The document describes different types of information systems, including Expert Systems, Decision Support Systems, Executive Information Systems, Management Information Systems, and Transaction Processing Systems. It provides details on each system's focus, inputs, processes, outputs, and typical users. Key dimensions for developing a Decision Support System include the user interface, model management, data management, and types of models used.
The document discusses different types of information systems:
- Expert systems focus on knowledge from experts to provide answers to questions.
- Decision support systems use a number of analytical models and tools to interactively simulate answers to questions for senior managers.
- Executive information systems provide graphical and interactive projections to answer questions for senior managers.
- Management information systems provide structured information through reports and analysis for staff experts or managers.
- Transaction processing systems sort and combine transactional data to provide detailed reports and summaries for operational personnel and supervisors.
Success or failure of information system implementationbamaki
The document discusses factors that can lead to success or failure when implementing an information system. It provides definitions of different types of information systems such as transaction processing systems, management information systems, decision support systems, and executive support systems. The document then lists some common reasons for information system implementation failures such as lack of knowledge, difficulties with technology, low quality business process reengineering, and lack of management support. Finally, it notes some benefits of successful information system implementations, including operational efficiencies, cost reductions, improved decision-making, better customer service, and growth in communication capabilities.
The document discusses business intelligence systems (BIS). It defines BIS as using applications and technologies to collect, store, analyze and provide access to information to improve business processes and decision making. BIS benefits businesses by improving management and operations, enabling fraud detection and predicting the future. It is created using procured data and information, and combines skills, processes, technologies and practices to provide business insights for better decisions. The purpose of a BIS is to help executives and managers make more informed decisions. It facilitates the decision making process and efficient communication within a business.
The document discusses different types of information systems used in organizations:
1. Expert systems use knowledge from experts to provide answers to questions.
2. Decision support systems provide interactive support to help with decision making by analyzing multiple data and models.
3. Executive information systems provide structured information to senior executives.
It also discusses key concepts in developing decision support systems like the iterative design process and components such as the user interface, data management, and model management.
This document provides an overview of an Information Systems Analysis and Design (ISAD) course. It includes information about lecture times and locations, required textbooks, course objectives which are to teach concepts of systems analysis and design. It also describes chapters that will be covered including basic IS concepts, the system development life cycle, systems theory, different types of information systems and how system development differs based on the type of system.
1. System Analyst Work as A
2. Qualities of the system Analyst
3. System Development Life Cycle
4. Identifying Problems, Opportunities and objectives
5. Determining Human Information Requirements
6. Analyzing System Needs
7. Designing the recommended System
8. Testing and Maintaining the system
9. Implementing and Evaluating
The document discusses several myths about data mining. It summarizes that data mining is not instant predictions from a crystal ball, but rather a multi-step process requiring clean data. It also notes that data mining is a viable technology for businesses that can provide insights regardless of company size or amount of customer data. Advanced algorithms are not the only important aspect of data mining, as business knowledge is also essential.
Data refers to raw facts and figures that have no inherent meaning. Information is data that has been organized and processed to give it context and meaning. There are many types of data including alphanumeric, text, images, audio. Information systems collect data, process it into information, and disseminate the results. Key components of information systems are hardware, software, databases, people, procedures, and telecommunications. Information systems support various levels of an organization from transaction processing to decision making.
The document discusses different types of information systems:
- Expert systems focus on knowledge from experts to provide answers to questions.
- Decision support systems use a number of analytical models and tools to interactively simulate answers to questions for senior managers.
- Executive information systems provide graphical and interactive projections to answer questions for senior managers.
- Management information systems provide structured information through reports and analysis for staff experts or managers.
- Transaction processing systems sort and combine transactional data to provide detailed reports and summaries for operational personnel and supervisors.
Success or failure of information system implementationbamaki
The document discusses factors that can lead to success or failure when implementing an information system. It provides definitions of different types of information systems such as transaction processing systems, management information systems, decision support systems, and executive support systems. The document then lists some common reasons for information system implementation failures such as lack of knowledge, difficulties with technology, low quality business process reengineering, and lack of management support. Finally, it notes some benefits of successful information system implementations, including operational efficiencies, cost reductions, improved decision-making, better customer service, and growth in communication capabilities.
The document discusses business intelligence systems (BIS). It defines BIS as using applications and technologies to collect, store, analyze and provide access to information to improve business processes and decision making. BIS benefits businesses by improving management and operations, enabling fraud detection and predicting the future. It is created using procured data and information, and combines skills, processes, technologies and practices to provide business insights for better decisions. The purpose of a BIS is to help executives and managers make more informed decisions. It facilitates the decision making process and efficient communication within a business.
The document discusses different types of information systems used in organizations:
1. Expert systems use knowledge from experts to provide answers to questions.
2. Decision support systems provide interactive support to help with decision making by analyzing multiple data and models.
3. Executive information systems provide structured information to senior executives.
It also discusses key concepts in developing decision support systems like the iterative design process and components such as the user interface, data management, and model management.
This document provides an overview of an Information Systems Analysis and Design (ISAD) course. It includes information about lecture times and locations, required textbooks, course objectives which are to teach concepts of systems analysis and design. It also describes chapters that will be covered including basic IS concepts, the system development life cycle, systems theory, different types of information systems and how system development differs based on the type of system.
