Data Matching

Data deduplication involves comparing data entries based on specific attributes and identifying instances where multiple records refer to the same entity.

Data deduplication algorithms employ various matching techniques, such as exact matching, fuzzy matching, or probabilistic matching, to determine duplicate records.  Once identified, duplicates can be merged, deleted, or flagged for further action.

Data deduplication helps improve data accuracy, reduce storage requirements, and enhance overall data quality.

Data quality and enrichment techniques focus on improving the completeness, accuracy, consistency, and reliability of data.

Comparing and matching data across different sources or within the same dataset, data quality issues like missing values, formatting inconsistencies, or outdated information can be detected.

Once identified, data can be cleansed, standardised, and enriched with additional relevant information, such as geolocation data, demographic data, or third-party data sources.

This enhances the overall quality and usefulness of the data for analysis, modelling, reporting, and decision-making.

Legacy systems refer to outdated or obsolete computer systems, software, or technologies that are still in use within an organisation.

When it comes to data matching, legacy systems can pose challenges due to their limited capabilities, lack of integration with modern systems, or outdated data formats.  Data matching techniques need to be adapted to accommodate legacy systems’ limitations and ensure compatibility with their data structures.

This may involve data transformation, extraction, or integration techniques to extract data from legacy systems, match it with data from other sources, and ensure the accuracy and consistency of the matched data.

Integrating legacy systems with modern data matching tools and techniques can help organisations leverage existing data assets while ensuring efficient and accurate data matching processes.