In information systems, what is data warehousing and what is its primary purpose in an organisation?

Introduction / Context:
Data warehousing is a fundamental concept in business intelligence and decision support systems. Organisations often have many transactional systems, each with its own database. A data warehouse brings together data from these systems into a central, consistent, historical store for reporting and analysis. Interview questions about data warehousing test whether candidates understand this separation between operational processing and analytical processing.

Given Data / Assumptions:

• Operational systems handle day to day transactions such as orders, payments, and inventory movements.
• A data warehouse is designed for query performance, aggregations, and historical analysis.
• Data warehousing involves ETL processes to Extract, Transform, and Load data into the warehouse.
• The question asks for the definition and primary purpose of data warehousing, not for detailed modelling techniques.

Concept / Approach:
A data warehouse is a subject oriented, integrated, non volatile, and time variant collection of data that supports management decisions. Data from multiple sources is extracted, cleaned, transformed into a common schema, and loaded into central fact and dimension tables or similar structures. Business users then run queries, dashboards, and analytical models against this data without impacting the performance of the transactional systems. This architecture enables consistent reporting and strategic analysis across the organisation.

Step-by-Step Solution:
Step 1: Identify the key requirements: integration of data from different sources, long term storage, and optimisation for read heavy workloads such as queries and reports. Step 2: Recognise that data warehousing usually involves periodic batch loads or near real time feeds, not direct transaction by transaction updates. Step 3: Understand that the warehouse is separate from backup systems; although backups exist, the main purpose is analysis, not archiving only. Step 4: Note that user interface design is a different discipline; data warehousing focuses on data structures and processing, not on screen layout. Step 5: Option a describes a central repository that integrates data from multiple sources and is optimised for reporting and analysis, which matches standard definitions. Step 6: Options b, c, and d describe real time replication, backup compression, or interface design, none of which capture the main purpose of data warehousing.

Verification / Alternative check:
Textbooks and industry references define a data warehouse as a central repository that supports business intelligence. Typical architectures show ETL tools, staging areas, and warehouse schemas such as star and snowflake. They emphasise historical data retention and support for OLAP queries. Real time replication tools and backup systems are discussed separately, which confirms that data warehousing is about integrated analytical storage as described in option a.

Why Other Options Are Wrong:
Option b is wrong because direct transactional updates between operational systems are more related to replication or integration middleware, not to warehousing. Option c is incorrect because compression of backup tapes is a storage optimisation activity, not an analytical architecture. Option d is wrong because user interface layout design belongs to front end development and does not describe data warehousing.

Common Pitfalls:
A common pitfall is to treat operational databases as warehouses by running heavy analytical queries directly on them, which can degrade transaction performance. Another is to build a warehouse without proper data quality and governance, leading to inconsistent reports. Understanding the purpose of data warehousing helps organisations separate operational and analytical workloads and design systems that support both daily operations and long term strategic analysis.

Final Answer:
Data warehousing is the process and architecture of collecting, integrating, and storing data from multiple operational sources in a central repository optimised for reporting and analysis.