1. System Analyst Work as A
2. Qualities of the system Analyst
3. System Development Life Cycle
4. Identifying Problems, Opportunities and objectives
5. Determining Human Information Requirements
6. Analyzing System Needs
7. Designing the recommended System
8. Testing and Maintaining the system
9. Implementing and Evaluating
The document discusses several myths about data mining. It summarizes that data mining is not instant predictions from a crystal ball, but rather a multi-step process requiring clean data. It also notes that data mining is a viable technology for businesses that can provide insights regardless of company size or amount of customer data. Advanced algorithms are not the only important aspect of data mining, as business knowledge is also essential.
Data refers to raw facts and figures that have no inherent meaning. Information is data that has been organized and processed to give it context and meaning. There are many types of data including alphanumeric, text, images, audio. Information systems collect data, process it into information, and disseminate the results. Key components of information systems are hardware, software, databases, people, procedures, and telecommunications. Information systems support various levels of an organization from transaction processing to decision making.
The document discusses different types of information systems including office information systems, transaction processing systems, management information systems, decision support systems, expert systems, and integrated information systems. It describes the key characteristics and functions of each type of system, such as capturing and processing transactional data, generating reports to help managers make decisions, and using artificial intelligence to emulate human expertise. The document also discusses best practices for ensuring information is relevant and timely.
The document discusses decision support systems and expert systems for e-banking in India. It describes how banks have progressed from the information age by collecting transactional data to the knowledge age by storing data in warehouses. It states that banks can now use this stored knowledge and intelligence to drive profits and differentiate services. The document proposes that an expert decision support system solution can help banks consolidate vast data volumes to target the right customer segments for the right banking schemes, helping reduce costs.
This document discusses decision support systems (DSS) and online analytical processing (OLAP). It defines DSS as interactive computer systems that help managers make decisions, using tools like analytical models, databases, and modeling processes. OLAP enables examining and manipulating large amounts of consolidated data from different perspectives. Both DSS and OLAP support analysis of operational data, markets, sales, and customers to help with decisions around pricing, forecasting, and risk.
This document summarizes the sub-system linkages within an organization's administration (SEWA) including its human resources, information technology, finance, billing, diagnosis, labs, and treatment departments. It also introduces the implementation of an upgraded CT scan machine and describes the roles of the top (DSS), middle (MIS), and lower (TPS) levels of management in gathering information, making decisions, and operationalizing the new machine.
This document defines and provides examples of different types of information systems:
Transaction Processing Systems (TPS) process user requests by collecting, modifying, and retrieving data and are used in banks, credit cards, and airlines. Office Automation Systems (OAS) integrate software to manage information electronically and are used in offices. Knowledge Work Systems (KWS) support knowledge workers by organizing work information, and are used by companies to improve standards. Decision Support Systems (DSS) improve business decisions by gathering relevant information, and are used by companies compiling data to solve problems. Executive Support Systems (ESS) summarize data into reports and can predict future performance, for use by executive managers. Group Decision Support Systems (GDSS)
Decision support systems use analytical models, specialized databases, and interactive computer-based modeling to support semi-structured business decisions. They provide information and techniques to analyze specific problems in a flexible format. Common components of decision support systems include a model base containing analytical models and a knowledge base for expert systems. Popular applications of decision support include data mining, forecasting, optimization, and knowledge discovery.
This document discusses different types of management information systems and decision support systems. It describes management information systems as providing managers with information to support decision making and monitor daily operations. Decision support systems are organized collections of tools used to support problem-specific decision making. Group support systems add collaboration functionality, while executive support systems are tailored specifically for senior executives. Key types of systems include management information systems, decision support systems, group support systems, and executive support systems.
A Decision Support System is a computer-based information system that supports business or organizational decision-making activities.
A DSS is a collection of integrated software applications and hardware that form the backbone of an organization’s decision making process and help to make decisions, which may be rapidly changing and not easily specified in advance.
Management information system (MIS) is
Integrated collection of people, procedures, databases, and devices Provides managers and decision makers with information to help achieve organizational goals.
The document discusses decision support systems (DSS), business intelligence (BI), and an Automated Intelligent Decision Support System (AIDSS) for human resource management. It defines these concepts and outlines some of their key features. Specifically, it explains that DSS help decision-making, BI aims to support better business decisions using tools like reporting and analytics, and AIDSS is a specific DSS that uses AI to help with HR challenges like employee performance. It also provides details on the development of AIDSS, including its modules for the user interface, data input/editing, intelligent analysis, and data storage/protection.
A Transaction Processing System (TPS) is used to organize and support large volumes of daily business transactions. It provides functions for data integrity, system administration, and custom applications. TPS are commonly used in accounting, billing, payroll processing, ticket reservations, and ATM machines. An Office Automation System (OAS) digitizes common office tasks like document storage, messaging, and group work using a local area network. Knowledge Work Systems (KWS) help professionals in fields like engineering and law organize their work using collaboration software. Decision Support Systems (DSS) provide managers with data and models to aid strategic planning, forecasting, resource allocation, and budgeting. Executive Support Systems (ESS) give executives quick access to
Decision support systems (DSS) are a class of computerized systems that help organizational decision-making. A DSS compiles useful information from data, documents, and business models to help decision-makers identify and solve problems. It has three key functions: capturing past information, data processing, and data retrieval. A DSS has three core components - a database management system, model-based management system, and dialog generation/management system. There are different types of DSS that aid decision-making through various methods like data, models, knowledge, or documents.
