Dr.Anita Seth

INTRODUCTION TO INFORMATION TECHNOLOGY

IS01

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Managing Organizational Data

• Today’s business enterprises cannot survive without quality data about their internal operations and external environment.

• Data can be anything…numbers, image or raw fact.

• Information-when the data is processed and converted into meaningful and useful form.

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Foundation Data Concepts

• Bit- Smallest unit of data; binary digit (0,1)

• Byte- Group of bits that represents a single character.

• Character – single alphabetic, numeric or other symbol

• Field – group of related characters. E.g student’s name etc.

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Foundation Data Concepts

• Record – logical grouping of related fields.

• File – group of related records

• Entity- Person, place, thing, event about which information is maintained

• Attribute- Description of a particular entity

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Foundation Data Concepts

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Foundation Data Concepts

• Key field- Identifier field used to retrieve, update, sort a record

• Primary Key- that uniquely identifies a record so that the record can be retrieved, updated.

• Foreign Key- primary key of one file and appears in another file.

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Data Access Methods• Sequential Access– data records retrieved

in the same physical sequence in which they are stored. e.g. magnetic tape

• Direct Access- records can be retrieved in any sequence. e.g. floppy disk

• Indexed sequential Access-uses the key field to locate physical address of a record.

- employs transform algorithm to translate the key field into record’s storage location on disk

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Types of Data Processing Batch processing Changes to data file accumulated and stored,

processing is done periodically. e.g. generation of student’s mark sheet.

Online processing Transactions are entered directly into

computer and processed immediately.- In real time applications, data is captured and processed. e.g. airline reservation system

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Traditional File Processing

• Data are organized, stored in independent files each organized in a different way.

• Each file was organized to be used by different application program.

• Difficult to get the required information.

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Problems of File Processing• Data Redundancy – independent data files

included lot of duplicated data; duplicated data had to be updated.

• Data inconsistency -various copies of data may not agree.

• Lack of Data Integrity – data values may not be accurate across multiple data files.

• Lack of Data security – new applications may be added to the system on ad-hoc basis and more people access the data.

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Database: Modern approach

• Logically organized collection of similar or related data.

• Serves a base from which the desired information can be retrieved and further processing or reorganizing can be done.

• Eliminates problems associated with traditional file approach.

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Types of Databases

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Types of Databases

• Operational – contain the data to support the business processes and operations of a company. e.g. customer database.

• Centralized database- All the related files in one physical location.- When centralized database computer fails, all users affected.

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Types of Databases

• Distributed – complete copies of database in more than one physical location.- Two types: replicated and partitioned.- Replicated database has complete copy of

entire database in many locations; creates too much overhead.- In Partitioned database, data is subdivided;

data can be entered quickly; widespread access to sensitive company data increases security problems.

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Databases Management System

• A collection of programs that enable to store, modify, and extract information form a database.

• Few examples- computerized library- flight reservation system- computerized inventory system

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Data Abstraction

Process of distilling the data

• Physical view specifies how the data actually stored.

• Logical view describes what relationship exists between the various data.

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Database Structures

• Hierarchical – relationships between records form a hierarchy or treelike structure; Structure characterized by one to many relationship.

• Network – data can be accessed by one of several paths because any data element or record can be related to any number of other data elements- Depicts data logically as many-to-many relationships

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Hierarchical and Network DBMS

Disadvantages

• Time consuming; difficult to install.

• Less flexible compared to RDBMS

• Lack support for ad-hoc and English language-like queries

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Relational Database Structure

• All data elements within the database are viewed as being stored in the form of 2D tables called as relations

• Relates data across tables based on common data element

• Examples: DB2, Oracle, MS SQL Server

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Object-Oriented Database Structure

• Multi-dimensional database structure.

• Can accommodate more complex data types including graphics, pictures, voice and text

• Inheritance – automatically creating new objects by replicating some or all of the characteristics of one or more existing objects

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Evaluation of Database Structures

• Hierarchical data structure is best for structured, routine types of transaction processing.

• Network data structure is best when many-to-many relationships are needed.

• Relational data structure is best when ad hoc reporting is required.

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Database Management Approach

• Consolidates data records into one database that can be accessed by many different application programs.

