Info 360 Chapter 11 – Flashcards
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unstructured decision
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Decision maker must provide judgment to solve problem Novel, important, non-routine No well-understood or agreed-upon procedure for making them; decided by senior management; ex. decide entry or exit from markets, approve capital budget, decide long-term goals
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structured decision
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Repetitive and routine Involve definite procedure for handling them so do not have to be treated as new; decided by operational management i.e. individual employees and teams; ex. determine overtime eligibility, restock inventory, offer credit to customers, determine special offers to customers
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semi-structured
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Only part of problem has clear-cut answer provided by accepted procedure; decided by middle management; ex. design a marketing plan, develop a departmental budget, design a new corporate website
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decision-making process
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intelligence, design, choice, implementation
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intelligence
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Discovering, identifying, and understanding the problems occurring in the organization—why is there a problem, where, what effects it is having on the firm
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design
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Identifying and exploring various solutions
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choice
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Choosing among solution alternatives
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implementation
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Making chosen alternative work and monitoring how well solution is working
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high velocity automated decision making
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Humans eliminated; For example: Trading programs at electronic stock exchanges
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Quality of decisions
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Accuracy Comprehensiveness Fairness Speed (efficiency) Coherence Due process
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Accuracy
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decision reflects reality
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comprehensiveness
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decision reflects a full consideration of the facts and circumstances
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fairness
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decision faithfully reflects the concerns and interests of affected parties
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speed (efficiency)
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decision making is efficient with respect to time and other resources, including the time and resources of affected parties, such as customers
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coherence
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decision reflects a rational process that can be explained to others and made understandable
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due process
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decision is the result of a kmown process and can be appealed to a higher authority
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six elements in business intelligence environment
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Data from business environment Business intelligence infrastructure Business analytics toolset Managerial users and methods Delivery platform User interface
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data from the business environment
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businesses must deal with both structured and unstructured data from many different sources, including big data; the data need to be organized and integrated so that they can be analyzed and used by human decision makers
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Delivery platform
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MIS, ESS, DSS; deliver information and knowledge to different people and levels in the firm
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data from business environment
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call centers, website, mobile devices, stores, blogs, suppliers, government employees
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business intelligence infrastructure
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the underlying foundation of business intelligence that is a powerful database system that captures all the relevant data to operate the business; data may be stored in transactional databases, or combined and integrated into an enterprise-data warehouse, series of interrelated data marts, or analytic platforms; has a focus on the collection, cleansing, storing, and reporting of business data
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business analytics toolset
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a set of software tools that are used to analyze data and produce reports, respond to questions posed by managers, and track the progress of the business using key indicators of performance; statistical models, data mining, OLAP, production reports
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managerial users and methods
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impose order on the analysis of data using a variety of methods that define strategic business goals and specify how progress will be measured; includes business performance management and balanced scorecard, forecasts
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user interface
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people are no longer tied to their desks and desktops; they learn quicker from a visual representation of the data; features data visualization in the form of graphs, charts, dashboards, and maps; scorecards, desktop, mobile, web portal, social media
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business intelligence and analytics capabilities
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Production reports Parameterized reports Dashboards/scorecards Ad-hoc query/search/report creation Drill-down Forecasts, scenarios, models
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production reports
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pre-defined reports based on industry specific requirements
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parameterized reports
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users enter several parameters in a pivot table to filter data and isolate impacts of parameters
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dashboards/scorecards
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visual tools for presenting performance data defined by users
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ad hoc query/search/report creation
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these allow users to create their own reports based on queries and searches
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forecasts, scenarios, models
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these include capabilities for linear forecasting, "what-if" scenario analysis, and analyzing data using standard statistical tools
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predictive analytics
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Use statistical analytics, data mining techniques, historical data, and assuptions about future conditions to predict future trends and behavior patterns by predicting responses to direct marketing campaigns, identifying best potential customers for credit cards, identify at-risk customers, & predicting how customers will respond to price changes and new services; Accuracies range from 65 to 90 percent
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Big Data Analytics
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Predictive analytics can use this data generated from social media, consumer transactions, sensor and machine output, and so on - For example: eBay's Hunch.com to predict user affinities for items not immediately obvious Public sector's drive to "smart cities" to inform decisions on Utility management, Transportation operation, Healthcare delivery, Public safety
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location analytics
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the ability to gain business insight from the location (geographic) component of the data; Big data analytics that uses location data from mobile phones, sensors, and maps; ex. Helping a utility company view customer costs as related to location
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(GIS) geographic information systems
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Help decision makers visualize problems with mapping; Tie location data about resources to map
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Decision-Support systems (DSS)
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BI delivery platform for "super-users" who want to create own reports, use more sophisticated analytics and models; used by middle managers for decision making; What-if analysis; Sensitivity analysis; Backward sensitivity analysis; Pivot tables; Intensive modeling techniques
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pivot tables
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support for semi-structured decision; spreadsheet function for multidimensional analysis; can analyze using multiple variables to aid in decision making; a "super user" manager or analyst employs these to identify and understand patterns in business information that may be useful for semistructured decision making
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sensitivity analysis
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support for semi-structured decision; asks what-if questions repeatedly to predict a range of outcomes when one or more variables are changed multiple times
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backward sensitivity analysis
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helps decision makers with goal seeking
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Executive support systems (ESS)
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balanced score card method; combine internal data with external -- financial data, news, etc.