The document discusses various concepts related to database design and data warehousing. It describes how DBMS minimize problems like data redundancy, isolation, and inconsistency through techniques like normalization, indexing, and using data dictionaries. It then discusses data warehousing concepts like the need for data warehouses, their key characteristics of being subject-oriented, integrated, and time-variant. Common data warehouse architectures and components like the ETL process, OLAP, and decision support systems are also summarized.
Decision support systems and business intelligenceShwetabh Jaiswal
The document discusses decision support systems and business intelligence. It describes how business environments have become more complex, requiring faster and better decision-making supported by computerized systems. Business intelligence involves transforming raw data into useful information to enable strategic, tactical and operational insights. Decision support systems couple individual expertise with computer capabilities to improve decision quality for semi-structured problems.
There are seven basic types of decision support systems (DSS): file drawer systems, data analysis systems, analysis information systems, accounting models, representational models, optimization systems, and suggestion systems. DSS can also be categorized based on the support they provide (data-based or model-based), the nature of the decision situation (institutional or ad hoc), or the number of users (individual, multi-individual, or group). Different types of DSS fit at various steps in Simon's model of decision making, from problem identification to choice.
A Decision Support System (DSS) is an interactive computer system intended to help decision makers solve problems and make decisions using data, documents, knowledge, and models. A DSS for tax administration can help analyze tax data to identify non-compliance, forecast revenues, and evaluate policies and programs. It integrates data from various tax applications into a data warehouse that supports online analytical processing, data mining, and reporting to assist with decisions.
A Decision Support System (DSS) is a computer-based information system that supports business or organizational decision-making. It can help managers at different levels of an organization make decisions by providing tools to access and analyze data. A DSS combines data, documents, knowledge, and models to help identify and solve problems. It has evolved over time from focusing on data and models to also include knowledge and documents. A DSS is made up of a database, models, and a user interface. It aims to support semi-structured and unstructured decision-making.
This document discusses different types of information systems used for decision making. It describes Transaction Processing Systems (TPS) which handle basic operational tasks, Management Information Systems (MIS) which provide summary reports for middle managers, and Decision Support Systems (DSS) which use interactive models and analysis for professionals and managers. Executive Support Systems (ESS) are specialized DSS designed for senior managers, providing projections and responses to strategic queries. The document provides examples and compares key characteristics of MIS and DSS.
A decision support system (DSS) is a computer-based information system that supports business or organizational decision-making activities. DSSs serve the management
The document provides an overview of management information systems (MIS). It defines key concepts such as data, information, and systems. It explains that an MIS is a system for collecting, processing, storing, and distributing data to managers within an organization. The main outputs of an MIS are scheduled reports, key indicator reports, demand reports, and exception reports. These help managers monitor performance and make decisions. Overall, the document serves as an introduction to MIS, covering essential elements like the relationship between data, information, and systems.
This document discusses different types of information systems used in business. It describes Office Automation Systems, Transaction Processing Systems, Management Information Systems, Decision Support Systems, and Executive Support Systems. It provides details on the purpose and components of each system type to support different levels of management in a business.
The document discusses different types of information systems including office information systems, transaction processing systems, management information systems, decision support systems, expert systems, and integrated information systems. It describes the key characteristics and functions of each type of system, such as capturing and processing transactional data, generating reports to help managers make decisions, and using artificial intelligence to emulate human expertise. The document also discusses best practices for ensuring information is relevant and timely.
The document discusses decision support systems and expert systems for e-banking in India. It describes how banks have progressed from the information age by collecting transactional data to the knowledge age by storing data in warehouses. It states that banks can now use this stored knowledge and intelligence to drive profits and differentiate services. The document proposes that an expert decision support system solution can help banks consolidate vast data volumes to target the right customer segments for the right banking schemes, helping reduce costs.
This document discusses decision support systems (DSS) and online analytical processing (OLAP). It defines DSS as interactive computer systems that help managers make decisions, using tools like analytical models, databases, and modeling processes. OLAP enables examining and manipulating large amounts of consolidated data from different perspectives. Both DSS and OLAP support analysis of operational data, markets, sales, and customers to help with decisions around pricing, forecasting, and risk.
This document summarizes the sub-system linkages within an organization's administration (SEWA) including its human resources, information technology, finance, billing, diagnosis, labs, and treatment departments. It also introduces the implementation of an upgraded CT scan machine and describes the roles of the top (DSS), middle (MIS), and lower (TPS) levels of management in gathering information, making decisions, and operationalizing the new machine.
This document defines and provides examples of different types of information systems:
Transaction Processing Systems (TPS) process user requests by collecting, modifying, and retrieving data and are used in banks, credit cards, and airlines. Office Automation Systems (OAS) integrate software to manage information electronically and are used in offices. Knowledge Work Systems (KWS) support knowledge workers by organizing work information, and are used by companies to improve standards. Decision Support Systems (DSS) improve business decisions by gathering relevant information, and are used by companies compiling data to solve problems. Executive Support Systems (ESS) summarize data into reports and can predict future performance, for use by executive managers. Group Decision Support Systems (GDSS)
Decision support systems use analytical models, specialized databases, and interactive computer-based modeling to support semi-structured business decisions. They provide information and techniques to analyze specific problems in a flexible format. Common components of decision support systems include a model base containing analytical models and a knowledge base for expert systems. Popular applications of decision support include data mining, forecasting, optimization, and knowledge discovery.
