📊Business Intelligence: Descriptive Analytics (Definitions)

"The practice of reporting what has happened, analyzing contributing data to determine why it happened, and monitoring new data to determine what is happening now. Also known as reporting and business intelligence." (Brenda L Dietrich et al, "Analytics Across the Enterprise", 2014)

"If you are using charts and graphs or time series plots to study the demand or the sales patterns, or the trend for the stock market you are using descriptive analytics. Also, calculating statistics from the data such as, the mean, variance, median, or percentiles are all examples of descriptive analytics." (Amar Sahay, "Business Analytics" Vol. I, 2018)

"The simplest form of data analytics, in which historical data is collated and summarized in a user-friendly format, providing an understanding of what has previously happened." (Board International)

"Descriptive analytics is a form of data analytics that looks at data statistically to tell you what happened in the past. It helps a business understand how it is performing by providing context that will aid stakeholders in interpreting information." (Logi Analytics) [source]

"Descriptive analytics is a preliminary stage of data processing that creates a summary of historical data to yield useful information and possibly prepare the data for further analysis." (Techtarget) [source]

📊Business Intelligence: Delta Lake (Definitions)

"Delta Lake, an open source ACID table storage layer over cloud object stores initially developed at Databricks." (Michael Armbrust et al, "Delta Lake: High-Performance ACID Table Storage over Cloud Object Stores", Proceedings of the VLDB Endowment 13(12), 2020) [link]

"Delta Lake is a transactional storage software layer that runs on top of an existing data lake and adds RDW-like features that improve the lake’s reliability, security, and performance. Delta Lake itself is not storage." (James Serra, "Deciphering Data Architectures", 2024)

"A Delta Lake is an open-source storage layer designed to run on top of an existing data lake and improve its reliability, security, and performance." (Hewlett Packard Enterprise) [source]

"Delta Lake is an open source storage framework that enables building a Lakehouse architecture with various compute engines." (Apache Druid) [source] 

"Delta Lake is an open-source storage framework designed to improve performance and provide transactional guarantees to data lake tables." (lakeFS) [source] 

"Delta Lake is an open-source storage framework that enables building a format agnostic Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, Hive, Snowflake, Google BigQuery, Athena, Redshift, Databricks, Azure Fabric and APIs for Scala, Java, Rust, and Python." (Delta) [source]

"Delta Lake is an open-source storage layer that uses the ACID compliance of transactional databases to bring reliability, performance, and flexibility to data lakes. It is ideal for scenarios where you need transactional capabilities and schema enforcement within your data lake." (Qlik) [source] 

"Delta Lake is an open-source storage layer for big data workloads. It provides ACID transactions for batch/streaming data pipelines reading and writing data concurrently."  (Database of Databases) [source] 

"Delta Lake is the optimized storage layer that provides the foundation for tables in a lakehouse on Databricks. Delta Lake is open source software that extends Parquet data files with a file-based transaction log for ACID transactions and scalable metadata handling." (Databricks) [source]

"Delta Lake is the optimized storage layer that provides the foundation for tables in a lakehouse on Databricks. Delta Lake is open source software that extends Parquet data files with a file-based transaction log for ACID transactions and scalable metadata handling. Delta Lake is fully compatible with Apache Spark APIs, and was developed for tight integration with Structured Streaming, allowing you to easily use a single copy of data for both batch and streaming operations and providing incremental processing at scale." (Microsoft) [source] 

📊Business Intelligence: Prescriptive Analytics (Definitions)

"The analytics methods that recommend actions with the goal of finding a set of action that is predicted to produce the best possible outcome." (Brenda L Dietrich et al, "Analytics Across the Enterprise", 2014)

"Prescriptive analytics manipulate large data sets to make recommendations. Decision support that prescribes or recommends an action, rather than a forecast or a summary report." (Daniel J. Power & Ciara Heavin, "Data-Based Decision Making and Digital Transformation", 2018)

"Prescriptive analytics involves analyzing the results of the predictive analytics and 'prescribes' the best category to target and minimize or maximize the objective (s). It builds on predictive analytics and often suggests the best course of action leading to best possible solution. It is about optimizing (maximizing or minimizing) an objective function." (Amar Sahay, "Business Analytics" Vol. I, 2018)

"A combination of analytics, math, experiments, simulation, and/or artificial intelligence used to improve the effectiveness of decisions made by humans or by decision logic embedded in applications." (Forrester)

"A type of data analytics in which a combination of previous performance, business models, and logic is used by a machine to suggest the best course of action to achieve a desired outcome." (Board International)

"Prescriptive analytics is a form of data analytics that uses historical data to forecast what will happen in the future and recommend actions you can take to affect those outcomes." (Logi Analytics) [source]

"Prescriptive analytics is the area of business analytics (BA) dedicated to finding the best course of action for a given situation. Prescriptive analytics is related to both descriptive and predictive analytics. While descriptive analytics aims to provide insight into what has happened and predictive analytics helps model and forecast what might happen, prescriptive analytics seeks to determine the best solution or outcome among various choices, given the known parameters." (Techtarget) [source]

📊Business Intelligence: Business Analytics (Definitions)

"Meta-data that includes data definitions, report definitions, users, usage statistics, and performance statistics." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Provides models, which are formulas or algorithms and procedures to BI." (Linda Volonino & Efraim Turban, "Information Technology for Management "8th Ed, 2011)

"The process of leveraging all forms of analytics to achieve business outcomes by requiring business relevancy, actionable insight, performance management, and value measurement." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"Application of analytical tools to business questions. Business Analytics focuses on developing insights and understanding related to business performance using quantitative and statistical methods. Business Analytics includes Business Intelligence and Reporting." (Daniel J Power & Ciara Heavin, "Decision Support, Analytics, and Business Intelligence" 3rd Ed., 2017)

"BA is a data-driven decision making approach that uses statistical and quantitative analysis, information technology, and management science (mathematical modeling, simulation), along with data mining and fact-based data to measure past business performance to guide an organization in business planning and effective decision making." (Amar Sahay, "Business Analytics" Vol. I, 2018) 

