During a data integration project, what key factors typically need to be addressed to successfully integrate data from multiple sources?

Introduction / Context:
Integrating data from multiple sources is a complex task that involves much more than simply moving records. Successful projects systematically address technical, semantic, and organizational factors. Interview questions about these factors test whether you appreciate the full scope of work required to build robust, maintainable data integration solutions.

Given Data / Assumptions:

• Several heterogeneous systems contribute data, including databases, files, and external feeds.
• Each source has its own schema, codes, and data quality profile.
• The target may be a data warehouse, data mart, or integrated operational store.
• Integration must comply with security, privacy, and performance requirements.

Concept / Approach:
Key factors in data integration include analyzing source systems and understanding their data models, mapping fields from sources to targets, and defining the necessary transformations to harmonize formats and semantics. Data quality must be assessed and improved through validation, cleansing, and deduplication. Keys and identifiers must be managed, often using surrogate keys to unify entities. Additionally, integration processes must be designed with security, performance, and governance in mind, including scheduling, monitoring, and error handling.

Step-by-Step Solution:
Step 1: Emphasize the need to understand source structures and business meaning, including table relationships, key fields, and data types. Step 2: Describe mapping activities, where source fields are aligned with target fields and transformations such as type conversion, aggregation, and code translation are defined. Step 3: Highlight the importance of data quality checks to identify missing values, inconsistencies, duplicates, and outliers, and to apply cleaning rules. Step 4: Explain how keys and identifiers are handled, including generating surrogate keys for dimensions and resolving conflicts between overlapping identifiers from different systems. Step 5: Mention cross cutting concerns such as securing sensitive data, designing for efficient performance within load windows, and putting governance mechanisms in place for monitoring, logging, and auditing.

Verification / Alternative check:
Project plans for real integration initiatives typically include phases for source system analysis, mapping and design, data quality assessment, ETL development, performance testing, and security review. Post implementation reviews often identify issues in these areas when they are not addressed up front, such as slow loads due to underestimated volumes or inaccurate reports due to unhandled code differences. This underscores the importance of considering all these factors.

Why Other Options Are Wrong:
Option B trivializes integration by focusing only on report fonts, which are unrelated to how data is actually integrated. Option C assumes hardware alone solves integration challenges, ignoring the need for analysis, mapping, and transformation. Option D suggests deleting history, which would remove valuable analytical context and is not a general integration requirement.

Common Pitfalls:
Common pitfalls include underestimating the effort required to resolve semantic differences between systems (for example, different definitions of an active customer) and not investing enough in data quality improvement. Another mistake is designing ETL jobs without considering long term performance and maintainability, leading to fragile or slow pipelines. Addressing the full set of factors from the outset greatly increases the chances of a successful integration project.

Final Answer:
Key factors in data integration include understanding and mapping source data, applying transformations, improving data quality, resolving semantic and code differences, managing keys and identifiers, and designing secure, performant, and well governed ETL processes.