Data Information Systems and Management Valuing Organizational

Data Information Systems and Management

Valuing Organizational Information • Transactional Information – Contained within a business process – Supports performing daily operations • Analytical Information – Includes transactional information plus market and industry information • The Value of Timely Information – Real Time: Immediate, up-to-date – Within the Decision Makers Time frame

Characteristics of High. Quality Information • • • Accuracy Completeness Consistency Uniqueness Timeliness

The Cost of Low-Quality Information • Using the wrong information can lead to making the wrong decision • The wrong decision can cost time, money, and even reputations

The Benefits Of High-Quality Information • Improve chances of making a good decision which, in turn, may directly affect the organizations bottom line

Data Resource Management Data Planning • Develop an overall data and architecture for the firm’s data resources that ties in with the firm’s strategic mission and plans, and the objectives and processes of it’s business units. Data Administration • Involves the establishment and enforcement of policies and procedures for managing data as a strategic corporate resource.

Cross-Functional Information Systems • Support business processes • Production • Distribution • Order management • Cross boundaries of Traditional business functions. • IT helps by supporting the re-engineering and improvement of business processes. • A strategic way to use IT to share information resources and improve both efficiency and effectiveness of business processes to help a business attain it’s strategic objectives.

Enterprise Resource Planning • Replace functional mainframe legacy systems with cross-functional client/server network applications. • SAP and others

Database Structures • Hierarchical • One-to-many (Tree like) • Network – Many-to-many • Relational – Elements reside in two dimensional interlinked tables • Multidimensional – Cubes of data • Object Oriented – Encapsulation: data and operations are stored together

Entity Relationship Diagram (ERD) • • • Tool Used In Data Modeling Depicts relationships between entities Entity: a category of stored data Relationship: how entities are associated Attributes: descriptive components of an entity • An ERD model can be easily translated into virtually any type of physical data base implementation

Entity Relationship Diagram Customer Order Item

Rules Of Thumb • 1: 1 : One Table • 1: M : primary key from one side used as a foreign key in the many side • M: M : New table with a primary key which is a combination of both the other primary keys.

Rules Of Thumb Bit Byte ≡ Character Field ≡ Record ≡ File • • ≡ Data Element ≡ Data Structure Database Primary Key Secondary Key (or Foreign Key) Referential Integrity Normalization Attributes Entity ≡ ≡ Table Relational Database

Normalization • A method of simplifying complex data structures • A process of assigning attributes to entities • Determine how to traverse a relational database by identifying primary keys and foreign keys

Referential Integrity The Primary key data must exist before data can be entered in the table where the primary key is used as a Foreign key.

Referential Integrity First Normal Form (1 NF) • An entity is in 1 NF if there are no elements, or group of elements, which repeat for a single occurrence of the entity. Second Normal Form (2 NF) • An entity is in 2 NF if it is in 1 NF and if the full key and not part of it derive all non-key elements Third Normal Form (3 NF) • An entity is in 3 NF if it is in 2 NF and if the values for the non-key elements are not dependent on any other non-key elements.

ERD Example Faculty Course Student Department

U of L Database Calendar HR Course Faculty • Course # (K) • Course Name • Course Description • Faculty # (k) • Fac. # (K) • Name • Address • Dept # (k) Phone Book Department • Dept. # (K) • Dept. Name • Dept. Description Organizational Chart Course # Student # Mark Student • Student # (K) • Student Name • Student Address Registration To Grading System Admissions

Organizing Data • Data is processed into information which in turn supports decision making • Database Management System (DBMS) – User/database interface • Database Administrator (DBA) – IT professional responsible for all aspects of the database

Data Management • For data to be turned into information it must first be organized in a meaningful way • Traditional approach – Data redundancy: duplication of data in separate files – Data integrity: the degree to which data is correct • Database approach – A pool of related data is shared by mulitple application programs

Data Modeling • Key Considerations: • What data will be collected • Who will have access to it • How the data will be used • Data Model • A diagram of data entities and their relationships

Data Modeling • Enterprise Data Modeling • Data modeling done at the enterprise level • Entity Relationship Diagram (ERD) • Use basic graphic symbols • Show the organization and relationships between data • Planned Data Redundancy • Summary totals carried in data • To improve system performance • Data Marts in ERP systems

The Relational Database Model • Relational Model: • A database model that describes data in which all data elements are placed in two dimensional tables • The tables are the logical equivalent to files • Domain: Allowable values for data attributes

Data Clean-up • The process of looking for and fixing inconsistencies to ensure that data are accurate and complete

Overview of Database Types • Flat file – Sequential or direct – Does not use database concepts • Single User – One person can use the database at a time (Access) • Multiple Users – Large DBMS (Oracle)

Providing a User View • Schema: • a description of the entire database • Sub schema: • a description of a subset of the database • Users can view and modify data terms in the subset

Creating and Modifying the Database • Data Definition Language (DDL) • Commands used to describe data and their relationships • Data Dictionary • Detailed descriptions of all data in the database

Storing and Retrieving Data • The system must calculate the physical location based upon logical application of data • Concurrency Control • A method of dealing with two people accessing the same record, in the same database, at the same time

Manipulating Data and Generating Reports • Query-by-example (QBE) – Point and click, drag and drop • Data Manipulation Language (DML) – Commands used to manipulate data in a database – Structured Query Language (SQL)

Selecting a Database Management System • Determine information needs of the organization • Considerations • • Size (current and future) Number of Concurrent Users Performance (response time) Integration (relation to other applications) Features (security, privacy, templates) The Vendor (service, reputation, viability) Cost

• Data Warehouse: • A database that collects business information from many sources in the enterprise, covering all aspects of the company’s processes, products, and customers • Data Mart: • Subset of a data warehouse

Data Mining • An information analysis tool that involves the automated discovery of patterns and relationships in a data warehouse • Predictive Analysis • Combines historical data with assumptions about future conditions • Used to predict outcome of events

Business Intelligence • The process of gathering enough of the right information in a timely manner and usable form and analyzing it to have a positive impact on business strategy, tactics, or operations • Competitive Intelligence • Counter Intelligence • Knowledge Management

More Business Intelligence • Competitive Intelligence – One aspect of business intelligence limited to information about competitors • Counter Intelligence – The steps an organization takes to protect information sought by “hostile” intelligence gathers • Knowledge Management – The process of capturing a company’s collective expertise wherever it resides – in computers, on paper, in people’s heads – and distributing it wherever it can help produce bigger payoffs

Distributed Databases • A database in which the data may spread across several smaller databases connected via telecommunication devices • Replicated Database – A database that holds a duplicate set of data

Online Analytical Processing (OLAP) • Software that allows users to explore data from a number or different perspectives

Object-Oriented • Object-Oriented Database • Database that stores both data and its processing instructions together • Encapsulation