"Use of data and quantitative and qualitative tools and techniques to improve operations and to support business decision ­making. Emphasis on using statistical and management science techniques, including data mining, to develop predictive and prescriptive models." (Daniel J. Power & Ciara Heavin, "Data-Based Decision Making and Digital Transformation", 2018)

"Aggregated information on business processes that enables managers to analyze process trends, view performance metrics, and respond to organizational change." (Appian)

"Refers to the skills, technologies, and practices for investigation of past business performance to gain insight and drive business planning. It focuses on developing new insights and understanding of business performance based on data and statistical methods. While business intelligence (BI) focuses on a consistent set of metrics to both measure past performance and guide business planning, business analytics is focused on developing new insights and understanding based on statistical methods and predictive modeling." (Insight Software)

"Business Analytics describes the skills, technologies, statistical methods and data driven approaches used to explore and investigate past business performance to gain new insights that can support business planning." (Accenture)

"Business analytics is comprised of solutions used to build analysis models and simulations to create scenarios, understand realities and predict future states. Business analytics includes data mining, predictive analytics, applied analytics and statistics, and is delivered as an application suitable for a business user." (Gartner)

"Business analytics (BA) is the iterative, methodical exploration of an organization's data, with an emphasis on statistical analysis." (Techtarget) [source] 

"Business Analytics is the process by which businesses use statistical methods and technologies for analyzing historical data in order to gain new insight and improve strategic decision-making." (OmiSci) [source]

"Business analytics is the process of gathering and processing all of your business data, and applying statistical models and iterative methodologies to translate that data into business insights." (Tibco) [source]

"Describes the skills, technologies, statistical methods and data driven approaches used to explore and investigate past business performance to gain new insights that can support business planning. Examples of business analytics tools include data visualization, business intelligence reporting and big data platforms." (Accenture)

♜Strategic Management: Scenario Analysis (Definitions)

"The process of identifying and evaluating potential risks to your business, and how they might play out, before they occur." (Annetta Cortez & Bob Yehling, "The Complete Idiot's Guide® To Risk Management", 2010)

"A technique to evaluate scenarios in order to predict their effect on portfolio objectives." (Project Management Institute, "The Standard for Portfolio Management" 3rd Ed., 2012)

"Scenario analysis, or what-if analysis, assesses the potential out­come of various scenarios by setting up several possible situations and analyzing the potential outcomes of each situation." (Christopher Donohue et al, "Foundations of Financial Risk: An Overview of Financial Risk and Risk-based Financial Regulation" 2nd Ed., 2015)

"A scenario analysis involves changing parameters in a model and then examining the results. A tool that helps a user explore different scenarios by changing a range of input values." (Ciara Heavin & Daniel J Power, "Decision Support, Analytics, and Business Intelligence" 3rd Ed., 2017)

"A technique for integrating information and ideas on current trends and future developments into a small number of distinctly different future outcomes." (Robert M Grant, "Contemporary Strategy Analysis" 10th Ed, 2018)

📊Business Intelligence: Key Performance Indicator [KPI] (Definitions)

"A performance measure that is indicative of the organization's performance within a specific area." (William A Giovinazzo, "Internet-Enabled Business Intelligence", 2002)

"A key performance indicator is a metric that provides business users with an indication of the current and historical performance of an aspect of the business." (Claudia Imhoff et al, "Mastering Data Warehouse Design", 2003)

"A measurement of business operations that compares a value at a specified point in time to a predetermined goal and, optionally, determines a trend direction. Often, a KPI is displayed using a graphical image such as a stoplight or a gauge using colors and relative indicators according to predetermined business rules." (Reed Jacobsen & Stacia Misner, "Microsoft SQL Server 2005 Analysis Services Step by Step", 2006) 

"An important set of metrics (see Metrics) used to determine how well a product is performing in the market." (Steven Haines, "The Product Manager's Desk Reference", 2008)

"Financial and non-financial metrics used to assess the strategic performance of an organization." (Ralph Kimball, "The Data Warehouse Lifecycle Toolkit", 2008)

"Quantifiable, measurable objectives agreed to beforehand and that reflect the critical success factors of an organization." (Tilak Mitra et al, "SOA Governance", 2008)

"A piece of information that an organization considers a crucial reflection of how well it's doing." (Ken Withee, "Microsoft Business Intelligence For Dummies", 2010)

"Financial and nonfinancial metrics used by an organization to define and evaluate how successful it is, typically in terms of making progress toward its goals." (Janice M Roehl-Anderson, "IT Best Practices for Financial Managers", 2010) 

"A business calculation (metric) with associated target values or ranges that allows macro level insights into the business process to manage profitability and monitor strategic impact." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"In business intelligence, refers to quantifiable measurements (numeric or scale-based) that assess a company’s effectiveness or success in reaching strategic and operational goals. Examples of KPI are product turnovers, sales by promotion, sales by employee, earnings per share, etc." (Carlos Coronel et al, "Database Systems: Design, Implementation, and Management 9th Ed", 2011)

"Metrics that measure the actual performance of critical aspects of IT, such as critical projects and applications, servers, the network, and so forth, against predefined goals and objectives." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"A measure used to quantify performance and outcomes." (Carl F Lehmann, "Strategy and Business Process Management", 2012)

"Quantitative performance measures that define the critical success factors of an organization, help the organization measure progress toward its goals and objectives, and identify areas for organizational performance and improvement." (Joan C Dessinger, "Fundamentals of Performance Improvement" 3rd Ed., 2012)

"A high-level measurement meant to indicate how well an individual or group is performing a set of activities that is considered critical to the overall success of an endeavor." (Project Management Institute, "Navigating Complexity: A Practice Guide", 2014)

"A measure that indicates the achievement of a specific objective." (Sally-Anne Pitt, "Internal Audit Quality", 2014)

