Teams, Partnerships, and Alliances :
- The core competency of an organization is its key strength, a business function that it does better than any of its competitors.
- A core competency strategy is one in which an organization chooses to focus specifically on what it does best ( its core competency ) and forms partnerships and alliances with other specialist organizations to handle nonstrategic business processes.
- An information partnership occurs when two or more organizations cooperate by integrating their IT systems, thereby providing customers with the best of what each can offer.
Collaboration Systems :
- Is an IT-based set of tools that supports the work of teams by facilitating the sharing and flow of information.
Collaboration systems meet unique business challenges that :
1. include complex interactions between people who may be in different locations and desire to work across function and discipline areas.
2. require flexibility in work process and the ability to involve others quickly and easily.
3. call for creating and sharing information rapidly and effortlessly within a team.
Collaboration systems fall into one of two categories :
1. unstructured collaboration.
2. structured collaboration.
Knowledge Management Systems :
- Knowledge management (KM) - involves capturing, classifying, evaluating, retrieving, and sharing information assets in away that provides context for effective decisions and actions.
- A knowledge management system (KMS) supports the capturing, organization, and dissemination of knowledge throughout an organization.
Explicit and Tacit Knowledge :
- Explicit knowledge consists of anything that can be documented, archived, and codified, often with the help of IT.
- Tacit knowledge is the knowledge contained people's heads.
- Shadowing and joint problem solving are two best practices for transferring or re-creating tacit knowledge inside an organization.
Content Management Systems :
=> Provides tools to manage the creation, storage, editing, and publication of information in a collaborative environment.
Working Wikis :
=> Are web-based tools that make it easy for users to add, remove, and change online content.
Workflow Management Systems :
Workflow - all the steps or business rules, from beginning to end, required for a business process.
Workflow management systems - facilitate the automation and management of business processes and control the movement of work through the business process.
Messaging-based workflow systems - send work assignments through an e-mail system.
Database-based workflow systems - store documents in a central location and automatically ask the team members to access the document when it is their turn to edit the document.
Groupware Systems :
=> Is software that supports team interaction and dynamics including calendaring, scheduling, and videoconferencing.
Groupware systems fall along two primary categories :
1. users of the groupware are working together at the same time or different times.
2. users are working together in the same place or in different places.
Videoconferencing :
=> Is a set of interactive telecommunication technologies that allow two or more locations to interact via two-way video and audio transmissions simultaneously.
Web Conferencing :
=> Blends audio, video, and document-sharing technologies to create virtual meeting rooms where people 'gather' at a password-protected website.
Instant Messaging :
=> Is a type of communications service that enables someone to create a kind of private chat room with another individual in order to communicate in real time over the Internet.
Most of the popular instant messaging programs provide a variety of features, such as :
1. web links.
2. images.
3. sounds.
4. files.
5. talk.
6. streaming content.
7. instant messages.
EBUSINESS MODELS
- An ebusiness model is a plan that details how a company creates, delivers, and generates revenues.
- Ebusiness models fall into one of four categories which is :
Business-to-Business (B2B)
- Applies to businesses buying from and selling to each other over the internet.
- Electronic marketplace (e-marketplace) are interactive business communities providing a central market where multiple buyers and sellers can engage in e-business activities
Business-to-Consumer (B2C)
- Applies to any business that sells its products or services directly to consumers online
- Common B2C e-business models include:
e-shop – a version of a retail store where customers can shop at any hour of the day without leaving their home or office
e-mall – consists of a number of e-shops; it serves as a gateway through which a visitor can access other e-shops
- Business types:
Brick-and-mortar business
Pure-play business
Click-and-mortar business
Consumer-to-Business (C2B)
- Applies to any consumer who sells a product or service to a business on the internet
- Priceline.com is an example of a C2B e-business model
- The demand for C2B e-business will increase over the next few years due to customer’s desire for greater convenience and lower prices
Consumer to consumer (C2C)
- Applies to consumers offering goods and services to each other on the internet.
