Monday, 19 August 2013


Reason for Growth of Decision Making Information System

1. People need to analyze large amounts of information :- improvement in technology itself, innovations in communication, and globalisation have resulted in a dramatic increase in the alternatives and dimension people need to consider when making a decision or appraising an opportunity.

2. People must make decision quickly :- time is of the essence and people simply do not have time to sift through all the information manually.

3. People must apply sophisticated analysis technique such as modelling and forecasting to make good decision :- information system substantially reduce the time required to perform this sophisticated analysis technique.

4. People must protect the corporate asset of organizational information :- information systems offer the security required to ensure organization information remains safe.

Model - a simplified representation or abstraction of reality.
Transaction Processing System

Ø  Moving up through the organizational pyramid users move from requiring transactional information to analytical information

 Ø  Transaction processing system – the basic business system that serves the operational level (analysis) in an organization
Ø  Online transaction processing (OLTP) – the capturing of transaction and event information using technology to (1) process the information according to defined business rules, (2) store the information, (3) update existing information to reflect the new information
Ø  Online analytical processing (OLAP) – the manipulation of information to create business intelligence in support of strategic decision making

Decision support systems
Ø  Decision support system (DSS) – models information to support managers and business professionals during the decision-making process
Ø  Three quantitative models used by DSSs include;
1.       Sensitivity analysis – the study of the impact that changes in one (or more) parts of the model have on other parts of the model
2.       What-if analysis – checks the impact of a change in an assumption on the proposed solution
Goal-seeking analysis – finds the inputs necessary to achieve a goal such as a desired level of outputs

Executive information system
Ø  Executive information system (EIS) – A specialized DSS that supports senior level executives within the organization
Ø  Most EISs offering the following capabilities;
-          Consolidation – involves the aggregation of information and features simple roll-ups to complex groupings of interrelated information
-          Drill-down – enables users to get details, and details of information
-          Slice-and-dice – looks at information from different perspectives
Ø  Interaction between a TPS and an EIS

Ø  Digital dashboard – integrates information from multiple components and presents it in a united display
Artificial intelligence (AI)
Ø  The ultimate goal of AI is the ability to build a system that can mimic human intelligence
Ø  Intelligent system – various commercial applications of artificial intelligence
Ø  Artificial intelligence (AI) – simulates human intelligence such as the ability to reason and learn
Ø  Four most common categories of AI include;
1.       Expert system – computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems
2.       Neural network – attempts to emulate the way the human brain works
o   Fuzzy logic – a mathematical method of handling imprecise or subjective information
3.       Genetic algorithm – an artificial intelligent system that mimics the evolutionary, survival-of-the-fittest process to generate increasingly better solutions to a problem
4.       Intelligent agent – special-purposed knowledge-based information system that accomplishes specific tasks on behalf of its users

Data Mining

Ø  Data-mining software includes many forms of AI such as neutral networks and expert systems 


What is Data Warehouse?
Ø  Defined in many different ways, but not rigorously
-          A decision support database that is maintained separately from the organization’s operational database.
-          A consistent database source that bring together information from multiple sources for decision support queries.
-          Support information processing by providing a solid platform of consolidated, historical data for analysis.
History of Data Warehousing
Ø  In the 1990’s executives became less concerned with the day-to-day business operations and more concerned with overall business functions
Ø  The data warehouse provided the ability to support decision making without disrupting the day-to-day operations, because;
-          Operational information is mainly current – does not include the history for better decision making
-          Issues of quality information
-          Without information history, it is difficult to tell how and why things change over time
Data warehouse fundamentals
Ø  Data warehouse – A logical collection of information – gathered from many different operational databases – that supports business analysis activities and decision-making takes
Ø  The primary purpose of a data warehouse is to combined information throughout an organization into a single repository for decision-making purposes – data warehouse support only analytical processing
Data warehouse model
Ø  Extraction, transformation and loading (ETL) – A process that extracts information from internal and external databases, transforms the information using a common set of enterprise definitions, and loads the information into a data warehouse.
Ø  Data warehouse then send subsets of the information to data mart.