"A measurement that shows whether an organization is progressing toward its stated goals." (Jim Davis & Aiman Zeid, "Business Transformation: A Roadmap for Maximizing Organizational Insights", 2014)

"Quantitative and measurable statement used to judge whether or not a goal has been reached; linked to a measurement and to the means of evaluation." (Gilbert Raymond & Philippe Desfray, "Modeling Enterprise Architecture with TOGAF", 2014)

"A set of metrics directly linked to the desired corporate objective (e.g., shareholder value) and explicitly integrated into the firm's incentive compensation system." (Thomas C Wilson, "Value and Capital Management", 2015)

"Most frequently referred to as KPIs. Metrics that indicate the performance of the business." (Brittany Bullard, "Style and Statistics", 2016)

"A set of business metrics used to determine whether a person, product, group, or division is successful." (Pamela Schure & Brian Lawley, "Product Management For Dummies", 2017)

"A variable or metric against which the success of a function or business is judged." (Jonathan Ferrar et al, "The Power of People: Learn How Successful Organizations Use Workforce Analytics To Improve Business Performance", 2017)

"A quantifiable measure used to evaluate the success of an organization, strategic initiative, employee, etc., in meeting the objectives for performance." (H James Harrington & William S Ruggles, "Project Management for Performance Improvement Teams", 2018)

"Quantifiable measurements, agreed to beforehand, that reflect the critical success factors of an organization." (William Stallings, "Effective Cybersecurity: A Guide to Using Best Practices and Standards", 2018)

"Used to assess and measure the performance of a specific business task. For example sales results in terms of order rates over a quarterly (3 month) period." (BCS Learning & Development Limited, "CEdMA Europe", 2019)

"KPIs are metrics defined to measure business performance of an enterprise. This term is related to BPM." (Martin Oberhofer et al, "The Art of Enterprise Information Architecture", 2010)

"A key performance indicator (KPI) is a high-level measure of system output, traffic or other usage, simplified for gathering and review on a weekly, monthly or quarterly basis." (Gartner)

"An acronym for Key Performance Indicator. These are key indicators to the health of the business." (BI System Builders)

"A business calculation that allows macro level insights into the business process to manage profitability." (Information Management)

"A type of performance measurement an organization may use to evaluate its success." (Board International)

"Business metrics used to evaluate factors that are crucial to organizational success." (Insight Software)

"Personalized performance metrics and benchmarks that drive the financial and operational success of the company." (Appian)

"A predefined measure that is used to track performance of a strategic goal, objective, plan, initiative, or business process. A KPI is evaluated against a target. An explicit and measurable value taken directly from a data source. Key performance indicators (KPIs) are used to measure performance in a specific area, for example, revenue per customer." (Microsoft)

"An indicator gauging how well a company progresses in numerous areas such as finance, customer service, and product availability and distribution." (Microstrategy)

"Key Performance Indicator - is a critical measurement of the performance of essential tasks, operations, or processes in a company. A KPI will usually unambiguously reveal conditions or performance that is outside the norm and that signals a need for managerial intervention." (Targit)

"Key performance indicators or KPIs […] are visual indicators in the form of color-coded shapes that are tied to a pre-defined, critical threshold. When the threshold is crossed, the KPI’s function is to alert key personnel so that they can take the necessary action." (Logi Analytics) [source]

"Key performance indicators (KPIs) are business metrics used by corporate executives and other managers to track and analyze factors deemed crucial to the success of an organization." (Techtarget) [source] 

📊Business Intelligence: Single Version of Truth [SVoT]/Single Source of Truth [SSoT] (Definitions)

System of Record (SOR): "Also called Single Point of Truth (SPOT), is a method for addressing the data quality problems caused by having multiple, inconsistent representations of the same entity or entity attribute by designating one system as holding and maintaining the authoritative source." (John R Talburt, "Entity Resolution and Information Quality", 2011)

"The SSOT is a logical, often virtual and cloud-based repository that contains one authoritative copy of all crucial data, such as customer, supplier, and product details." (Leandro DalleMule &  Thomas H Davenport, "What’s Your Data Strategy?" , Harvard Business Review, 2017) [source] 

"A single source of truth (SSOT) is the practice of aggregating the data from many systems within an organization to a single location. A SSOT is not a system, tool, or strategy, but rather a state of being for a company’s data in that it can all be found via a single reference point." (MuleSoft) [source]

"One version of the truth (or ‘single version of the truth’; or SVOT: "A technical concept describing the business analysis ideal of having either a single centralized database (data warehouse), or at least a distributed synchronized database, which stores all of an organization’s data in a consistent and non-redundant form. A combination of software, data quality, and strong data leadership can help enterprises and organizations achieve SVOT." (Insight Software)

"Single source of truth (SSOT) is a concept used to ensure that everyone in an organization bases business decisions on the same data." (Talend) [source]

Single Version of the Truth: "One single central data warehouse containing quality assured data that is delivered accurately through Business Intelligence reports. The opposite of this is numerous databases resulting in Business Users getting conflicting answers and results to the same question." (BI System Builders)

📊Business Intelligence: Self-Service BI (Definitions)

Self-service business intelligence (BI): "A self-service BI is a semantic layer that enables business users to perform ad hoc reporting and analysis with no IT intervention. Self-service BI helps in the higher adoption of BI solutions." (Saumya Chaki, "Enterprise Information Management in Practice", 2015)

Self-Service BI: "The activity of end users being self-sufficient in supplying themselves with Business Intelligence reports and/ or queries without having to rely on IT." (BI System Builders)

"Self-service analytics or self-service business intelligence refers to tools used to connect and analyze data, and which are operated primarily by business departments in the organization – rather than IT professionals or dedicated data analysts." (Sisense) [source]

"Self-service BI is a trend with a somewhat vague definition. In the most general sense, self-service BI tasks are those that business users carry out themselves instead of passing them on to IT for fulfillment. The aim is to give the users of BI tools more freedom and responsibility at the same time. At its heart lies the notion of user independence and self-sufficiency when it comes to the use of corporate information, which leads to a decentralization of BI in the organization." (BI Survey) [source]

