By Aspet Galestanian 05/08/2010

 Data is information that has been translated into a form that is more convenient to move or process.

 The terms information and knowledge are frequently used for overlapping concepts. The main difference is in the level of abstraction being considered. Data is the lowest level of abstraction, information is the next level, and finally, knowledge is the highest level among all three

 Data on its own carries no meaning. In order for data to become information, it must be interpreted and take on a meaning. For example, the height of Mt. Everest is generally considered as "data", a book on Mt. Everest geological characteristics may be considered as "information", and a report containing practical information on the best way to reach Mt. Everest's peak may be considered as "knowledge".

 Most research projects need data in order to answer a proposed research problem. The data that need to be acquired, and the sources of such data, must be identified as a matter of utmost importance.

 is a broad field of study, but essentially is the process of managing data as a resource that is valuable to an organization or business. Main Topics in Data management  Data Modeling  Data Warehousing  Data Movement  Database Administration  Data Mining

 Data modeling is first creating a structure for the data that you collect and use and then organizing this data in a way that is easily accessible and efficient to store and pull the data for reports and analysis.

 Top-down  Bottom-up:

 Semantic data modeling  Generic data modeling  Data Vault Modeling  Object-Relational Mapping  Object Role Modeling  Relational Model

 It’s technique to define the meaning of data within the context of its interrelationships with other data.

 A data warehouse is a repository of an organization's electronically stored data. Data warehouses are designed to facilitate reporting and analysis

 Provides a common data model for all data of interest regardless of the data's source.  Information in the data warehouse is under the control of data warehouse users so that, even if the source system data is purged over time, the information in the warehouse can be stored safely for extended periods of time.  Because they are separate from operational systems, data warehouses provide retrieval of data without slowing down operational systems.  Data warehouses can work in conjunction with and, hence, enhance the value of operational business applications, notably Customer Relationship Management (CRM) systems.  Data warehouses facilitate decision support system applications such as trend reports

 Data warehouses are not the optimal environment for unstructured data.  latency  High costs.  Data warehouses can get outdated relatively quickly

 Data movement is the ability to move data from one place to another.

 To access information from a database, you need a database management system This is a collection of programs that enables you to enter, organize, and select data in a database.  A DBMS is a set of software program that controls the organization, storage, management, and retrieval of data in a database.

 1960s Navigational DBMS  1970s Relational DBMS  End 1970s SQL DBMS  1980s Object Oriented Databases

 DBMS Engine  Data Definition Subsystem  Data Manipulation Subsystem  Application Generation Subsystem  Data Administration Subsystem

 Relational Database Management Systems  Network Database Management Systems  Hierarchical Database Management Systems

 Database Development  Database Interrogation  Database Maintenance  Application Development

Advantages:  Control of data redundancy, consistency, abstraction, sharing  Improved data integrity, security, enforcement of standards and economy of scale.  Balanced conflicting requirements  Improved data accessibility, responsiveness, maintenance  Increase productivity, concurrency, backup and recovery services.

 Complexity, size, cost of DBMSs  Higher impact of a failure

 Data mining is a process in which large amounts of data are sifted through to show trends, relationships, and patterns.

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