1. Foundations of Business Intelligence: Databases and Information Management

2.   Bits (smallest unit of data a computer can handle)  Bytes (8 bits; each represents a single character – letter, number, or symbol)  Field (group of words or complete number)  Record (group of related fields)  File (group of records of same type)  Database (group of related files)  Entity (person, place, thing, or event about which we store and maintain info)  Attribute (characteristic or quality describing an entity) Terms

3.   Data redundancy and inconsistency  Program-data dependence  Lack of flexibility  Poor security  Lack of data sharing and availability Traditional File Environment

4.   DBMS – Access, Oracle, DB2 examples/software  Logical view – data as perceived by end users and data specialists  Physical view – where data stored and structured  Relational DBMS – represent data as two dimensional tables (called relations)  Tuples (rows in a table)  Key field  Primary Key  Foreign key Database Management Systems (DBMS)

6.  Relational DBMS Select, Join, and Project

8.   Object-Oriented (stores data and procedures as objects)  Databases in the Cloud Databases in the Cloud  DBMS capabilities  Data definition – specify structure  Data dictionary – stores definitions of data  Query and reporting tools, including SQL DBMS

9.   Normalization (smallest form of data structures) Database Design

10.   Referential integrity (rules; consistency in relationships between tables)  Entity Relationship (ER) diagram (show relationships between the entities in your database) Database Design (cont.)

11.   Data Warehouse (stores current and historical data; from multiple sources)  Data Mart (subset; separate database for different population) Data Warehouses

12.  Multidimensional Model

13.   Online Analytical Processing (OLAP) (supports multidimensional data analysis)  Data Mining (discovery driven data analysis)  Associations  Sequences  Classification  Clustering  Forecasting  Predictive analytics (uses data mining techniques; predict future outcomes)  Web Mining (patterns from WWW) – example Google AnalyticsGoogle Analytics  Text Mining (extract elements from unstructured data sets) Tools for Business Intelligence

14.   Database server (where database resides) Database Server

15.   Information Policy  Data administration  Data governance  Database administrator  Data Quality  Data quality audit  Data cleansing (scrubbing) Other