02 / Record
Fragmented Data and Duplicate Records
Let your team work from consistent, validated records.
Let’s review your workflow ↗Understanding the problem
Repeated versions of a customer or order affect reporting and daily work. Consolidation alone is insufficient: identify which records represent the same entity, mandatory fields and relationships. Combine data cleanup with controls that prevent new errors.
ILLUSTRATIVE SCENARIO
Example: Multiple versions of a customer
Abbreviated names, different phone formats and outdated details can produce duplicates. Matching only by name can merge unrelated customers. Define reliable identifiers and a review path for uncertain matches.
How we build the solution
- 01
Map sources, unique identifiers and record relationships.
- 02
Define matching, validation and review rules for duplicates and missing data.
- 03
Apply the same controls to new records and track subsequent changes.
Frequently asked questions
Is putting everything in one file enough?
No. A central file can still contain duplicates or conflicts. Design identifiers, validation, relationships and access boundaries together.
Can duplicate records be merged automatically?
Some can, if matching criteria are reliable. Uncertain matches need review. Assess the impact of incorrect merges and whether they can be reversed.
Will the problem return after cleanup?
It can if the rules for creating records remain unchanged. Required fields, uniqueness checks and source mappings must become part of normal operations.