Data Management

Traditional File Processing

Data start to be manage in a very traditional way, that is Traditional Files Processing. In this ways, business application can only use specific data files that contain only specific data records. The disadvantage of this data management include,

1. Data Redundancy - data duplicated in several files and hard to update in each of the files.
   
2. Lack of Data Integration - end user finds difficult to find information when they need to access different files.


3. Data Dependance - all the files location. organization and application software - depended on each other.


4. Lack of Intregrity or Standardization - different end users and application defined data element differently.
   

Data Management Approach - data is being consolidate into database that can be accessed by different program using Database Management System(DBMS).

Database Management System

Student Database

Logical Data elements

Character/bit - single alphabetic. ex: A, B , C etc


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• Field/data item - group of related character representing an attribute, ex: Lastname, state etc
  

• Record - group of field to describe attribute of an entity, ex: Galla, Mclaughing etc

• File /Table - group of related record, ex: as in the picture.

Related table - matching field among tables, primary keys

Type of Database

1. Operational database – needed data are stored to support business processes and application. Other name includes subject area databases, transaction databases and production databases.

2. Distributed databases – parts of database are copied to network server at variety location. This is to protect the data. However, any changes must be done in every location.

3. External databases – free database available in commercial online services. Example Google.

4. Hypermedia Databases – website that stores hyperlinked pages of multimedia.

DATA WAREHOUSE

Databases in an organization will be extract into static data and stored in warehouses. Data warehouses can be divided to data marts.

DATA MINING

Hidden pattern or trend that are analyzed in data warehouses. Example include• Prevent customer attrition• Cross sell to existing customer• Profile customer with more accuracyMarket Basket Analysis - common data mining analysis that determined what product customer will purchase together.