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balanced score card method
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framework for operationalizing a firm's strategic plan by focusing on measurable outcomes on four dimensions of firm performance: financial, business process, customer, and learning and growth
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Key performance indicators (KPI)
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used to measure each dimension of the balanced score card method
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group decision support systems (GDSS)
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interactive computer-based system for facilitating the solution of unstructured problems by a set of decision makers working together as a group in one location or remotely; tool for groups to make decisions; Used in conference rooms with special hardware and software for collecting, ranking, storing ideas and decisions; Promote a collaborative atmosphere by guaranteeing contributors' anonymity; Support increased meeting sizes with increased productivity; Software follows structured methods for organizing and evaluating ideas
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facilitator
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more elaborate group support systems are set up to have this person who will coordinate the meeting to ensure it runs smoothly and efficiently; guides and acts as the sole decision maker
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anonymity
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an important element of GDSS so each person can contribute without worry of being criticized or having their ideas rejected solely on who submitted the idea; eliminates politics within the session
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business performance management (BPM)
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Management methodology that attempts to systematically translate a firm's strategies into operational targets For example: differentiation, low-cost producer, market share growth, scope of operation; Uses a set of KPI (key performance indicators) to measure progress toward targets
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drill down
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capability that takes the user from a more general view of the data to a more specific one at the click of a mouse
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intelligent techniques
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consist of Expert systems, Case-based reasoning, Fuzzy logic, Neural networks, Genetic algorithms, Intelligent agents that are based on artificial intelligence
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artificial intelligence (AI)
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Computer-based systems (hardware and software) that attempt to emulate human behavior and thought patterns; aid decision makers by capturing individual and collective knowledge, discovering patterns and behaviors in very large quantities of data, and generating solutions to problems that are too large and complex for human beings to solve on their own
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expert systems
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captures human expertise in a limited domain of knowledge as a set of rules in a software system that can be used by others in the organization; typically perform a limited number of tasks that can be performed by professionals in a few minutes or hours; model human knowledge as a set of rules that are collectively called the knowledge base - From 200 to 10,000 rules, depending on complexity; The system's inference engine searches through the rules and "fires" those rules that are triggered by facts gathered and entered by the user; Useful for dealing with problems of classification in which there are relatively few alternative outcomes and in which these possible outcomes are all known in advance; system contains a set of rules to be followed when used. The rules are interconnected; the number of outcomes is known in advance and is limited; there are multiple paths to the same outcome; and the system can consider multiple rules at a single time.
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knowledge base
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a collective set of rules that model human knowledge in the expert system
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inference engine
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in an expert system works by searching through the rules and "firing" those rules that are triggered by facts gathered by the user
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case-based reasoning
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Knowledge and past experiences of human specialists are represented as cases and stored in a database for later retrieval. System searches for stored cases with problem characteristics similar to new one, finds closest fit, and applies solutions of old case to new case. Successful and unsuccessful applications are tagged and linked in database. Used in medical diagnostic systems, customer support; represents knowledge as a database of past cases and their solutions. The system uses a six-step process to generate solutions to new problems encountered by the user
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fuzzy logic
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Rule-based technology that represents imprecision in categories (e.g., "cold" versus "cool") by creating rules that use approximate or subjective values; Describes a particular phenomenon or process linguistically and then represents that description in a small number of flexible rules; Provides solutions to problems requiring expertise that is difficult to represent in the form of IF-THEN rules
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neural networks
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Use hardware and software that parallel the processing patterns of a biological brain. ; "Learn" patterns from large quantities of data by searching for relationships, building models, and correcting over and over again the model's own mistakes.; Humans "train" the network by feeding it data for which the inputs produce a known set of outputs or conclusions, Machine learning; Useful for solving complex, poorly understood problems for which large amounts of data have been collected; uses rules it "learns" from patterns in data to construct a hidden layer of logic. The hidden layer then processes inputs, classifying them based on the experience of the model
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genetic algorithims
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Find the optimal solution for a specific problem by examining very large number of alternative solutions for that problem; Based on techniques inspired by evolutionary biology: inheritance, mutation, selection, and so on; Work by representing a solution as a string of 0s and 1s, then searching randomly generated strings of binary digits to identify best possible solution; Used to solve complex problems that are very dynamic and complex, involving hundreds or thousands of variables or formulas; uses an iterative process to refine the initial solutions so that the better ones, those with the higher fitness, are more likely to emerge as the best solution.