- C2C communities include:
Communities of interest - People interact with each other on specific topics, such as golfing and stamp collecting
Communities of relations - People come together to share certain life experiences, such as cancer patients, senior citizens, and car enthusiasts
Communities of fantasy - People participate in imaginary environments, such as fantasy football teams and playing one-on-one with Michael Jordan
Ebusiness Tools for Connecting and Communicating
Email
Instant messaging
Podcasting
Videoconferencing
Web conferencing
Content management systems
The Challenges of Ebusiness
Identifying limited market segments
Managing consumer trust
Ensuring consumer protection
Adhering to taxation rules
ENTERPRISE RESOURCE PLANNING (ERP)
- Serve as the organization's backbone in providing fundamental decision-making support.
- At the heart of all ERP systems is a database, when a user enters or updates information in one module, it is immediately and automatically updated throughout the entire system
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Bringing the Organization Together
- ERP enables employees across the organization to share information across a single,centralized database.
The Evolution of ERP
- to deliver automation across multiple units of an organization
- to help facilitate the manufacturing process and address issues such as raw materials, inventory, order entry and distribution
Integrating SCM, CRM, and ERP
- SCM, CRM, and ERP are the backbone of e-business
- Allows the unlocking of information to make it available to any user, anywhere, anytime
- Integration of these applications is the key to success for many companies
Integration Tools
- Many companies purchase modules from an ERP vendor, an SCM vendor, and a CRM vendor and must integrate the different modules together
Middleware – several different types of software which sit in the middle of and provide connectivity between two or more software applications
Enterprise application integration (EAI) middleware – packages together commonly used functionality which reduced the time necessary to develop solutions that integrate applications from multiple vendors
CUSTOMER RELATIONSHIP MANAGEMENT
• CRM enables an organization to:
- Provide better customer service
- Make call centers more efficient
- Cross sell products more effectively
- Help sales staff close deals faster
- Simplify marketing and sales processes
- Discover new customers
- Increase customer revenues
Recency, Frequency, and Monetary Value
• Organizations can find their most valuable customers through “RFM” - Recency, Frequency, and Monetary value
- How recently a customer purchased items (Recency)
- How frequently a customer purchased items (Frequency)
- How much a customer spends on each purchase (Monetary Value)
The Evolution of CRM
• CRM reporting technology – help organizations identify their customers across other applications
• CRM analysis technologies – help organization segment their customers into categories such as best and worst customers
• CRM predicting technologies – help organizations make predictions regarding customer behavior such as which customers are at risk of leaving
Using Analytical CRM to Enhance Decisions
• Operational CRM – supports traditional transactional processing for day-to-day front-office operations or systems that deal directly with the customers
• Analytical CRM – supports back-office operations and strategic analysis and includes all systems that do not deal directly with the customers
Customer Relationship Management Success Factors
• CRM success factors include:
1. Clearly communicate the CRM strategy – ensuring that all departments and employees understand exactly what CRM means and how it will add value to the organization is critical to the success of the implementation
2. Define information needs and flows – the organization must understand all of the different ways that information flows into and out of the organization to implement a successful CRM system. If the organization misses one of the information flows, such as a customer service Web site, then none of that information from that Web site will be integrated into the CRM system and the company will not have a complete view of its customers
3. Build an integrated view of the customer – the CRM system must support the organization's strategies and goalS
4. Implement in iterations – avoid the big-bang approach and implement in small, manageable, pieces
5. Scalability for organizational growth – ensure the system can support the organization's future growth
SUPPLY CHAIN MANAGEMENT
• The average company spends nearly half of every dollar that it earns on production
• In the past, companies focused primarily on manufacturing and quality improvements to influence their supply chains
• The supply chain has three main links:
1. Materials flow from suppliers and their “upstream” suppliers at all levels
2. Transformation of materials into semi-finished and finished products through the organization’s own production process
3. Distribution of products to customers and their “downstream” customers at all levels
• Organizations must embrace technologies that can effectively manage supply chains
• Supply chain management improves ways for companies to find the raw components they need to make a product or service, manufacture that product or service, and deliver it to customers
Plan – This is the strategic portion of supply chain management. A company must have a plan for managing all the resources that go toward meeting customer demand for products or services. A big piece of planning is developing a set of metrics to monitor the supply chain so that it is efficient, costs less, and delivers high quality and value to customers.