• Software interface between users and databases

• Data definition is stored once, separately from application programs

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Database Interrogation Capability of a DBMS to report information from the

database in response to end users’ requests

• Query Language – allows easy, immediate access to ad hoc data requests

• Report Generator - allows quick, easy specification of a report format for information users have requested

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Database LanguageTo create or manipulate a database

• Data definition language (DDL) - defines types of information in the database and how they will be structured.- provides the link between logical and physical view of database.- defines physical characteristics of each record, fields within a record, field’s logical name, data type and character length.

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Database Language

• Data manipulation language (DML) - used to query, retrieve, store, update, delete or display the contents of the database- Query languages like SQL (Structured Query Language), an important component of DBMS.- SQL combines both DML and DDL features.- can perform complicated searches with simple statements

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Structured Query Language• Uses keywords like SELECT (specify the desired attribute )

FROM ( specify the table to be used)WHERE (specify conditions to apply)

• Example: To find from university database, all those students graduating with honors and belonging to general category. SQL statement would beSELECT (student name), FROM (student database), WHERE (category=G and Grade point average >=5)

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Data Dictionary

• In relational database, information organized and accessed according to logical structure .

• When relational database created, data dictionary prepared.

• Data dictionary contains logical properties of field values. e.g. Field name Type- alphabetic, numeric etc.

Default value etc.

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On-line Transaction Processing (OLTP)

• Implies gathering information, processing and updating.

• DBMS and databases support OLTP.

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On-line analytical Processing (OLAP)

• Multidimensional data analysis

• Supports manipulation and analysis of large volumes of data from multiple dimensions/perspectives

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On-line analytical processing (OLAP)

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Data Warehouse• Large database that stores data that have

been extracted from the various operational, external, and other databases of an organization

• Supports reporting and query tools

• Stores current and historical data

• Consolidates data for management analysis and decision making

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Meta Data• Data about data• What data is available, what their sources

are; where they are; how to access them?• Technical metadata- where the data come

from; how the data was changed?; how the data is organized? how the data is stored? who owns the data etc.

• Business metadata- what data is available?; how to access the data?; how current the data is?; what the data mean?

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Data Warehouse System

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Data Mart

• Scaled down version of data warehouse and hold subsets of data from a data warehouse.

• Focus on specific aspects of a company, such as a department or a business process.

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Data Warehouse & Data Marts

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Data Mining

• Analyzing the data in a data warehouse to reveal hidden patterns and trends.

• Data mining tools include sophisticated, automated algorithms to identify hidden patterns, correlations and relationships.

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Data Mining

• Predict trends and behavior to make proactive decisions.

• E.g. forecasting bankruptcy; detecting fraudulent credit card transactions; discovering pattern in the retail sales data for the products that are often purchased together

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Data Mining Uses

• Perform “market-basket analysis” to identify new product bundles.

• Find root causes to quality or manufacturing problems.

• Prevent customer attrition and acquire new customers.

• Profile customers with more accuracy

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Database Schema

• Graphical presentation of whole database.

• Database system may have different schemas:- Physical schema describes database design at the physical level. - Logical schema describes database design at the logical level

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Case #1: Data base Business Value

• Successful sellers of books, music other entertainment on internet owe their success to Muze company.

• Muze aggregates and classifies millions of products from thousands of publishers.

• Muze stores this massive amount of information in relational database and license its database at a fraction of what it would cost sellers to compile their own information.

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Case #1: Data Base Business Value

• Information provided by Muze enables retail customers to get in-depth information regarding books, CDs, videotapes without having the product in hand.

• Muze also provides classification data that helps retailer’s search engine to opertae more efficiently.

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Important Considerations• Data warehouse and data mining tools are

expensive. • Organization need to devote considerable time

to create a Data warehouse.• Training to use data minning tools is also

expensive.• Some organizations may not need data

warehouse; necessary information to support decision making from operational databases.

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Summary• Managing organizational data requires IT and

software tools.• The database management approach

consolidates data needed by different applications.

• DBMS are software packages that simplify the creation, use, and maintenance of databases.

• Several types of databases are used by business organizations including operational, distributed, and external databases.

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Summary• Data warehouses are a central source of data

from other databases that have been transformed and cataloged for business analysis and decision support applications.