"Self-service BI (business intelligence) is a software tool or application that empowers business users to analyze data, visualize insights, and obtain and share information in the form of reports and self-service BI dashboards – without the help of IT." (Logi Analytics) [source]

📊Business Intelligence: Real-time Analytics [RTA] (Definitions)

"Real-time analytics (RTA) describes an approach to data processing that allows us to extract value from events as soon as they are made available." (Mark Needham, "Building Real-Time Analytics Systems: From Events to Insights with Apache Kafka and Apache Pinot", 2023)

"Real-time analytics involves the use of tools and techniques to analyze data immediately after it's collected, enabling decision-makers to draw insights and take action in real-time. It integrates, processes, and analyzes live data to support instantaneous decision making. It's characterized by low latency between when data enters the system and when it’s processed." (Dremio) [source]

"Real-Time Intelligence is a powerful service that empowers everyone in your organization to extract insights and visualize their data in motion. It offers an end-to-end solution for event-driven scenarios, streaming data, and data logs." (Microsoft) [source]

"Real-time analytics is a set of techniques for processing data as soon as it becomes available. The main goal is to provide fast and actionable insights." (MongoDB) [source]

"Real-time analytics is the analysis of data as soon as that data becomes available. In other words, users get insights or can draw conclusions immediately (or very rapidly after) the data enters their system." (Sisense) [source]

"Real-time analytics is the discipline that applies logic and mathematics to data to provide insights for making better decisions quickly. For some use cases, real time simply means the analytics is completed within a few seconds or minutes after the arrival of new data. On-demand real-time analytics waits for users or systems to request a query and then delivers the analytic results. Continuous real-time analytics is more proactive and alerts users or triggers responses as events happen." (Gartner) [source]

"Real-time analytics is the use of data and related resources for analysis as soon as it enters the system. The adjective real-time refers to a level of computer responsiveness that a user senses as immediate or nearly immediate." (Techtarget) [source]

"Real time analytics lets users see, analyze and understand data as it arrives in a system. Logic and mathematics are applied to the data so it can give users insights for making real-time decisions." (OmiSci) [source]

"Real-time analytics refers to the practice of collecting and analyzing streaming data as it is generated, with minimal latency between the generation of data and the analysis of that data." (Databricks) [source]

"The ability to use all available enterprise data as needed and usually involves streaming data that allows users to make business decisions on the fly." (Solutions Review)

📊Business Intelligence: Decision Support System [DSS] (Definitions)

"Interactive computer-based systems intended to help decision makers utilize data and models to identify and solve problems and make decisions." (D J Power, "Decision Support Systems Hyperbook", 2000)

"The original name for data warehousing." (Ralph Kimball & Margy Ross, "The Data Warehouse Toolkit" 2nd Ed., 2002)

"The presentation of data to support management in making decisions." (William A Giovinazzo, "Internet-Enabled Business Intelligence", 2002)

"The automated process to provide facts and information to facilitate decision-making activities. Usually DSS involves the analysis of many units of data in a heuristic fashion." (Margaret Y Chu, "Blissful Data ", 2004)

"A system used to support managerial decisions. Usually DSS involves the analysis of many units of data in a heuristic fashion. As a rule, DSS processing does not involve the update of data." (William H Inmon, "Building the Data Warehouse", 2005)

"Commonly known as DSS databases, these support decisions, generally more management-level and even executive-level decision-type of objectives." (Gavin Powell, "Beginning Database Design", 2006)

"A system used to support managerial decisions. Usually DSS involves the analysis of many units of data in a heuristic fashion. As a rule, DSS processing does not involve the update of data." (William H Inmon & Anthony Nesavich, "Tapping into Unstructured Data", 2007)

"A branch of the broadly defined management information system (MIS). It is an information system that provides answers to problems and that integrates the decision maker into the system as a component. The system utilizes such quantitative techniques as regression and financial planning modeling. DSS software furnishes support to the accountant in the decision - making process." (Jae K Shim & Joel G Siegel, "Budgeting Basics and Beyond", 2008)

"An application that uses data to support managerial decisions through ad hoc query, summarization, drill-down analysis, trend analysis, exception identification and 'what if' scenario modeling." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"An arrangement of computerized tools used to assist managerial decision making within a business." (Carlos Coronel et al, "Database Systems: Design, Implementation, and Management" 9th Ed., 2011)

"Computer-based information system that combines models and data to solve semistructured and some unstructured problems with intensive user involvement." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"Information processing application used by managers and business professionals to analyze situations, monitor and compare performance data, highlight changes that require their attention, and to identify the more promising solutions. DSSs are one component of the overall MIS content for a business" (Kenneth A Shaw, "Integrated Management of Processes and Information", 2013)

"A DSS is an interactive computer-based system or subsystem intended to help decision makers use communications technologies, data, documents, knowledge, or models to identify and solve problems, complete decision process tasks, and make decisions." (Ciara Heavin & Daniel J Power, "Decision Support, Analytics, and Business Intelligence" 3rd Ed., 2017)

"A computer-based information ­system that supports individual or team decision making. Five primary types: communications-driven, data-driven, document-driven, knowledge­driven, and data-driven DSS." (Daniel J Power & Ciara Heavin, "Data-Based Decision Making and Digital Transformation", 2018)

"A coordinated assemblage of people, devices or other resources that analyzes, typically, business data and presents it so that users can make business decisions more easily." (GEMET - Environmental thesaurus)

"A computer system that provides managers with the tools they need to analyze information they deem relevant for a particular decision or class of decisions. Pearson, "Digital Planet: Tomorrow's Technology and You" 10th Ed.)