Source – Companies must carefully choose reliable suppliers that will deliver goods and services required for making products. Companies must also develop a set of pricing, delivery, and payment processes with suppliers and create metrics for monitoring and improving the relationships.
Make – This is the step where companies manufacture their products or services. This can include scheduling the activities necessary for production, testing, packaging, and preparing for delivery. This is by far the most metric-intensive portion of the supply chain, measuring quality levels, production output, and worker productivity.
Deliver – This step is commonly referred to as logistics. Logistics is the set of processes that plans for and controls the efficient and effective transportation and storage of supplies from suppliers to customers. During this step, companies must be able to receive orders from customers, fulfill the orders via a network of warehouses, pick transportation companies to deliver the products, and implement a billing and invoicing system to facilitate payments.
Return – This is typically the most problematic step in the supply chain. Companies must create a network for receiving defective and excess products and support customers who have problems with delivered products.
Information Technology’s Role in the Supply Chain
• IT’s primary role is to create integration or tight process and information linkages between functions within a firm
Factors Driving SCM
1. Visibility
• more visible models of different ways to do things in the supply chain have emerged. High visibility in the supply chain is changing industries, as Wal-Mart demonstrated
• Supply chain visibility – the ability to view all areas up and down the supply chain
• Bullwhip effect – occurs when distorted product demand information passes from one entity to the next throughout the supply chain
• Supply chain visibility allows organizations to eliminate the bullwhip effect
• To explain the bullwhip effect to your students discuss a product that demand does not change, such as diapers. The need for diapers is constant, it does not increase at Christmas or in the summer, diapers are in demand all year long. The number of newborn babies determines diaper demand, and that number is constant.
• Retailers order diapers from distributors when their inventory level falls below a certain level, they might order a few extra just to be safe
• Distributors order diapers from manufacturers when their inventory level falls below a certain level, they might order a few extra just to be safe
• Manufacturers order diapers from suppliers when their inventory level falls below a certain level, they might order a few extra just to be safe
• Eventually the one or two extra boxes ordered from a few retailers becomes several thousand boxes for the manufacturer. This is the bullwhip effect, a small ripple at one end makes a large wave at the other end of the whip.
2. Consumer behavior
• companies must respond to demanding customers through supply chain enhancements
• Companies can respond faster and more effectively to consumer demands through supply chain enhances
• Demand planning software – generates demand forecasts using statistical tools and forecasting techniques
3. Competition
• increased competition makes any organization that is ignoring its supply chain at risk of becoming obsolete
• Supply chain planning (SCP) software – uses advanced mathematical algorithms to improve the flow and efficiency of the supply chain
• Supply chain execution (SCE) software – automates the different steps and stages of the supply chain
4. Speed
as the pace of business increases through electronic media, an organization's supply chain must respond efficiently, accurately, and quickly
• Three factors fostering speed
Supply Chain Management Success Factors
• SCM industry best practices include:
1. Make the sale to suppliers
2. Wean employees off traditional business practices
3. Ensure the SCM system supports the organizational goals
4. Deploy in incremental phases and measure and communicate success
5. Be future oriented
DECISION MAKING
• Reasons for growth of decision-making information systems
1. People need to analyze large amounts of information—Improvements in technology itself, innovations in communication, and globalization have resulted in a dramatic increase in the alternatives and dimensions people need to consider when making a decision or appraising an opportunity.
2. People must make decisions 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 techniques, such as modeling and forecasting, to make good decisions —Information systems substantially reduce the time required to perform these sophisticated analysis techniques.
4. People must protect the corporate asset of organizational information — Information systems offer the security required to ensure organizational information remains safe.
• Model – a simplified representation or abstraction of reality. Models can calculate risks, understand uncertainty, change variables, and manipulate time.
IT systems in an enterprise
• Decision support system (DSS) – models information to support managers and business professionals during the decision-making process
• Executive information system (EIS) – a specialized DSS that supports senior level executives within the organization
• Artificial intelligence (AI) – simulates human intelligence such as the ability to reason and learn
• Data mining – typically includes many forms of AI such as neural networks and expert systems. Data mining tools apply algorithms to information sets to uncover inherent trends and patterns in the information.
