What are the two risks associated with Master Data Management (MDM) matching?

What are the two risks associated with Master Data Management (MDM) matching?

• A. Data loss and data corruption
• B. False positives and false negatives
• C. Unreliable data sources and inefficient algorithms
• D. High costs and data redundancy

Answer: (B)

The identification of false positives and false negatives as risks in Master Data Management (MDM) matching is a critical aspect of ensuring data quality. In MDM, matching involves determining which records represent the same entity across different data sources. False positives occur when two distinct entities are incorrectly matched, leading to inaccurate data linkage. This can result in erroneous insights, misguided business decisions, and compromised trust in the data. For instance, if the system matches two different customers as one due to similarities in their names, it can affect customer service and relationship management negatively. Conversely, false negatives arise when records that should be matched are not identified as such. This means relevant data remains siloed or underutilized, which can hinder comprehensive analysis and integration efforts. For example, if two records for the same customer are treated as separate due to an oversight in the matching process, the business might miss opportunities for cross-selling or providing tailored services. Overall, managing these risks effectively is crucial for maintaining the integrity of the data managed through MDM and ensuring that organizations can trust their data-driven decisions.

The identification of false positives and false negatives as risks in Master Data Management (MDM) matching is a critical aspect of ensuring data quality. In MDM, matching involves determining which records represent the same entity across different data sources.

False positives occur when two distinct entities are incorrectly matched, leading to inaccurate data linkage. This can result in erroneous insights, misguided business decisions, and compromised trust in the data. For instance, if the system matches two different customers as one due to similarities in their names, it can affect customer service and relationship management negatively.

Conversely, false negatives arise when records that should be matched are not identified as such. This means relevant data remains siloed or underutilized, which can hinder comprehensive analysis and integration efforts. For example, if two records for the same customer are treated as separate due to an oversight in the matching process, the business might miss opportunities for cross-selling or providing tailored services.

Overall, managing these risks effectively is crucial for maintaining the integrity of the data managed through MDM and ensuring that organizations can trust their data-driven decisions.