"A computer-based system that supports organizational decision making activities. Oftentimes, this type of system is used when data is changing rapidly or is not easy to extrapolate." (Solutions Review)

"A decision support system includes the technologies used for management, operations, and planning in an organization to help users make better decisions by providing data and analytics capabilities." (Qlik) [source]

"A decision support system (DSS) is a computer program application that analyzes business data and presents it so that users can make business decisions more easily. It is an 'informational application' (to distinguish it from an 'operational application' that collects the data in the course of normal business operation)." (Techtarget) [source]

"A decision support system or tool is one specifically designed to allow business end users to perform computer generated analyses of data on their own. This system supports exception reporting, stop light reporting, standard repository, data analysis and rule-based analysis." (Information Management)

"An application primarily used to consolidate, summarize, or transform transaction data to support analytical reporting and trend analysis." (IDW BI)

"Business intelligence, sometimes abbreviated BI, is a broad term that describes the set of processes that business use to analyze the data that they generate through operations and turn it into actionable insights that can drive effective business decision-making." (Sumo Logic) [source]

"Software tools that help with decision support." (Oracle) 

📊Business Intelligence: Business Intelligence [BI] (Definitions)

"Throughout Holland, Flanders, France, and Germany, he maintained a complete and perfect train of business intelligence. The news of the many battles fought was thus received first by him, and the fall of Namur added to his profits, owing to his early receipt of the news." (Richard M Devens, "Cyclopaedia of Commercial and Business Anecdotes", 1865) [first usage of the term] 

"An automatic system is being developed to disseminate information to the various sections of any industrial, scientific or government organization. This intelligence system will utilize data-processing machines for auto-abstracting and auto-encoding of documents and for creating interest profiles for each of the ‘action points’ in an organization. Both incoming and internally generated documents are automatically abstracted, characterized by a word pattern, and sent automatically to appropriate action points. […] All of these techniques are based on statistical procedures which can be performed on present-day data processing machines. Together with proper communication facilities and input-output equipment a comprehensive system may be assembled to accommodate all information problems of an organization. We call this a Business Intelligence System." (Hans P Luhn, "A Business Intelligence System", IBM Journal, 1958)  [first usage of the term in modern context] 

"The communication facility serving the conduct of a business (in the broad sense) may be referred to as an intelligence system. The notion of intelligence is also defined here, in a more general sense, as ‘the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal’." (Hans P Luhn,"A Business Intelligence System", IBM Journal, 1958)

"The process of accessing and analyzing data and using it to make better business decisions. Business intelligence distinguishes the use of data, which may or may not be valuable, with the use of information, which is always of value in business decisions." (Microsoft Corporation, "Microsoft SQL Server 7.0 Data Warehouse Training Kit", 2000)

"A generic term to describe leveraging the organization’s internal and external information assets for making better business decisions." (Ralph Kimball & Margy Ross, "The Data Warehouse Toolkit" 2nd Ed., 2002)

"The processes, technologies, and tools needed to turn data into information, information into knowledge, and knowledge into plans that drive profitable business action. Business intelligence encompasses data warehousing, business analytic tools, and content/knowledge management." (Data Warehousing Institute, 2002)

"Thinking abstractly about an organization, reasoning about the business, organizing large quantities of information about the business in order to define and execute a strategy." (William A Giovinazzo, "Internet-Enabled Business Intelligence", 2002)

"Business intelligence is the set of processes and data structures used to analyze data and information used in strategic decision support. The components of Business Intelligence are the data warehouse, data marts, the DSS interface and the processes to 'get data in' to the data warehouse and to 'get information out'." (Claudia Imhoff et al, "Mastering Data Warehouse Design", 2003)

"The set of products or services used to access and analyze data to turn them into information or knowledge enhancement. It includes decision support and data warehousing." (Margaret Y Chu, "Blissful Data", 2004)

"A category of applications and technologies to guide the analysis and use of detailed business data for improved business decision making. The term is sometimes used synonymously with decision support, though business intelligence is technically much broader." (Jill Dyché & Evan Levy, "Customer Data Integration", 2006)

"An approach to management that allows an organization to define what information is useful and relevant to its corporate decision making. Business intelligence helps decision makers make better decisions faster by converting data into information." (Reed Jacobsen & Stacia Misner, "Microsoft SQL Server 2005 Analysis Services Step by Step", 2006)

"Business Intelligence is defined as getting the right information to the right people at the right time. The term encompasses all the capabilities required to turn data into intelligence that everyone in your organization can trust and use for more effective decision making."(Stefanie V Gerlach et al, "Business Intelligence Competency Centers", 2006)

"The part of information technology that focuses on reporting and analysis currently goes by the name business intelligence (BI)." (Stephen Few, "Information Dashboard Design", 2006)

"Business information and business analyses within the context of key business processes that lead to decisions and actions and which result in improved business performance." (Steve Williams & Nancy Williams, "The Profit Impact of Business Intelligence", 2007)

"The activity of converting data into information." (William H Inmon & Anthony Nesavich, "Tapping into Unstructured Data", 2007)

"Business Intelligence is a method of storing and presenting key enterprise data so that anyone in your company can quickly and easily ask questions of accurate and timely data. Effective BI allows end users to use data to understand why your business got the particular results that it did, to decide on courses of action based on past data, and to accurately forecast future results." (Lynn Langit, "Foundations of SQL Server 2005 Business Intelligence", 2007)

"A generic term to describe leveraging the organization’s internal and external information assets to support improved business decision making. Some commentators use the term business intelligence to refer only to the reporting and analysis of data stored in the data warehouse. Because the industry has not reached agreement, we consistently use the phrase data warehouse/business intelligence (DW/BI) to mean the complete end-to-end system. Though some would argue that you can theoretically deliver BI without a data warehouse, and vice versa, that is ill-advised from our perspective. Linking the two together in the DW/BI acronym further reinforces their dependency." (Ralph Kimball, "The Data Warehouse Lifecycle Toolkit", 2008)

"A method used to analyze and interpret business performance data so that fact-based business decisions can be made. The business data referred to in BI is usually extracted from a variety of domains and databases, and presented in a way to bring about more efficient analysis." (Steven Haines, "The Product Manager's Desk Reference", 2008)