This document discusses different types of management information systems and decision support systems. It describes management information systems as providing managers with information to support decision making and monitor daily operations. Decision support systems are organized collections of tools used to support problem-specific decision making. Group support systems add collaboration functionality, while executive support systems are tailored specifically for senior executives. Key types of systems include management information systems, decision support systems, group support systems, and executive support systems.
A Decision Support System is a computer-based information system that supports business or organizational decision-making activities.
A DSS is a collection of integrated software applications and hardware that form the backbone of an organization’s decision making process and help to make decisions, which may be rapidly changing and not easily specified in advance.
Management information system (MIS) is
Integrated collection of people, procedures, databases, and devices Provides managers and decision makers with information to help achieve organizational goals.
The document discusses decision support systems (DSS), business intelligence (BI), and an Automated Intelligent Decision Support System (AIDSS) for human resource management. It defines these concepts and outlines some of their key features. Specifically, it explains that DSS help decision-making, BI aims to support better business decisions using tools like reporting and analytics, and AIDSS is a specific DSS that uses AI to help with HR challenges like employee performance. It also provides details on the development of AIDSS, including its modules for the user interface, data input/editing, intelligent analysis, and data storage/protection.
A Transaction Processing System (TPS) is used to organize and support large volumes of daily business transactions. It provides functions for data integrity, system administration, and custom applications. TPS are commonly used in accounting, billing, payroll processing, ticket reservations, and ATM machines. An Office Automation System (OAS) digitizes common office tasks like document storage, messaging, and group work using a local area network. Knowledge Work Systems (KWS) help professionals in fields like engineering and law organize their work using collaboration software. Decision Support Systems (DSS) provide managers with data and models to aid strategic planning, forecasting, resource allocation, and budgeting. Executive Support Systems (ESS) give executives quick access to
Decision support systems (DSS) are a class of computerized systems that help organizational decision-making. A DSS compiles useful information from data, documents, and business models to help decision-makers identify and solve problems. It has three key functions: capturing past information, data processing, and data retrieval. A DSS has three core components - a database management system, model-based management system, and dialog generation/management system. There are different types of DSS that aid decision-making through various methods like data, models, knowledge, or documents.
The document discusses various concepts related to database design and data warehousing. It describes how DBMS minimize problems like data redundancy, isolation, and inconsistency through techniques like normalization, indexing, and using data dictionaries. It then discusses data warehousing concepts like the need for data warehouses, their key characteristics of being subject-oriented, integrated, and time-variant. Common data warehouse architectures and components like the ETL process, OLAP, and decision support systems are also summarized.
Decision support systems and business intelligenceShwetabh Jaiswal
The document discusses decision support systems and business intelligence. It describes how business environments have become more complex, requiring faster and better decision-making supported by computerized systems. Business intelligence involves transforming raw data into useful information to enable strategic, tactical and operational insights. Decision support systems couple individual expertise with computer capabilities to improve decision quality for semi-structured problems.
There are seven basic types of decision support systems (DSS): file drawer systems, data analysis systems, analysis information systems, accounting models, representational models, optimization systems, and suggestion systems. DSS can also be categorized based on the support they provide (data-based or model-based), the nature of the decision situation (institutional or ad hoc), or the number of users (individual, multi-individual, or group). Different types of DSS fit at various steps in Simon's model of decision making, from problem identification to choice.
A Decision Support System (DSS) is an interactive computer system intended to help decision makers solve problems and make decisions using data, documents, knowledge, and models. A DSS for tax administration can help analyze tax data to identify non-compliance, forecast revenues, and evaluate policies and programs. It integrates data from various tax applications into a data warehouse that supports online analytical processing, data mining, and reporting to assist with decisions.
A Decision Support System (DSS) is a computer-based information system that supports business or organizational decision-making. It can help managers at different levels of an organization make decisions by providing tools to access and analyze data. A DSS combines data, documents, knowledge, and models to help identify and solve problems. It has evolved over time from focusing on data and models to also include knowledge and documents. A DSS is made up of a database, models, and a user interface. It aims to support semi-structured and unstructured decision-making.
This document discusses different types of information systems used for decision making. It describes Transaction Processing Systems (TPS) which handle basic operational tasks, Management Information Systems (MIS) which provide summary reports for middle managers, and Decision Support Systems (DSS) which use interactive models and analysis for professionals and managers. Executive Support Systems (ESS) are specialized DSS designed for senior managers, providing projections and responses to strategic queries. The document provides examples and compares key characteristics of MIS and DSS.
A decision support system (DSS) is a computer-based information system that supports business or organizational decision-making activities. DSSs serve the management
The document provides an overview of management information systems (MIS). It defines key concepts such as data, information, and systems. It explains that an MIS is a system for collecting, processing, storing, and distributing data to managers within an organization. The main outputs of an MIS are scheduled reports, key indicator reports, demand reports, and exception reports. These help managers monitor performance and make decisions. Overall, the document serves as an introduction to MIS, covering essential elements like the relationship between data, information, and systems.
This document discusses different types of information systems used in business. It describes Office Automation Systems, Transaction Processing Systems, Management Information Systems, Decision Support Systems, and Executive Support Systems. It provides details on the purpose and components of each system type to support different levels of management in a business.
Here are a few key points on both sides of this issue:
Pros of computer-based transactions replacing person-to-person contact:
- Increased convenience - customers can complete transactions anytime from anywhere.
- Faster transactions - computers can process transactions more quickly than humans.
- Lower costs - automated systems are generally cheaper to operate than human employees.
