Foundations of Business Intelligence:
Databases and Information Management
Terms

 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)
Traditional File Environment

 Data redundancy and inconsistency
 Program-data dependence
 Lack of flexibility
 Poor security
 Lack of data sharing and availability
Database Management
Systems (DBMS)

 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)

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Tuples (rows in a table)
Key field
Primary Key
Foreign key
Relational DBMS

Select, Join, and Project
DBMS

 Object-Oriented (stores data and procedures as
objects)
 Databases in the Cloud
 DBMS capabilities
 Data definition – specify structure
 Data dictionary – stores definitions of data
 Query and reporting tools, including SQL
Database Design

 Normalization (smallest form of data structures)
Database Design (cont.)

 Referential integrity (rules; consistency in
relationships between tables)
 Entity Relationship (ER) diagram (show
relationships between the entities in your database)
Data Warehouses

 Data Warehouse (stores current and historical data; from
multiple sources)
 Data Mart (subset; separate database for different population)
Multidimensional Model

Tools for Business Intelligence

 Online Analytical Processing (OLAP) (supports
multidimensional data analysis)
 Data Mining (discovery driven data analysis)

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Associations
Sequences
Classification
Clustering
Forecasting
 Predictive analytics (uses data mining techniques; predict
future outcomes)
 Web Mining (patterns from WWW) – example Google
Analytics
 Text Mining (extract elements from unstructured data sets)
Database Server

 Database server (where database resides)
Other

 Information Policy
 Data administration
 Data governance
 Database administrator
 Data Quality
 Data quality audit
 Data cleansing (scrubbing)