"A somewhat generic term used for computer programs that store, analyze, and broadcast data to users to answer business questions."  (Stuart Mudie et al, "BusinessObjects™ XI Release 2 for Dummies", 2008)

"Business intelligence is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision making." (Boris Evelson, Forrester Research, 2008)

"Skills and technologies used to help organizations make better decisions by better understanding their business, their market, and their customers." (Tony Fisher, "The Data Asset", 2009)

"The collection of one or more reports or analyses, using data from the data warehouse, that provide insight into the performance of a business organization. These reports and analyses are typically interactive to enable further understanding of specific areas of interest. They are used to support business professionals in their decision-making processes." (Laura Reeves, "A Manager's Guide to Data Warehousing", 2009)

"BI combines products, technology, and methods to organize key information that management needs to improve profit and performance. More broadly, we think of BI as business information and business analyses within the context of key business processes that lead to decisions and actions and that result in improved business performance. In particular, BI means leveraging information assets within key business processes to achieve improved business performance." (Nancy Williams & Steve Williams, "The Profit Impact of Business Intelligence", 2010)

"Focuses on the collection of those transactions and forming them into a database structure that facilitates analysis." (Anthony D Giordano, "Data Integration Blueprint and Modeling: Techniques for a Scalable and Sustainable Architecture", 2010)

"Generally used synonymously with the information available in an enterprise for making strategic decisions." (Paulraj Ponniah, "Data Warehousing Fundamentals for IT Professionals", 2010)

"Using computer software systematically, throughout an organization, to get a handle on the mountains of data that flow from modern business. BI turns the raw data into ready-to-use business information that becomes an ongoing part of strategic decision-making." (Ken Withee, "Microsoft Business Intelligence For Dummies", 2010)

"Software that enables users to obtain enterprise-wide information for reporting, analytics, data mining, benchmarking, business performance management, and predictive analytics in order to support business decision making." (Janice M Roehl-Anderson, "IT Best Practices for Financial Managers", 2010)

"This is a term that describes a broad variety of analytical applications used by an enterprise to get intelligent and meaningful insight into how the business performed in the past or is currently performing. This insight is typically used to make decisions, giving a business a competitive advantage. BI covers a broad field such as Data Warehousing, data marts, text analytics, data mining, or business reporting to name just a few." (Martin Oberhofer et al, "The Art of Enterprise Information Architecture", 2010)

"A collection of data analysis methods and techniques used by businesses to improve decision making, forecasting, and operational processes in order to gain a competitive advantage in the marketplace." (John R Talburt, "Entity Resolution and Information Quality", 2011)

"A comprehensive, cohesive, and integrated set of tools and processes used to capture, collect, integrate, store, and analyze data with the purpose of generating and presenting information used to support business decision making." (Carlos Coronel et al, "Database Systems: Design, Implementation, and Management" 9th Ed., 2011)

"Category of applications for gathering, storing, analyzing, and providing access to data to help enterprise users make better decisions." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"Software products that create integrated systems across an organization or between an organization and its customers and suppliers to improve management of employee teams, customer service, and supply chains. May be used for strategic planning, budgeting, financial consolidation, decision support, and reporting to support diagnostic and interactive controls." (Leslie G Eldenburg & Susan K Wolcott, "Cost Management 2nd Ed", 2011)

[Strategic BI:] The application of BI tools to provide metrics to executives, often in conjunction with some formal method of business performance management, to help determine if a corporation is on target for meeting its goals and objectives. Used to support long-term corporate goals and objectives." (DAMA International, "The DAMA Dictionary of Data Management" 1st Ed., 2010)

"Business intelligence (BI) is a set of techniques that takes business data and creates information from those data so that managers can make decisions. In that way, organizations create business intelligence." (Michael S Gendron, "Business Intelligence Applied", 2012)

"Business intelligence taps information systems to extract and report data in organized ways that are helpful to decision makers." (John R Schermerhorn Jr, "Management" 12th Ed., 2012)

"Computer-based techniques used in identifying, extracting, and analyzing business data. Common functions of BI technologies are reporting, online analytical processing (OLAP), analytics, data and process mining, complex event processing, business performance management, benchmarking, and predictive analytics." (Craig S Mullins, "Database Administration: The Complete Guide to DBA Practices and Procedures" 2nd Ed., 2012)

"A broad classification of information-systems-based technologies that support the identification and presentation of insight. Common historical usage referred primarily to reporting-focused systems, but usage of the term has been broadened by some to include all forms of insight generation (including exploratory data analysis and predictive analytics)." (Evan Stubbs, Delivering Business Analytics: Practical Guidelines for Best Practice, 2013)

"A term often used to describe the range of analysis approaches used to process business data." (Kenneth A Shaw, "Integrated Management of Processes and Information", 2013)

"A broad category of applications and technologies for reporting, analyzing, and providing access to data to help enterprise users make better business decisions. BI applications include the activities of decision support systems, query and reporting, and online analytical processing (OLAP)." (Jim Davis & Aiman Zeid, "Business Transformation", 2014)

"A process for improving the decision-making process through enhanced data analysis." (Owen P. Hall Jr., "Teaching and Using Analytics in Management Education", 2014)

"Business intelligence is a set of theories, methodologies, architectures, and technologies that transform raw data into meaningful and useful information for business purposes."(Keith Holdaway, "Harness Oil and Gas Big Data with Analytics", 2014)

"The ability to collect, integrate, and organize the data in a way which received by the right source, at the right time, and via the right tool. It provides basic insights about the data by regenerating reports, queries, alerts, etc." (Shokoufeh Mirzaei, Defining a Business-Driven Optimization Problem, 2014) 

"The practice of reporting what has happened, analyzing contributing data to determine why it happened, and monitoring new data to determine what is happening now. It may include data summarization, visualization, and data interactions capability. Also known as descriptive analytics and reporting." (Brenda L Dietrich et al, "Analytics Across the Enterprise", 2014)