Cons of losing person-to-person contact:
- Reduced human interaction/customer service - personal touch is lost without a human element.
- Technology issues - systems can fail or have errors, unlike humans. Reliance on tech is a risk.
- Accessibility issues - some customers prefer or need in-person support that computers cannot provide
This document defines key concepts related to information systems. It discusses what an information system is, how it differs from a manual system, and key components like input, processing, output and feedback. It also covers different types of information systems such as functional vs integrated systems and knowledge-based systems like expert systems, decision support systems, and executive information systems.
The document discusses data, information, and systems. It defines data as facts without context, while information is data that has been analyzed and given meaning and context. An information system takes raw data as input, processes it, and produces information as output. The document also describes the four stages of data processing as input, processing, output, and feedback. Finally, it discusses different types of information systems from operational to strategic levels.
The document provides an overview of management information systems (MIS). It defines MIS as a system for collecting, processing, storing, and disseminating data to support the information needs of management for decision making. The document discusses the components and types of information systems, outputs of MIS including scheduled reports and exception reports, and the impact of MIS in streamlining operations and monitoring performance. It also outlines considerations for MIS planning, development, and design.
This document discusses management information systems (MIS) and their role in organizations. It begins by defining MIS as a system that provides managers with information to help with decision making, planning, and control. It then discusses different types of information systems at various levels, including operational, knowledge, management, and strategic levels. Transaction processing systems, management information systems, decision support systems, and executive information systems are described. The document also discusses digital firms and how they leverage various applications and technologies to digitally enable core business functions.
Information systems collect, process, store, and distribute data as information. They consist of hardware, software, data, people, and procedures. Information systems are used in various fields like education, business, and management to track student grades, facilitate online learning, carry out online transactions, and analyze products. The main components of an information system are data, hardware, software, people, and procedures. Common types of information systems include transaction processing systems, management information systems, decision support systems, executive information systems, and expert systems.
The document provides information on various topics related to management information systems (MIS) including:
1) The definitions of data, information, and knowledge and how they relate to each other through processing.
2) Qualitative and quantitative data and examples of each.
3) The definition of information and how processed data becomes information.
4) The definition of MIS and its role and outputs in decision making.
5) System development methodologies including structured and object-oriented approaches.
The document describes different types of information systems including transaction processing systems, management information systems, and decision support systems. It provides examples of transaction processing systems like billing systems and defines their key characteristics and cycle. Management information systems are described as software tools that provide processed information to managers to help with decision making. The types, advantages, and outputs of management information systems are outlined. Decision support systems are defined as computer programs that compile information from various sources to support problem solving and decision making for managers.
The document discusses four main types of computer-based information systems: office automation systems, transaction processing systems, management information systems, and decision support systems. It provides details on the inputs, processes, and outputs of each system. Transaction processing systems capture and store organizational data. Management information systems take transaction data and transform it into information to support management decisions. Decision support systems provide access to data and models to support semi-structured problems and planning.
Transaction Processing Systems (TPS), Management Information System (MIS), Decision Support Systems (DSS), Group Decision Support System (GDSS), Executive Information System (EIS), Expert System (ES) – features, process, advantages & disadvantages, role of these systems in decision making process.
The three main information systems for strategic planning are decision support systems, management information systems, and executive information systems. Management information systems aim to meet the information needs of managers regarding current and past operations. Decision support systems provide tools and data to support decision making and allow flexibility. Executive information systems analyze and present customized information to executives in a friendly format using graphics and tools.
Here are some potential MIS solutions for the scenarios provided:
For the late orders issue, develop a tracking system to monitor order fulfillment from receipt to delivery. Set targets and alerts to address delays.
For the overwhelmed customer service team, implement an AI chatbot to handle common inquiries and complaints automatically via email. Forward only complex issues requiring human judgment.
For the long phone wait times, deploy an interactive voice response system with a virtual assistant to answer basic questions. Collect customer data to identify service gaps. Provide estimated wait times and call-back options for non-urgent requests.
For budget planning, create a forecasting model to analyze past income and expense trends. Factor in expected business conditions. Allow "what-if
Here are some potential MIS solutions for the scenarios provided:
For the late orders issue, develop a tracking system to monitor order fulfillment from receipt to delivery. Set targets and alerts to address delays.
For the overwhelmed customer service team, implement an AI chatbot to handle common inquiries and complaints automatically via email/website. Route only complex issues to human agents to improve efficiency.
For long phone wait times, deploy an interactive voice response system with a virtual assistant to answer basic questions without needing an agent. Collect customer data to identify service gaps.
For budget planning, create a forecasting model to analyze past income trends and external factors to generate predictive revenue estimates for the coming year. Incorporate sensitivity analysis to plan for various
Here are some potential MIS solutions for the scenarios provided:
For the late orders issue, develop a tracking system to monitor order fulfillment from receipt to delivery. Set targets and alerts to address delays.
For the overwhelmed customer service team, implement an AI chatbot to handle common inquiries and complaints automatically via email. Forward only complex issues requiring human judgment.
For the long phone wait times, deploy an interactive voice response system with a virtual assistant to answer basic questions. Collect customer data to identify service gaps. Provide estimated wait times and call-back options for non-urgent requests.
For budget planning, create a forecasting model to analyze past income and expense trends. Factor in expected business conditions. Allow "what-if
Here are some potential MIS solutions for the scenarios provided:
For the late orders issue, develop a tracking system to monitor order fulfillment from receipt to delivery. Set benchmarks and alerts to identify bottlenecks.
