In enterprise data management, what is data integration and what does it aim to achieve?

Introduction / Context:
Data integration is a foundational concept in building data warehouses, master data systems, and unified analytics platforms. Organizations typically store information in many separate applications, and integrating that data is necessary to obtain a complete view of customers, products, and performance. Interview questions about data integration test whether you understand this broader goal beyond simple data movement.

Given Data / Assumptions:

• The enterprise has multiple heterogeneous source systems such as CRM, ERP, billing, and web analytics.
• Each system may have its own data structures, codes, and quality issues.
• Business stakeholders want consolidated reports and dashboards that span these systems.
• ETL, ELT, or data virtualization technologies are available to move and transform data.

Concept / Approach:
Data integration is the end to end process of extracting data from different sources, transforming it into consistent formats and structures, and loading or presenting it in a unified way. This may involve mapping fields, reconciling codes, handling duplicates, and applying business rules. The integrated data can be stored in a data warehouse, data lakehouse, or operational data store, or it can be exposed virtually through a uniform access layer. The main aim is to provide trustworthy, consistent information for decision making across the organization.

Step-by-Step Solution:
Step 1: Define data integration as combining and harmonizing data from multiple sources into a single coherent view. Step 2: Explain that integration includes extracting data from source systems, transforming data types and structures, and resolving differences in codes and semantics. Step 3: Describe how integrated data is loaded into target stores such as data warehouses, data marts, or integrated databases. Step 4: Mention that integration enables cross system reporting, such as comparing CRM leads with ERP sales orders and support tickets. Step 5: Emphasize that the ultimate goal is to support consistent analytics and operations with a trusted, organization wide view of key data.

Verification / Alternative check:
Real world integration projects often start with multiple isolated systems that cannot answer questions like total customer value across channels. After implementing ETL pipelines and an integrated warehouse, users can run unified reports and self service analytics. Success is evident when previously conflicting numbers are reconciled and stakeholders agree on a single version of the truth for core metrics.

Why Other Options Are Wrong:
Option B focuses only on compression, which reduces storage but does not combine or reconcile data from different sources. Option C describes a destructive process of deleting and recreating databases, which is not integration. Option D suggests that renaming columns alone is sufficient; in practice, integration requires moving, transforming, and aligning data at a deeper semantic level.

Common Pitfalls:
A common pitfall is treating data integration as a one time project instead of an ongoing process that must adapt as systems change and new sources appear. Another mistake is integrating data at a purely technical level without validating business meaning, which can produce misleading results. Effective data integration involves clear business definitions, robust ETL processes, data quality management, and collaboration between IT and business teams.

Final Answer:
Data integration is the process of combining data from multiple, heterogeneous sources into a unified, consistent view that supports enterprise reporting, analytics, and operational needs.