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ADM1370 Module 3

Databases

QuestionAnswer
Data Raw facts that descrive the characteristics of an event
Information Data converted into meaningful and useful content
Database A collection of data organized to serve many applications efficiently by centralizing data and controlling redundant data. A single database services multiple applications.
Problems with traditional file organization Data redundancy: Multiple files all over Data inconsistency: Same attribute with different values Lack of flexibility: not much you can do with data Poor security Lack of data availability: Information cannot flow freely across the organization.
Some sources of low quality information -Customers intentionally enter false information -Call centres enter abbreviated or erroneous information by accident or to save time -Different entry standards
Costs of poor information Tracking customers Selling opportunities Valuable customers Poor marketing
Database management system (DBMS) A software package to create and maintain databases. - Acts as an interface between application and physical data files. -Relieves the programmer from the task of finding logical data (business view) and physical data (actual view)
How does DBMS solve the problems of traditional filing? -Reduces data redundancy by control/minimizing -Uncouples programs and data to stand on its own -Access and availability increases -Managing security is easier
Relational DBMS Represents data as 2D tables. The tables are related by common elements
Primary key A field or group that uniquely identifies a given entity in a table.
Foreign key A primary key of one table that appears as an attribute in another table and acts to provide a logical relationship between two tables.
Three Basic Operations in a Relational DBMS Select: the subset rows that meet a criteria Join: the relational tables Project: relevant information
Hierarchical DBMS, Network DBMS Organizes data in a tree-like structure like a family tree. Supports one-many-relationships Network: Logical many-to-many relationships between data. OUTDATED, not used in new applications Less flexible than Relational DBMS Lack of support
Object-Oriented DBMS (OODBMS), Hybrid OODBMS Stores data and procedures as objects that can be retrieved and shared automatically. Hybrid OODBMS: Combines the benefits of relational and OODBMS
Data dictionary a file that stores definitions of data elements and their characteristics.
Distributed Database, - Partitioned database. - Replicated database Partitioned database: parts of the database are stored in different location Replicated database: duplicates of entire databases at all remote locations
Data cleansing Activities that detect and correct data in a database or file that are incorrect, incomplete, redundant... etc.
Data warehouse A copy of transaction data specifically structured for data mining Stores historical and current data used for decision making
Extraction, transformation, and loading (ETL) Extracts information from internal and external databases transforms the information into common set of definitions Loads into data warehouse.
Data Mart A subset of data warehouse information, contains summarized or highly focused portion of data
Online Analytical Processing (OLAP) Interactive, exploratory analysis of multidimensional data from multiple dimensions/perspectives.
Hypermedia Database Online database, supporting graphic, data organized as nodes
Data Mining and its use Tools for deep down analysis of large pools of data - To find patterns - To predict future behaviour - To guide decision making
Cluster analysis Divides an information set into mutually exclusive groups -members are as close as possible -different groups are far as possible -To study behaviour of groups
Data mining and Target marketing (prospects) -identifies prospects -Chooses appropriate communication channels -Picks suitable messages
Customer Relationship Management (CRM) -Match campaigns to customers Use loyalty programs don't stop marketing -Customer segmentation Find behavioural segments and use techniques -Reduce exposure to credit risk predict deafults, and avoid bad customers
Normalization process of creating small, stable, flexible data structures from complex groups of data ~necessary for effective use of relational database
Classification recognizes patterns describing a group by examining characteristics of a customer
Predictive analysis use of data mining, historical data, and assumptions about future conditions to predict outcomes of events
Market Basket Analysis association information; detection of customers' buying behaviour based on their purchasing choices to predict future behaviour
Information policy -Specifics organization's rules for sharing, acquiring, classifying, and inventory information. -Lays out specific procedures
Data administration -Responsibility for specific policies and procedures of how data can be managed.
Data governance Deals with policies and processes for managing availability, usability, and security of the data employed in an enterprise. -In compliance with government regulation
Created by: wli043
 

 



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