TRANSACTION PROCESSING SYSTEMS
• Moving up through the organizational pyramid users move from requiring transactional information to analytical information
• The structure of a typical organization is similar to a pyramid
• Organizational activities occur at different levels of the pyramid
• People in the organization have unique information needs and thus require various sets of IT tools (see Figure)
• At the lower levels of the pyramid, people perform daily tasks such as processing transactions
• Moving up through the organizational pyramid, people (typically managers) deal less with the details (“finer” information) and more with meaningful aggregations of information (“coarser” information) that help them make broader decisions for the organization
• Granularity refers to the extent of detail in the information (means fine and detailed or “coarse” and abstract information)
• Transaction processing system - the basic business system that serves the operational level (analysts) in an organization
• 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
• Online analytical processing (OLAP) – the manipulation of information to create business intelligence in support of strategic decision making
• Analysts typically use TPS to perform their daily tasks
• What types of TPS are used at your college?
• Payroll system (Tracking hourly employees)
• Accounts Payable system
• Accounts Receivable system
• Course registration system
• Human resources systems (tracking vacation, sick days)
DECISION SUPPORT SYSTEMS
• Decision support system (DSS) – models information to support managers and business professionals during the decision-making process
• In a DSS, data is first queried and collected from the knowledge database
• Results from the query are then checked and analyzed against decision models
• Once checked against the decision models, the results are then generated for review to find a “best” solution for the situation
• One national insurance company uses DSSs to analyze the amount of risk the company is undertaking when it insures drivers who have a history of driving under the influence of alcohol. The DSS discovered that only 3 percent of married male homeowners in their forties received more than one DUI. The company decided to lower rates for customers falling into this category, which increased its revenue while mitigating its risk.
• Three quantitative models used by DSSs include:
Sensitivity analysis – the study of the impact that changes in one (or more) parts of the model have on other parts of the model. Sensitivity analysis – studies the impact on a single change in a current model. For example – if we continually change the amount of inventory we carry, how low can our inventories go before issues start occurring in other parts of the supply chain? This would require changing the inventory level and watching the model to see “how sensitive” it is to inventory levels.
What-if analysis – checks the impact of a change in an assumption on the proposed solution. What-if analysis – determines the impact of change on an assumption or an input. For example – if the economic condition improves, how will it affect our sales?
Goal-seeking analysis – finds the inputs necessary to achieve a goal such as a desired level of output. Goal-seeking analysis – solves for a desired goal. For example – we want to improve revenues by 30 percent, how much does sales have to increase and costs have to decrease to meet this goal?
EXECUTIVE INFORMATION SYSTEMS
• Executive information system (EIS) – a specialized DSS that supports senior level executives within the organization
• 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 details, of information
Slice-and-dice – looks at information from different perspectives
Interaction between a TPS and an EIS
Why would you need interaction between a TPS and EIS?
- The EIS needs information from the TPS to help executives make decisions
- Without knowing order information, inventory information, and shipping information from the TPSs, it would be very difficult for the CEO to make strategic decisions for the organization
• Digital dashboard – integrates information from multiple components and presents it in a unified display. As digital dashboards become easier to use, more executives can perform their own analysis without inundating IT personnel with queries and request for reports
ARTIFICIAL INTELLIGENCE (AI)
• Intelligent system – various commercial applications of artificial intelligence
• Artificial intelligence (AI) – simulates human intelligence such as the ability to reason and learn
• RivalWatch offers a strategic business information service using AI that enables organizations to track the product offerings, pricing policies, and promotions of online competitors
• Clients can determine the competitors they want to watch and the specific information they wish to gather, ranging from products added, removed, or out of stock to price changes, coupons offered, and special shipping terms
• RivalWatch allows its clients to check each competitor, category, and product either daily, weekly, monthly, or quarterly
• The ultimate goal of AI is the ability to build a system that can mimic human intelligence
• Four most common categories of AI include:
1. Expert system
- A computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems. Example robot.
- Human expertise is transferred to the expert system, and users can access the expert system for specific advice
- Most expert systems contain information from many human experts and can therefore perform a better analysis than any single human
2. Neural Network
- attempts to emulate the way the human brain works. Example California police.