"A broad category of applications, technologies, and processes for integrated acquisition, interpretation, collation, analysis, and exploitation of data to help business users make better decisions in order to improve business operations, reduce uncertainty and apply past experience to develop an exact understanding of business dynamics." (Mandana Farzaneh et al, "Using Fuzzy Logic for Optimizing Business Intelligence Success in Multiple Investment Combinations", 2015)

"Business Intelligence, the set of tools and structures related to the management and the use of data for operational or analytical (decision-making) purposes." (Fernando Iafrate, "From Big Data to Smart Data", 2015)

"Business intelligence is a broad set of information technology (IT) solutions that includes tools for gathering, analyzing, and reporting information to the users about performance of the organization and its environment." (Anil K. Maheshwari, "Business Intelligence and Data Mining", 2015)

"Raw data derived from manufacturing and other business processes that has been organized and structured into meaningful information on which decisions can be based." (Mike Harwood, "Internet Security: How to Defend Against Attackers on the Web" 2nd Ed., 2015)

"Business intelligence is the process of delivering actionable business decisions from analytical manipulation and presentation of data within the confines of a business environment." (Ahmed Sherif, "Practical Business Intelligence", 2016)

"BI is a popularized, umbrella term that describes a set of concepts and methods used to improve business decision making by using fact-based support systems. The term is sometimes used interchangeably with briefing books and executive information systems. A Business Intelligence system is a data-driven DSS." (Daniel J Power & Ciara Heavin, "Decision Support, Analytics, and Business Intelligence" 3rd Ed., 2017)

"Umbrella term that describe a set of concepts and methods to improve business decision making by using fact-based ­decision support systems. Also, refers to a category of software tools that can be used to extract and analyze data from corporate databases." (Daniel J. Power & Ciara Heavin, "Data-Based Decision Making and Digital Transformation", 2018)

"Business intelligence is getting the right information to the right people at the right time so they can make decisions that ultimately improve performance." (Satyadhyan Chickerur et al, "Forecasting the Demand of Agricultural Crops/Commodity Using Business Intelligence Framework", 2019)

"A technological driven process for analyzing data and presenting information, in such a way that user can take immediate actions and unable decision making." (Neha Garg & Kamlesh Sharma, "Machine Learning in Text Analysis", 2020)

"A set of processes, technologies and tools comprising data warehousing, On-Line Analytical Processing, and information delivery in order to turn data into information and information into knowledge." (Nenad Stefanovic, "Big Data Analytics in Supply Chain Management", 2021)

"A catchall term encompassing a variety of tools, applications and methodologies that enable organizations to collect data from internal systems and external sources. BI can be used to prepare data for analysis, develop and run queries, and create reports, dashboards and visualizations with the end goal of providing results to decision makers and end users." (Insight Software)

"A process for analyzing data and presenting actionable insights to stakeholders in order to help them make more informed business decisions." (Solutions Review)

"A set of methodologies, processes, architectures, and technologies - supported by organizational structures, roles, and responsibilities - that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision making that contribute to improving overall enterprise performance." (Forrester)

"Encompasses the technologies, applications and practices used in the collection, integration, analysis, and presentation of business information to support better business decision-making." (Accenture)

"Uses technologies, processes, and applications to analyze mostly internal, structured data and business processes to support decision-making. Common functions are reporting, online analytical processing, analytics, data mining, process mining, complex event processing, benchmarking, text mining, predictive analytics, and prescriptive analytics." (Board International)

"The activity of taking data from source systems and turning it into valuable information for business users." (BI System Builders)

"The applications, infrastructure, tools or processes for analyzing data and presenting information to help company executives, managers and others make more informed business decisions." (KDnuggets)

"Business intelligence (BI) combines business analytics, data mining, data visualization, data tools and infrastructure, and best practices to help organizations to make more data-driven decisions." (Tableau) [source]

"Business Intelligence (BI) encompasses the technologies, applications and practices used in the collection, integration, analysis, and presentation of business information to support better business decision-making." (Accenture)

"Business intelligence (BI) includes the applications, infrastructure, tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance." (Tibco) [source]

"Business intelligence involves using software to analyze data so companies can make informed decisions." (Xplenty) [source]

"Business Intelligence (BI), is a methodology which covers the compiling, analyzing and interpreting of business data in order to make better-informed decisions. BI data tends to be put together through extensive research across a wide range of sources like industry reports, customer feedback, actual usage data of the company’s products, and competitive research." (kloudless)

"Business intelligence is actually an environment in which business users receive data that is reliable, consistent, understandable, easily manipulated and timely. With this data, business users are able to conduct analyses that yield overall understanding of where the business has been, where it is now and where it will be in the near future. Business intelligence serves two main purposes. It monitors the financial and operational health of the organization (reports, alerts, alarms, analysis tools, key performance indicators and dashboards). It also regulates the operation of the organization providing two-way integration with operational systems and information feedback analysis." (Information Management)

"BI is a broad term that encompasses data mining, process analysis, performance benchmarking, and descriptive analytics. BI parses all the data generated by a business and presents easy-to-digest reports, performance measures, and trends driving management decisions. Business intelligence addresses the needs of casual users, including executives, managers, front-line workers, customers and suppliers. It delivers reports, dashboards and scorecards that are tailored to each user’s role and populated with metrics aligned with strategic objectives and goals. This top-down style is powered by a classic data warehousing structure that consolidates enterprise data and enforces information consistency by transforming shared data into a common data model (schema) and BI semantic layer (metadata)." (Teradata) [source]

"Business intelligence is a data-driven process for analyzing and understanding how organizations work and make better decisions based on real insights. Business intelligence, or BI, has become a popular term across industries, but it is a catch-all term that encompasses various processes, tools, and methodologies that let companies capture data, analyze it, and derive better answers to key questions." (Sisense) [source]

