, such as missing numbers, irregularities, anomalies, and outliers, is aided by data profiling.
Data standardization: This is the process of transforming the source data into a common format and applying rules for data cleansing, validation, and enrichment. Data standardization helps to improve data quality and comparability across sources.
Data matching: This is the process of using predetermined criteria, such as precise or fuzzy matching, probabilistic or deterministic matching, or rule-based or machine learning-based matching, to locate records that relate to the same item. Data matching facilitates the discovery of possible correlations and duplicates between sources.
Data merging: This is the process of combining records that match into a single record that represents the best version of the entity. Data merging helps to create a unified and consistent view of the data.
Data survivorship: This is the process of selecting the best attributes from the merged records to form the final record. Data survivorship helps to ensure data accuracy and completeness.
Some of the common techniques include
Blocking: This is a technique that helps to reduce the number of comparisons by grouping records based on some common attributes, such as name, address, or phone number.
Clustering: This is a technique that helps to group records that are likely to match based on some similarity measures, such as edit distance, Jaccard index, or cosine similarity.
Scoring: This is a technique that helps to assign a numerical value to each pair of records based on how well they match, such as using weights, thresholds, or probabilities.
Deduplication: This is a technique that helps to eliminate duplicate records within a single source or across multiple sources.
Some of the best practices include:
- defining MDM needs and business objectives
- Establishing data governance policies and roles for MDM
- Developing a data quality framework and metrics for MDM
- Choosing appropriate tools and techniques for MDM
- Testing and validating the results of MDM
- Monitoring and maintaining the performance of MDM
By following these steps, techniques, and best practices, MDM can help organizations to match and merge records effectively and efficiently and create a unified view of their data assets.
Purpose of Match and Merge in Reltio MDM
The primary purpose of match and merge in Reltio MDM is to ensure data quality by eliminating duplicates, which can lead to inefficiencies and inaccuracies in business operations. By having a single source of truth organizations can make more informed decisions, improve customer relationships, and streamline business processes.
Match Levels
Reltio MDM provides various levels of matching to cater to different needs. These levels range from exact matches, where records are identical across all fields, to potential matches that may require further review. The system uses sophisticated algorithms to determine the two records refer to the same entity.
Match Rules
Match rules in Reltio MDM are the guidelines that dictate how data is compared. These rules can be customized based on the specific needs of an organization. They consider various attributes and weights assigned to them, allowing for a nuanced approach to matching that can be tailored for different scenarios.
In conclusion, match and merge functionalities in Reltio MDM play a crucial role in maintaining high-quality data. By understanding and effectively utilizing these features, organizations can significantly enhance their data management strategies.
