Reconcile Records Across Sources
Problem Statement
Design and implement a system to reconcile person records originating from multiple disparate data sources. Each source provides records containing fields such as name, email, phone, and address.
Challenges
The input records are characterized by several data quality issues:
- Missing Values: Fields may be incomplete.
- Inconsistent Formatting: Data types or formats may vary across sources (e.g., phone numbers, addresses).
- Duplicates: Redundant records may exist within or across sources.
- Conflicting Information: Different sources may provide contradictory values for the same attribute of a person.
Requirements
Your reconciliation approach must fulfill the following objectives:
- Identity Resolution: Accurately determine if two records refer to the same real-world individual.
- Record Grouping: Group all identified matching records together.
- Canonical Record Generation: For each group of matching records, produce a single, authoritative, and merged record.
- Unmatched Record Handling: Records that do not match any other record should remain separate and untouched.
Key Considerations
Be explicit in your design about the following aspects:
- Normalization: How will you standardize data across sources to facilitate comparison?
- Matching Rules/Confidence Scoring: What logic or algorithms will you employ to identify matches, and how will you quantify the certainty of a match?
- Conflict Resolution: How will you decide which values to retain when conflicting information is present for a merged record?
- Validation: How would you measure and ensure the quality and accuracy of the generated matches and canonical records?
AI interview prep
Core dilemma
Balancing the precision and recall of identity resolution (matching accuracy) with the scalability and performance required to process large, dirty datasets, while also establishing robust, auditable conflict resolution and validation mechanisms.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Reconcile Records Across Sources
Problem Statement
Design and implement a system to reconcile person records originating from multiple disparate data sources. Each source provides records containing fields such as name, email, phone, and address.
Challenges
The input records are characterized by several data quality issues:
- Missing Values: Fields may be incomplete.
- Inconsistent Formatting: Data types or formats may vary across sources (e.g., phone numbers, addresses).
- Duplicates: Redundant records may exist within or across sources.
- Conflicting Information: Different sources may provide contradictory values for the same attribute of a person.
Requirements
Your reconciliation approach must fulfill the following objectives:
- Identity Resolution: Accurately determine if two records refer to the same real-world individual.
- Record Grouping: Group all identified matching records together.
- Canonical Record Generation: For each group of matching records, produce a single, authoritative, and merged record.
- Unmatched Record Handling: Records that do not match any other record should remain separate and untouched.
Key Considerations
Be explicit in your design about the following aspects:
- Normalization: How will you standardize data across sources to facilitate comparison?
- Matching Rules/Confidence Scoring: What logic or algorithms will you employ to identify matches, and how will you quantify the certainty of a match?
- Conflict Resolution: How will you decide which values to retain when conflicting information is present for a merged record?
- Validation: How would you measure and ensure the quality and accuracy of the generated matches and canonical records?