- Fuzzy logic – a mathematical method of handling imprecise or subjective information
- Neural networks are most useful for decisions that involve patterns or image recognition
- Typically used in the finance industry to discover credit card fraud by analyzing individual spending behavior
3. Genetic algorithm
- an artificial intelligent system that mimics the evolutionary, survival-of-the-fittest process to generate increasingly better solutions to a problem.
- Example to determine fiber optic by telecommunication
- Essentially an optimizing system, it finds the combination of inputs that give the best outputs
4. Intelligent agent
- special-purposed knowledge-based information system that accomplishes specific tasks on behalf of its users.
- Example Ford Motor Co. Balance with cost and demands.
- Used for environmental scanning and competitive intelligence
- An intelligent agent can learn the types of competitor information users want to track, continuously scan the Web for it, and alert users when a significant event occurs
- RivalWatch uses intelligent agents
DATA MINING
Common forms of data-mining analysis capabilities include:
1. Cluster analysis
• a technique used to divide an information set into mutually exclusive groups such that the members of each group are as close together as possible to one another and the different groups are as far apart as possible
• CRM systems depend on cluster analysis to segment customer information and identify behavioral traits
• Some examples of cluster analysis include:
- Consumer goods by content, brand loyalty or similarity
- Product market typology for tailoring sales strategies
- Retail store layouts and sales performances
- Corporate decision strategies using social preferences
2. Association detection
• reveals the degree to which variables are related and the nature and frequency of these relationships in the information
• Market basket analysis – analyzes such items as Web sites and checkout scanner information to detect customers’ buying behavior and predict future behavior by identifying affinities among customers’ choices of products and services
3. Statistical analysis
• performs such functions as information correlations, distributions, calculations, and variance analysis
• Forecast – predictions made on the basis of time-series information
• Time-series information – time-stamped information collected at a particular frequency
HISTORY OF DATA WAREHOUSING
• Data warehouses extend the transformation of data into information
• In the 1990’s executives became less concerned with the day-to-day business operations and more concerned with overall business functions
• The data warehouse provided the ability to support decision making without disrupting the day-to-day operations
DATA WAREHOUSE FUNDAMENTALS
• Data warehouse – a logical collection of information – gathered from many different operational databases – that supports business analysis activities and decision-making tasks
• The primary purpose of a data warehouse is to aggregate information throughout an organization into a single repository for decision-making purposes
• The primary difference between a database and a data warehouse is that a database stores information for a single application, whereas a data warehouse stores information from multiple databases, or multiple applications, and external information such as industry information
• This enables cross-functional analysis, industry analysis, market analysis, etc., all from a single repository
• Data warehouses support only analytical processing (OLAP)
• 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
• The ETL process gathers data from the internal and external databases and passes it to the data warehouse
• The ETL process also gathers data from the data warehouse and passes it to the data marts
• Data mart – contains a subset of data warehouse information
• The data warehouse modeled in the above figure compiles information from internal databases or transactional/operational databases and external databases through ETL
• It then send subsets of information to the data marts through the ETL process
MULTIDIMENSIONAL ANALYSIS AND DATA MINING
• Databases contain information in a series of two-dimensional tables
• In a data warehouse and data mart, information is multidimensional, it contains layers of columns and rows
– Dimension – a particular attribute of information
• Each layer in a data warehouse or data mart represents information according to an additional dimension
• Dimensions could include such things as:
Products
Promotions
Stores
Category
Region
Stock price
Date
Time
Weather
• Why is the ability to look at information based on different dimensions critical to a business success?