Ø  Data mart – contains a subset of data warehouse information.

Multidimensional Analysis and Data Mining
Ø  Relational Database contains information in a series of two-dimensional tables.
Ø  In a data warehouse and data mart, information is multidimensional, it contains layers of columns and rows
-          Dimension – A particular attribute of information

Ø  Cube – common term for the representation of multidimensional information

Ø  Once a cube of information is created, users can begin to slice and dice the cube to drill down into the information.
Ø  Users can analyze information in a number of different ways and with number of different dimensions.
Ø  Data Mining – the process of analyzing data to extract information not offered by the raw data alone. Also known as “knowledge discovery” – computer-assisted tools and techniques for sifting through and analyzing vast data stores in order to finds trends, patterns and correlations that can guide decision making and increase understanding
Ø  To perform data mining users need data-mining tools
-          Data-mining tool – uses a variety of techniques to finds patterns and relationships in large volumes of information. Eg: retailers and use knowledge of these patterns to improve the placement of items in the layout of a mail-order catalog page or Web page.
Information Cleansing or Scrubbing
Ø  An organization must maintain high-quality data in the data warehouse
Ø  Information cleansing or scrubbing – A process that weeds out and fixes or discards inconsistent, incorrect or incomplete information
Ø  Occurs during ETL process and second on the information once if is in the data warehouse
Ø  Contract information in an operational system
Ø  Standardizing Customer  name from Operational Systems
Ø  Information cleansing activities
-          Missing Records or Attributes
-          Redundant Records
-          Missing Keys or Other Required Data
-          Erroneous Relationships or References
-          Inaccurate Data

Ø  Accurate and complete information

Business Intelligence
Ø  Business Intelligence – refers to applications and technologies that are used to gather, provides access, analyze data and information to support decision making efforts
Ø  These systems will illustrate business intelligence in the areas of customer profiling, customer support, market research, market segmentation, product profitability, statistical analysis, and inventory and distribution analysis to name a few
Ø  Eg; Excel, Access

1.       Describe the roles and purposes of data warehouse and data marts in an organization
2.       Compare the multidimensional nature of data warehouses (and data  marts) with the two-dimensional nature of databases
3.       Identify the information of ensuring the cleanliness of information throughout an organization
4.       Explain the relationship between business intelligence and a data warehouse


 ReLaTiOnAL DaTaBaSe FuNdAmEnTaLs:-

vInformation is everywhere in an organization.
vInformation is stored in databases.
  §Database – maintains information about various types of objects (inventory), events (transactions), people (employees), and places (warehouses).
vDatabase models include: 
  §Hierarchical database model – information is organized into a tree-like structure (using parent/child relationships) in such a way that it cannot have too many relationships. 
  §Network database model – a flexible way of representing objects and their relationships. 
  §Relational database model – stores information in the form of logically related two-dimensional tables.

EnTiTiEs aNd AttRiBuTeS:- 

vEntity – a person, place, thing, transaction, or event about which information is stored. 
     §The rows in each table contain the entities. 
     §In Figure 7.1 CUSTOMER includes Dave’s Sub Shop and Pizza Palace entities. 
vAttributes (fields, columns) – characteristics or properties of an entity class. 
     §The columns in each table contain the attributes. 
     §In Figure 7.1 attributes for CUSTOMER include Customer ID, Customer Name, Contact Name.

KeYs aNd ReLaTiOnShiPs:- 
vPrimary keys and foreign keys identify the various entity classes (tables) in the database. 
   §Primary key – a field (or group of fields) that uniquely identifies a given entity in a table. 
   §Foreign key – a primary key of one table that appears an attribute in another table and acts to provide a logical relationship among the two tables. 
vPotential relational database for Coca-Cola:

Walk your students through the relational database model in Figure 7.1
To ensure your students are grasping the concepts, ask them to answer the following:
How many orders have been placed for T’s Fun Zone? 
Answer: 1 Order IT 34563.
How many orders have been placed for Pizza Palace? 
Answer:  None.
How many items are included in Dave’s Sub Shop’s two orders? 
Answer:  Order 34561 has 3 items and order 34562 has one item for a total of 4 items in both orders.
Who is responsible for distributing Dave’s Sub Shop’s orders? 
Answer:  Hawkins Shipping.
Which products are included in Order 34562? 
Answer:  300 Vanilla Coke. 

