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Data integration and its challenges
Short answer
Data integration merges data from multiple sources into a unified view. Challenges include schema conflicts, redundancy, value conflicts, and duplicates. They are handled by metadata matching, correlation analysis, standardizing units, and record linkage.
What is data integration? What are the challenges of Data Integration? How can they be handled?
Data integration is the merging of data from multiple sources (databases, data cubes, flat files, web) into a single coherent store such as a data warehouse, so that analysis can be done on a unified view.
Challenges and how to handle them
| Challenge | Explanation | Handling |
|---|---|---|
| Schema integration / entity identification | Same entity has different names, e.g. cust_id and customer_no | Use metadata (data dictionary) to match attributes; match keys and structure |
| Redundancy | An attribute can be derived from another (age and date of birth), or the same attribute repeats | Correlation analysis: χ² test for nominal, correlation coefficient r for numeric data; remove one of a correlated pair; detect duplicate tuples |
| Data value conflicts | Same attribute has different representations or units (kg and lb, Rs and USD, different date formats) | Convert to a common standard, scale and unit before merging |
| Heterogeneous data models | Relational, XML, text, different DBMS | Use wrappers/mediators or ETL tools to convert to a common model |
| Duplicates and inconsistency | Same record stored twice with different spellings | Record linkage / de-duplication, normalisation |
| Semantic differences | A "customer" in one system differs from another | Agree on common definitions, ontologies |
| Volume and time | Data size, update frequency | ETL scheduling, incremental loading |
Careful integration reduces redundancies and inconsistencies and improves the speed and quality of the later mining.
✓ Answer checked by Claude Opus 5.5 (high effort)
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