– Ans: The ability to look at information from different dimensions can add tremendous business insight
– By slicing-and-dicing the information a business can uncover great unexpected insights
• Cube – common term for the representation of multidimensional information
• Users can slice and dice the cube to drill down into the information
• Cube A represents store information (the layers), product information (the rows), and promotion information (the columns)
• Cube B represents a slice of information displaying promotion II for all products at all stores
• Cube C represents a slice of information displaying promotion III for product B at store 2
• Data mining – the process of analyzing data to extract information not offered by the raw data alone
• Data mining can begin at a summary information level (coarse granularity) and progress through increasing levels of detail (drilling down), or the reverse (drilling up)
• To perform data mining users need data-mining tools
Data-mining tool – uses a variety of techniques to find patterns and relationships in large volumes of information and infers rules that predict future behavior and guide decision making
Data-mining tools include query tools, reporting tools, multidimensional analysis tools, statistical tools, and intelligent agents
INFORMATION CLEANSING OR SCRUBBING
• An organization must maintain high-quality data in the data warehouse
• Information cleansing or scrubbing – a process that weeds out and fixes or discards inconsistent, incorrect, or incomplete information
• Contact information in an operational system
Taking a look at customer information highlights why information cleansing and scrubbing is necessary
Customer information exists in several operational systems
In each system all details of this customer information could change form the customer ID to contact information
Determining which contact information is accurate and correct for this customer depends on the business process that is being executed
• Standardizing Customer name from Operational Systems
• Information cleansing activities
• Accurate and complete information
• Why do you think most businesses cannot achieve 100% accurate and complete information?
• If they had to choose a percentage for acceptable information what would it be and why?
- Some companies are willing to go as low as 20% complete just to find business intelligence
- Few organizations will go below 50% accurate – the information is useless if it is not accurate
• Achieving perfect information is almost impossible
- The more complete and accurate an organization wants to get its information, the more it costs
- The trade off between perfect information lies in accuracy verses completeness
- Accurate information means it is correct, while complete information means there are no blanks
- Most organizations determine a percentage high enough to make good decisions at a reasonable cost, such as 85% accurate and 65% complete
BUSINESS INTELLIGENCE
BI is information that people use to support their decision-making efforts
Principle BI enablers include:
Technology
• Even the smallest company with BI software can do sophisticated analyses today that were unavailable to the largest organizations a generation ago. The largest companies today can create enterprisewide BI systems that compute and monitor metrics on virtually every variable important for managing the company. How is this possible? The answer is technology—the most significant enabler of business intelligence.
People
• Understanding the role of people in BI allows organizations to systematically create insight and turn these insights into actions. Organizations can improve their decision making by having the right people making the decisions. This usually means a manager who is in the field and close to the customer rather than an analyst rich in data but poor in experience. In recent years “business intelligence for the masses” has been an important trend, and many organizations have made great strides in providing sophisticated yet simple analytical tools and information to a much larger user population than previously possible.
Culture
• A key responsibility of executives is to shape and manage corporate culture. The extent to which the BI attitude flourishes in an organization depends in large part on the organization’s culture. Perhaps the most important step an organization can take to encourage BI is to measure the performance of the organization against a set of key indicators. The actions of publishing what the organization thinks are the most important indicators, measuring these indicators, and analyzing the results to guide improvement display a strong commitment to BI throughout the organization.
RELATIONAL DATABASE FUNDAMENTALS
Information is everywhere in an organization
Information is stored in databases
· Database – maintains information about various types of objects (inventory), events (transactions), people (employees), and places (warehouses)
Database 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
Entity – a person, place, thing, transaction, or event about which information is stored
Attributes (fields, columns) – characteristics or properties of an entity class
Keys and Relationships
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
RELATIONAL DATABASE ADVANTAGES
Database advantages from a business perspective include
· Increased flexibility
· Increased scalability and performance
· Reduced information redundancy
· Increased information integrity (quality)
· Increased information security
Increased Flexibility
A 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
A 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
• One of the primary goals of a database is to eliminate information redundancy by recording each piece of information in only one place
• Databases reduce information redundancy
Redundancy – the duplication of information or storing the same information in multiple places
• Inconsistency is one of the primary problems with redundant information
Increase Information Integrity (Quality)
• Information integrity – measures the quality of information
• Integrity constraint – rules that help ensure the quality of information
Increased Information Security
• Information is an organizational asset and must be protected
• Databases 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
software through which users and application programs interact with a 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.
Data-Driven Web Site Business Advantages
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
Integrating Information among Multiple Databases
• Integration – 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
One of the biggest benefits of integration is that organizations only have to enter information into the systems once and it is automatically sent to all of the other systems throughout the organization
• This feature alone creates huge advantages for organizations because it reduces information redundancy and ensures accuracy and completeness
• Without integration an organization would have to enter information into every single system that requires the information from marketing and sales to billing and customer service
Integrating Information among Multiple Databases
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 integration
• 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