For the overwhelmed customer service team, implement an AI chatbot to handle common inquiries and complaints automatically via email. Forward only complex issues requiring human judgment.
For the long phone wait times, deploy an interactive voice response system with a virtual assistant to answer basic questions. Collect customer data to identify service gaps. Provide estimated wait times and call-back options for non-urgent requests.
For budget planning, create a forecasting model to analyze past income and expense trends. Factor in expected growth, economic indicators, and new
This document provides an overview of system analysis and design. It begins by defining a system, system analysis, and system design. It describes the principal roles and functions of a systems analyst, which include understanding business problems and how technology can solve them. The document then outlines the phases of the system development life cycle, including feasibility analysis, design, development, implementation, and maintenance. It also discusses different types of systems like transaction processing systems, office automation systems, and executive support systems. Finally, it covers topics like integrating new technologies, enterprise resource planning, wireless systems, and open source software.
discuss about System system analysis, system design, system analyst's role, Development of System through analysis, SDLC, Case Tools of SAD, Implementation, etc.
Dokumen tersebut membahas tentang pengelolaan database perusahaan dan teknologi. Database digunakan untuk menyimpan dan mengintegrasikan berbagai data dari berbagai aplikasi bisnis. Ada beberapa jenis database seperti database operasional, terdistribusi, eksternal, dan hipermedia. Manajemen sumber daya data dan penambangan data penting untuk mengelola database secara efektif.
Dokumen tersebut membahas tentang konsep dan proses manajemen pengetahuan perusahaan, termasuk jenis-jenis pengetahuan, aset intelektual, penciptaan pengetahuan, teknologi pendukung, dan pengukuran efektivitasnya. Manajemen pengetahuan bertujuan untuk mengidentifikasi, menyebarkan, dan menerapkan pengetahuan penting di dalam organisasi guna meningkatkan kinerja perusahaan.
Dokumen tersebut membahas tentang manajemen basis data perusahaan dan teknologi di masa depan. Ia menjelaskan tentang berbagai jenis basis data seperti basis data operasional, terdistribusi, eksternal, dan hipermedia. Dokumen tersebut juga membahas tentang penambangan data, manajemen sumber daya data, sistem manajemen basis data, dan perencanaan pengembangan basis data perusahaan besar.
Teknologi informasi dapat digunakan untuk mengimplementasikan lima strategi kompetitif utama yaitu biaya yang lebih rendah, diferensiasi, inovasi, mendukung pertumbuhan, dan membangun persekutuan. Banyak perusahaan menggunakan teknologi internet sebagai dasar untuk strategi-strategi tersebut.
Sistem informasi dan teknologi informasi telah menjadi komponen penting bagi keberhasilan bisnis dan organisasi. Teknologi informasi dapat membantu bisnis meningkatkan efisiensi, efektivitas proses bisnis, dan pengambilan keputusan manajerial. Sistem informasi dapat mendukung proses bisnis, pengambilan keputusan manajerial, dan strategi bisnis untuk keunggulan kompetitif.
This document discusses managing corporate information and communication technology (ICT) in the future. It covers five main topics: 1) enterprises information systems, 2) management support systems like decision support systems, 3) how ICT provides corporate competitive advantages, 4) managing corporate knowledge, and 5) managing corporate databases and technology.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
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5. Tipe Input Proses output User EIS Pengumpulan data;eksternal, internal Grafik, Simulasi, interaktif Proyeksi jawaban pertanyaan Manajer senior DSS Sejumlah data untuk dianalisis; model2 analitik dan alat2 bantu analisis Interaktif, simulasi, analisis Laporan2 khusus, analisis keputusan, jawaban pertanyaan Staf ahli, atau manajer MIS Rangkuman data; sejumlah besar data, model2 sederhana Laporan rutin, model2 sederhana, analisis level rendah Rangkuman dan laporan akhir Manajemen madya ECS Dokumen; jadwal Pengolahan dokumen, penjadwalan, komunikasi Dokumen, jadwal, surat Manajer, petugas administrasi PCS Spesifikasi desain robotic Pemodelan, simulasi Model-model grafis Staf ahli, staf teknis TPS Transaksi;even-even Pensortiran, pengkombinasian, pembaruan Laporan rinci, daftar, rangkuman Personil operasi, pengawas, supervisor
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10. Dimensi DSS MIS Philosophy Provide integrated tools, data, models, and language to users Provide structured information to end users System analysis Establish what tool are used in the decision process Identify information requirements Rancangan Iteratve process Deliver system based on frozen requirements
11. “ What financing option will cost he least to buy the house when the principal and interest are all paid? What option will result in the lowest monthly payments?” Bob says : 1 Question 2 Choice of model 3 Needed information 4 Answer 6 Model result 5 Now Bob says : 7 “ Now that I’ve figured the total cost of all the loan options and also the payment for each of the loans I can use that information to make the final decision on financing.” U S E R I N T E R V A C E M A N A G E M E N T M O D E L M A N A G E M E N T D A T A M A N A G E M E N T What-If Models Optimization Models Goal-Seeking Models Statistical Models Organizational Information External Information Personal Information
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20. Type of Information System Focus Expert Systems Knowledge – from experts Decision Support Systems Decision – interactive support Executive Information Systems Information – for executives Management Information Systems Information – for managerial end users Transaction Processing Systems Data – from business operations
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Editor's Notes
Abad 21 adalah abad di mana bisnis sangat bergantung pada teknologi informasi/sistem informasi. Dengan sistem informasi pembuat keputusan dapat membuat suatu perencanaan, pemasaran, produksi, hubungan antar organisasi sesegera mungkin. Sistem informasi juga dapat membantu penyelesaian masalah dan dapat memberikan peluang untuk meraih keungulan bersaing.