ReLaTiOnAL DaTaBaSe AdVaNtaGeS:- 

vDatabase advantages from a business perspective include: 

   §Increased flexibility. 
   §Increased scalability and performance. 
   §Reduced information redundancy. 
   §Increased information integrity (quality). 
   §Increased information security. 

InCrEaSeD FLeXiBiLiTy:- 

vA well-designed database should: 
  §Handle changes quickly and easily. 
  §Provide users with different views. 
  §Have only one physical view. 
                •Physical view – deals with the physical storage of information on a storage device. 
  §Have multiple logical views. 
                •Logical view – focuses on how users logically access information. 

InCreAsEd ScALaBiLiTy aNd PeRfOrMaNcE:- 

vA database must scale to meet increased demand,  while maintaining acceptable performance levels. 
   §Scalability – refers to how well a system can adapt to increased demands. 
   §Performance – measures how quickly a system performs a certain process or transaction.

ReDuCed InFoRmAtiOn ReDuNdAnCy:- 

vDatabases reduce information redundancy. 
    §Redundancy – the duplication of information or storing the same information in multiple places. 

vInconsistency is one of the primary problems with redundant information.

InCrEaSe InFoRmAtiOn InTeGriTy (QuAliTy):- 

vInformation integrity – measures the quality of information. 

vIntegrity constraint – rules that help ensure the quality of information. 
   §Relational integrity constraint - rule that enforces basic and fundamental information-based constraints.
   §Business-critical integrity constraint - rule that enforce business rules vital to an organization’s success and often require more insight and knowledge than relational integrity constraints.

InCreAsEd InFoRmAtiOn SeCuRiTy:- 

vInformation is an organizational asset and must be protected. 

vDatabases offer several security features including: 
   §Password – provides authentication of the user. 
   §Access level – determines who has access to the different types of information. 
   §Access control – determines types of user access, such as read-only access.

Database Management Systems:- 
vDatabase management systems (DBMS) – software through which users and application programs interact with a database.

Direct interaction :
>The user interacts directly with the DBMS.
>The DBMS obtains the information from the database.

Indirect interaction:
>User interacts with an application (i.e., payroll application, manufacturing application, sales application).
>The application interacts with the DBMS.
>The DBMS obtains the information from the database.

DaTa-DrIvEn WeB SiTeS:-

>A data-driven Web site is an interactive Web Site kept constantly updated and relevant to the needs of its customers through the use of a database. Data-driven Web sites are especially useful when the site offers a great deal of information, products, or services. Web site visitors are frequently angered if they are buried under an avalanche of information when searching a Web site. A data-driven Web site invites visitors to select and view what they are interested in by inserting a query, which the Web site then analyzes and custom builds a Web page in real-time that satisfies the query. The figure displays a Wikipedia user querying business intelligence and the database sending back the appropriate Web page that satisfies the user’s request.

>What would happen to a Web site that is not data-driven? 
    Answer:The users would need to continually update the Web site data manually as the business data is updated.  This would be a redundant effort and most likely result in errors and the Web site could quickly become out of sync with the business data.

DaTa DriVeN WeB SiTe AdVaNtaGeS:-

1. Development: Allows the Web site owner to make changes any time—all without having to rely on a developer or knowing HTML programming. A well-structured, data-driven Web site enables updating with little or no training.

2. Content management: A static Web site requires a programmer to make updates. This adds an unnecessary layer between the business and its Web content, which can lead to misunderstandings and slow turnarounds for desired changes.

3. Future expandability: Having a data-driven Web site enables the site to grow faster than would be possible with a static site.  Changing the layout, displays, and functionality of the site (adding more features and sections) is easier with a data-driven solution.

