BUS 237 Study Guide - Final Guide: Decision-Making, Olap Cube, Data Mart

• School: Simon Fraser University
• Department: Business Administration
• Course: BUS 237
• Professor: Kamal Masri

Document Summary

Data quality marginally necessary don"t need to be. Inconsistent data: dirty data, missing values, data not integrated, wrong granularity. Dirty data problematic data: too much data, too fine, not fine enough, too many attributes, too many data points, data-mining applications suffer if many values are missing. Inconsistent data common in data that has gathered over time (i. e. area code, postal codes, etc. ) Data granularity refers to the degree of summarization or detail. Clickstream data very fine data (i. e. capturing customer"s clicking behaviour on websites: generally, better to have too fine a granularity than too coarse b/c data can be always made coarser. If granularity is too coarse, then there"s no way to separate data into constituent parts. Oltp systems are backbone of all functional, cross-functional, and interorganizational systems in a company. Designed to efficiently enter, process, and store data. Combines large databases wit input devices (i. e. grocery store scanners)