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intelligent agents
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Programs that work in the background without direct human intervention to carry out specific, repetitive, and predictable tasks for user, business process, or software application; Shopping bots; ex. Procter & Gamble (P) programmed group of semiautonomous agents to emulate behavior of supply-chain components, such as trucks, production facilities, distributors, and retail stores and created simulations to determine how to make supply chain more efficient
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knowledge management
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Business processes developed for creating, storing, transferring, and applying knowledge; Increases the ability of organization to learn from environment and to incorporate knowledge into business processes and decision making; Knowing how to do things effectively and efficiently in ways that other organizations cannot duplicate is major source of profit and competitive advantage
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3 kinds of knowledge
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structured, semistructured, tactic (unstructured)
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structured knowledge
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explicit knowledge that exists in formal text documents as well as in formal rules hat organizations derive by observing experts and their decision-making behaviors
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semistructured knowledge
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email, voice mail, digital pictures, etc.
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tacit (unstructured) knowledge
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knowledge residing in heads of employees, rarely written down
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enterprise-wide knowledge management systems
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Deal with all three types of knowledge; General-purpose, firm-wide systems that collect, store, distribute, and apply digital content and knowledge
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enterprise content management systems
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help organizations classify, manage, and organize both semi-structured an unstructured information and make it available throughout the enterprise; Capabilities for knowledge capture, storage, retrieval, distribution, and preservation to help firms improve business processes and decisions; Repositories for documents and best practices; Capabilities for collecting and organizing semistructured knowledge such as e-mail
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classification schemes
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Key problem in managing knowledge Each knowledge object must be tagged for retrieval
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digital asset management systems
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Manage, classify, store, and distribute unstructured digital data like photographs, graphic images, video, audio
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knowledge network systems (expertise location & management systems)
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Provide online directory of corporate experts in well-defined knowledge domains Use communication technologies to make it easy for employees to find appropriate expert in firm.
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collaboration tools
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social bookmarking
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social bookmarking
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allows users to to save their bookmarks publicly and tag with keywords.
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folksonomies
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Collection of tags created by users that provide metadata (data about data) regarding information; user-created taxonomies created for shared bookmarks and social tagging
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learning management systems
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Provide tools for management, delivery, tracking, and assessment of various types of employee learning and training.
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massive open online courses (MOOC)
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an online course made available to very large numbers of participants
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knowledge work systems (KWS)
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specialized systems for knowledge workers that are designed to promote the creation of knowledge and to ensure that new knowledge and technical expertise are properly integrated into business; requirements include specialized tools, great computing power to handle sophisticated graphics or complex calculations, quick and easy access to external databases, & user-friendly interfaces; require strong links to external knowledge bases in addition to specialized hardware and software.
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examples of knowledge work systems
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Computer-aided design (CAD) systems Virtual reality (VR) systems Virtual Reality Modeling Language (VRML) Augmented reality (AR) systems Investment workstations
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computer-aided design (CAD) systems
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capable of generating realistic-looking three dimensional graphic designs that can be rotated and viewed from all sides
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virtual reality systems
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capable of generating software to create computer-generated simulations that are so close to reality that users almost believe they are participating in a real-world situation
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augmented reality
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related technology for enhancing visualization; provides a live direct or indirect view of a physical real-world environment who elements are augmented by virtual computer-generated imagery
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virtual reality modeling language (VRML)
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a set of specifications for interactive, three-dimensional modeling on the world wide web that organizes multiple media types; platform independent, operates over a desktop computer, and requires little bandwith
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investment workstation
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used by the financial industry to leverage the knowledge and time of its brokers, traders, and portfolio managers
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knowledge network systems
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provide an online directory of corporate experts and their profiles, with details about them