Sistem informasi yang digunakan akan berbeda antara pegawai, manajer, executive, yang mempunyai tanggung jawab, rentang kendali yang berbeda. Sebab, sebagai end-user (pemakai informasi) harus mempunyai pengetahuian yang spesifik bagaimana sistem informasi mempengaruhi fungsional-fungsional organisasi. Misalnya, manajer pemasaran harus mengetahui bagaimana sistem informasi digunakan dalam aktivitas pemasaran. Sehingga informasi yang diperoleh dari sistem informasi pemasaran sanyat membantu dalam pengambilan keputusannya.
Pertama, Support Operational System, mendukung level operasional bisnis dan level operasional manajemen, terdiri dari tipe Enterprise Collaboration Systems, Process Control System, Trancaction Processing Systems. Sistem ini melayani manajer operasional dengan cara mencatat aktivitas transaksi di dalam organisasi. Biasanya data yang tersedia harus dengan mudah didapat, akurat, dan belum kadaluarsa , contoh : Persediaan bahan baku. Kedua, Support Management System, mendukung level taktikal manajemen, dan level stategik manajemen, terdiri dari management Information Systems, Decision Support Systems, Executive Information Systems. Sistem ini melayani manajer taktikal dan executive dengan cara memonitor, mengendalikan, mengambil keputusan. Data yang didapat berupa data berkala mengenai suatu aktivitas, contoh : Penentuan harga jual produk.
Pada tabel di atas diperlihatkan spesifik tipe-tipe sistem informasi yang dihubungkan pada tiap-tiap level di dalam organisasi. Organisasi memiliki Executive Information Systems (EIS) pada level stratejik, Management Information Systems (MIS) dan Decision Support Systems (DSS) pada level manajemen, Enterprise Collaboration Systems (ECS) pada operational level, dan Transaction Processing Systems (TPS) pada level operasional bisnis. Tipe informasi ini akan secara khusus melayani setiap fungsi masing-masing area. Karena itu tipe sistem yang didapat di dalam organisasi dirancang untuk pekerja, manajer di setiap level dan informasi fungsional seperti sales, marketing, manufacturing, keuangan, akuntansi, dan sumber daya manusia.
Dengan mengerti komponen dan phylosophy dari DSS memungkinkan untuk membicarakan lebih rinci apa yang dimaksud dengan decision support. Masalah terstruktur adalah berulang-ulang dan rutin untuk algoritma yang diketahui dan akan mendukung pemecahan masalah. Masalah tidak terstruktur adalah cerita tidak rutin, dimana tidak ada algoritma (rumus) untuk pemecahan masalah, sehingga tidak dapat dipecahkan dengan suatu persamaan. Masalah semistruktur berbagi antara terstruktur dengan masalah tidak terstruktur. DSS dirancang untuk mendukung masalah semistruktur dan analisa masalah tidak terstruktur.
Saat ini informasi dapat dikumpulkan sebanyak mungkin melalui berbagai media yang dimiliki, namun belum tentu dapat dikelola dengan baik agar dapat dimanfaatkan pada waktu yang tepat secara efisien dan efektif. Kemampuan mengambil keputusan yang cepat dan cermat akan menjadi kunci keberhasilan dalam persaingan global di waktu mendatang, memiliki banyak informasi saja tidak cukup, bila tidak mampu meramunya dengan cepat menjadi alternatif-alternatif terbaik untuk pengambilan keputusan. Perkembangan teknologi informasi telah memungkinkan pengambilan keputusan dilakukan secara lebih cepat dan cermat. Decision Support Systems berfokus pada pengambilan keputusan dan memungkinkan terjadinya komunikasi dan koordinasi antara berbagai bidang maupun tingkat manajemen.
Pengambilan keputusan di dalam suatu organisasi merupakan hasil suatu proses komunikasi dan partisipasi yang terus dari keseluruhan organisasi. Pendekatan dapat dilakukan melalui yang bersifat individual/kelompok, sentralisasi/desentralisasi, partisipasi/tidak partisipasi, demokrasi/konsensus. Persoalan pengambilan keputusan pada dasarnya adalah bentuk pemilihan dari berbagai alternatif tindakan yang mungkin dipilih melalui mekanisme tertentu untuk menghasilkan keputusan yang terbaik. Penyusunan model keputusan adalah suatu cara untuk mengembangkan hubungan-hubungan logika yang mendasari persoalan keputusan ke dalam suatu model matematis yang mencerminkan hubungan yang terjadi antara faktor-faktor yang terlibat.
Pada dasarnya DSS merupakan pengembangan lebih lanjut dari Sistem nformasi manajemen terkomputerisasi (Computerized management Information System) yang dirancang sedemiian rupa seingga bersifat interaktif dengan pemakainya. Interaktif dimaksudkan untuk memudahkan integrasi antara berbagai komponen dalam proses pengambila keputusan, seperti prosedur, kebijakan, teknik analisis, serta pengalaman dan wawasan manajerial guna membentuk suatu kerangka keputusan yang bersifat fleksibel.