4. Minimizing human error: Even the most competent programmer charged with the task of maintaining many pages will overlook things and make mistakes. This will lead to bugs and inconsistencies that can be time consuming and expensive to track down and fix. Unfortunately, users who come across these bugs will likely become irritated and may leave the site. A well-designed, data-driven Web site will have ”error trapping” mechanisms to ensure that required information is filled out correctly and that content is entered and displayed in its correct format.

5.Cutting production and update costs: A data-driven Web site can be updated and ”published” by any competent data entry or administrative person. In addition to being convenient and more affordable, changes and updates will take a fraction of the time that they would with a static site. While training a competent programmer can take months or even years, training a data entry person can be done in 30 to 60 minutes.

6.More efficient: By their very nature, computers are excellent at keeping volumes of information intact. With a data-driven solution, the system keeps track of the templates, so users do not have to. Global changes to layout, navigation, or site structure would need to be programmed only once, in one place, and the site itself will take care of propagating those changes to the appropriate pages and areas. A data-driven infrastructure will improve the reliability and stability of a Web site, while greatly reducing the chance of ”breaking” some part of the site when adding new areas.

7. Improved Stability: Any programmer who has to update a Web site from ”static” templates must be very organized to keep track of all the source files. If a programmer leaves unexpectedly, it could involve re-creating existing work if those source files cannot be found. Plus, if there were any changes to the templates, the new programmer must be careful to use only the latest version. With a data-driven Web site, there is peace of mind, knowing the content is never lost—even if your programmer is.

DaTa-DriVeN BuSinEsS InTelliGeNcE:-

vBusiness Intelligence in a data-driven Web site:

>Companies can gain business intelligence by viewing the data accessed and analyzed from their Web site.  The figure displays how running queries or using analytical tools, such as a Pivot Table, on the database that is attached to the Web site can offer insight into the business, such as items browsed, frequent requests, items bought together, etc.

InTeGraTiNg InFoRmAtiOn
AmOnG MuLtiPlE DaTabAsEs:- 

vIntegration – allows separate systems to communicate directly with each other.

  §Forward integration – takes information entered into a given system and sends it automatically to all downstream systems and processes.
  §Backward integration – takes information entered into a given system and sends it automatically to all upstream systems and processes.

Forward Integration:

>Basically, all information flows forward along the business process. Sales enters the information when it is negotiating the sale (looking for opportunities). The information is then passed to the order entry system when the order is actually placed. The order fulfillment system picks the products from the warehouse, packs the products, labels boxes, etc. Once the order is filled and shipped, the customer is billed.

What would happen if users could enter order information directly into the billing system?
The systems would quickly become out-of-sync.  There might be bills for nonexistent orders, or orders that do not have any bills (if someone deleted a bill). For this reason organizations typically place a business-critical integrity constraint on integrated systems:  With a forward integration the information must be entered in the sales system, you could not enter information directly into the billing system.

>Integrations are expensive to build and maintain and difficult to implement. For these reasons many organizations only build forward integrations and use business-critical integrity constraints to ensure all information is always entered only at the start of the integration (one source of record).

Backward Integration:

>Basically, all information flows backward along the business process. Billing enters information and this information is passed back to the order system. The order fulfillment system passes the information back to the order entry system. The order entry system passes the information back to the sales system.

Why would an organization want to build both forward and backward integrations?
This allows users to enter information at any point in the business process and the information is automatically sent upstream and downstream to all other systems. For example, if order fulfillment determined that they could not fulfill an order (the product had been discontinued), they could simply enter this information into the database and it would be sent automatically upstream to the sales representative who could contact the customer and downstream to billing to remove the item from the bill.

Building a central repository specifically for integrated information:-

The above figure displays an example of customer information integrated using this method. Users can create, read, update, and delete in the main customer repository, and it is automatically sent to all of the other databases. This method does not follow the business process when building the integrations. Business-critical integrity constraints still need to be built to ensure information is only ever entered into the customer repository, otherwise the information will become out-of-sync.
 cision faster.