DATA WAREHOUSING AND DATA MINING
Chapter notes, video classes, MCQ practice tests and quick-revision one-liners for Information Technology and Digital Banking (Elective) — CAIIB.
One-liners from this chapter
Free sample — 8 of 66 rapid-fire Q&A cards.
What is a Data Warehouse in the context of banking?
A Data Warehouse is a large, centralized repository that integrates data from multiple banking sources (core banking, ATM, internet banking) to support management reporting, analytics, and decision-making.
What is 'data scrubbing' in the ETL process of a Data Warehouse?
Removing errors and inconsistencies from source data before loading.
How does a Data Warehouse differ from an operational database in a bank?
Operational databases handle day-to-day transactions (OLTP) and are optimized for fast writes, while a Data Warehouse is optimized for complex queries and historical analysis (OLAP) across large datasets.
What is 'slice and dice' in OLAP analysis?
Selecting and viewing specific data subsets across multiple dimensions.
What does OLAP stand for and how is it used in banking?
OLAP stands for Online Analytical Processing; banks use it to analyze multidimensional data such as branch-wise, product-wise, or period-wise performance from the Data Warehouse.
What is a 'surrogate key' in Data Warehousing?
A system-generated unique identifier used in dimension tables.
What is the ETL process in Data Warehousing?
ETL stands for Extract, Transform, Load — the process of extracting data from source systems (like CBS), transforming it to a consistent format, and loading it into the Data Warehouse for analysis.
What is 'market basket analysis' in banking Data Mining?
Identifying products customers tend to purchase or use together.
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