DSS memiliki kapabilitas dialog untk memperoleh informasi sesuai dengan kebutuhan, outpt ditujukan untuk persoil organisasi dalam semua tingkatan, memiliki subsistem-subsistem yang terintegrasi sedemikian rupa sehingga dapat berfungsi, sebagai kesatuan sistem, membutuhkan struktur data yang komprehensif yang dapat melayani kebutuhan informasi seluruh tingkatan manajemen, pendekatan easy to use (DSS yang efektif adalah kemudahannya untuk digunakan yang memungkinkan pemakai memilih pendekatan-pendekatan baru dalam membahas masalah, kemampuan sistem beradaptasi secara cepat (dapat menghadapi masalah baru dan menangani dengan mengadaptasi perubahan yang terjadi.
Suatu DSS memiliki 3 basic komponen utama yang menentukan kapablitas teknis DSS yaitu user interface management (perangkat lunak penyelenggara dialog), model management (manajemen basis model) dan data management (manajemen basis data)
Software yang sudah sangat terkenal adalah buseness Intelligent. Software yang memungkinkan user menggunakan database dan membuat model untuk mengolah databasenya ataupun formula yang sesuai dengan keinginan user.
Suatu model adalah abstrak representasi yang menggambarkan komponen atau hubungan dari phenomena. Suatu model dapa menjadi phisic seperti model pesawat, suatu model matematika (seperti suatu persamaan) atau suatu model verbal (seperti penjabaran prosedure untuk menulis suatu pesana).
Kemampuan yang dibutuhkan dari manajemen database sebagai berikut : - Kemampuan untuk mengkombinasikan berbagai variasi data melalui pengambilan dan ekstraksi data Kemampuan untuk menambahkan sumber data secara cepat dan mudah - Kemampuan untuk menggambarkan sruktur data logikal sesuai dengan pengertian pemakai sehingga pemakai mengetahui apa yang tersedia dan dapat menentukan kebutuhan penambahan dan pengurangan. - Kemampuan untuk menangani data secara personil sehingga pemakai dapat mencoba berbagai alternatif pertimbangan personil - Kemampuan untuk mengelola berbagai variasi data
EIS difokuskan terhadap penyediaan informasi sesuai dengan spesifikasi end-user, status access terhadap penggunaan time series data untuk mengidentifikasi kecenderungan (trend) dan penggunaan secara terintegrasi dari informasi eksternal untuk memberikan suatu konteks dunia yang sebenarnya terhadap data korporate internal. Penerapan EIS yang berhasil akan meminimalkan penggunaan laporan-laporan hard-copy, namun tetap memberikan informasi-informasi yang paling mutakhir kepada eksekutif.
1. Sarana presentasi informasi yang memiliki fungsi untuk : Menyajikan data rutin dan merinci suatu informasi (Drill-Down), Pemantauan kecenderungan (Trend), Laporan pengecualian (exception Report), Multimedia analisa. 2. Sarana pembentukan keputusan (DSS), yang dapat membantu eksekutif dalam menjelaskan penyimpangan yang terjadi, membentuk suatu model, melihat hal yang bersifat subyektif. 3. Sarana sistem permintaan secara multi dimensi dan time series, sehingga mempermudah pembacaan informasi dan pengambilan keputusan karena informasi sudah dalam bentuk Matriks dan per periodik waktu.
Management by exception merupakan perbandingan antara kinerja yang dianggarkan dan kinerja aktual, EIS bisa mengidentifikasikan perkecualian secara otomatis yang membuatnya diperhatikan oleh eksekutif. Peran utama EIS adalah membuat sintesa/sari dari informasi bervolume besar, sehingga menghasilkan suatu gambaran/model mental dari operasi perusahaan. CSFs merupakan hasil analisis manajemen terhadap tujuan tertentu yang diajukan dalam bentuk penetapan elemen kritis agar tujuan tersebut dapat dicapai secara epektif.
Sponsor eksekutif yang mengerti dan berkomitmen, Usaha EIS yang paling berhasil adalah yang pemakai utamanya adalah puncak eksekutif Sponsor operasi bekerjasama dengan eksekutif pemakai dan spesialis informasi untuk memastikan bahwa pekerjaan itu terlaksana Staf jasa informasi yang sesuai, harus tersedia staf yang mengerti teknologi informasi dan juga mengerti cara eksekutif menggunakan sistem ini. Teknologi informasi yang sesuai, penerapan EIS tidak berlebihan dalam memasukan perangkat keras/lunak yang tidak perlu, sistem itu harus sesederhana mungkin dan harus memberikan tepat seperti yang eksekutif inginkan. Manajemen data, eksekutif harus mampu mengetahui seberapa mutakhir data, dan mampu mengikuti analisis data Kaitan yang jelas dengan tujuan bisnis, sebagian besar EIS yang berhasil dirancang utnuk memecahkan masalah-masalah spesifik atau memenui kebutuhan yang dapat ditangani oleh teknologi informasi.
EIS dirancang untuk membantu eksekutif atau manajer senior untuk melakukan pemantauan terhadap perencanaan strategis perusahaan maupun untuk membantu dalam melakukan perencanaan strategis di masa yang akan datang. DSS dirancang untuk mendukung dan mengkatkan proses pengambilan keputusan. DSS dikembangkan untuk mendukung keputusan dari tingkat menengah ke atas, berbeda dengan EIS yang berkonsentrasi pada tingkat manajemen paling atas.