"Business intelligence is a software-driven process allowing organizations to analyze raw data from multiple sources, extracting insights that lead to more effective business decisions.  […] While the term 'business intelligence' describes both a methodology and a category of enterprise software, the primary activity in business intelligence is data analysis. Business intelligence tools and applications correlate data about business performance and process it to determine the best course of action for a wide range of business functions." (Informatica) [source]

"Business intelligence (BI) is a technology-driven process for analyzing data and presenting actionable information which helps executives, managers and other corporate end users make informed business decisions. BI encompasses a wide variety of tools, applications and methodologies that enable organizations to collect data from internal systems and external sources, prepare it for analysis, develop and run queries against that data and create reports, dashboards and data visualizations to make the analytical results available to corporate decision-makers, as well as operational workers." (Techtarget) [source]

"Business intelligence (BI) leverages software and services to transform data into actionable insights that inform an organization’s strategic and tactical business decisions. BI tools access and analyze data sets and present analytical findings in reports, summaries, dashboards, graphs, charts and maps to provide users with detailed intelligence about the state of the business." (CIO) [source]

"Business intelligence (BI) is the collection of processes, technologies, skills, and applications used to make informed, data-driven business decisions. BI includes data collection, data aggregation, analysis, and meaningful presentation that facilitates decision-making." (Talend) [source]

"Business intelligence is the process by which enterprises use strategies and technologies for analyzing current and historical data, with the objective of improving strategic decision-making and providing a competitive advantage." (OmiSci) [source]

"[...] business intelligence is the process of collecting business data and turning it into information that is meaningful and actionable towards a strategic goal. Or put even more simply, BI is the effective use of data and information to make sound business decisions." (Logi Analytics) [source]

🕸Systems Engineering: Information Theory (Just the Quotes)

"[…] information theory is characterised essentially by its dealing always with a set of possibilities; both its primary data and its final statements are almost always about the set as such, and not about some individual element in the set." (W Ross Ashby, "An Introduction to Cybernetics", 1956)

"The general notion in communication theory is that of information. In many cases, the flow of information corresponds to a flow of energy, e. g. if light waves emitted by some objects reach the eye or a photoelectric cell, elicit some reaction of the organism or some machinery, and thus convey information." (Ludwig von Bertalanffy, "General System Theory", 1968) 

"The 'flow of information' through human communication channels is enormous. So far no theory exists, to our knowledge, which attributes any sort of unambiguous measure to this 'flow'." (Anatol Rapoport, "Modern Systems Research for the Behavioral Scientist", 1969)

"Probability plays a central role in many fields, from quantum mechanics to information theory, and even older fields use probability now that the presence of 'noise' is officially admitted. The newer aspects of many fields start with the admission of uncertainty." (Richard Hamming, "Methods of Mathematics Applied to Calculus, Probability, and Statistics", 1985)

"The field of 'information theory' began by using the old hardware paradigm of transportation of data from point to point." (Marshall McLuhan & Eric McLuhan, Laws of Media: The New Science, 1988)

"Without an understanding of causality there can be no theory of communication. What passes as information theory today is not communication at all, but merely transportation." (Marshall McLuhan & Eric McLuhan, "Laws of Media: The New Science", 1988)

"If quantum communication and quantum computation are to flourish, a new information theory will have to be developed." (Hans Christian von Baeyer, "Information, The New Language of Science", 2003)

"In fact, an information theory that leaves out the issue of noise turns out to have no content." (Hans Christian von Baeyer, "Information, The New Language of Science", 2003)

"In an information economy, entrepreneurs master the science of information in order to overcome the laws of the purely physical sciences. They can succeed because of the surprising power of the laws of information, which are conducive to human creativity. The central concept of information theory is a measure of freedom of choice. The principle of matter, on the other hand, is not liberty but limitation - it has weight and occupies space." (George Gilder, "Knowledge and Power: The Information Theory of Capitalism and How it is Revolutionizing our World", 2013)

"Information theory leads to the quantification of the information content of the source, as denoted by entropy, the characterization of the information-bearing capacity of the communication channel, as related to its noise characteristics, and consequently the establishment of the relationship between the information content of the source and the capacity of the channel. In short, information theory provides a quantitative measure of the information contained in message signals and help determine the capacity of a communication system to transfer this information from source to sink over a noisy channel in a reliable fashion." (Ali Grami, "Information Theory", 2016)

🕸Systems Engineering: Systems Thinking (Just the Quotes)

"A systems approach begins when first you see the world through the eyes of another." (C West Churchman, "The Systems Approach", 1968) 

"The systems approach to problems focuses on systems taken as a whole, not on their parts taken separately. Such an approach is concerned with total - system performance even when a change in only one or a few of its parts is contemplated because there are some properties of systems that can only be treated adequately from a holistic point of view. These properties derive from the relationship between parts of systems: how the parts interact and fit together." (Russell L Ackoff, "Towards a System of Systems Concepts", 1971) 

“The notion of ‘system’ has gained central importance in contemporary science, society and life. In many fields of endeavor, the necessity of a ‘systems approach’ or ‘systems thinking’ is emphasized, new professions called ‘systems engineering’, ‘systems analysis’ and the like have come into being, and there can be little doubt that this this concept marks a genuine, necessary, and consequential development in science and world-view.” (Ervin László, “Introduction to Systems Philosophy: Toward a New Paradigm of Contemporary Thought”, 1972)

"A company is a multidimensional system capable of growth, expansion, and self-regulation. It is, therefore, not a thing but a set of interacting forces. Any theory of organization must be capable of reflecting a company's many facets, its dynamism, and its basic orderliness. When company organization is reviewed, or when reorganizing a company, it must be looked upon as a whole, as a total system." (Albert Low, "Zen and Creative Management", 1976)

"There is a strong current in contemporary culture advocating ‘holistic’ views as some sort of cure-all […] Reductionism implies attention to a lower level while holistic implies attention to higher level. These are intertwined in any satisfactory description: and each entails some loss relative to our cognitive preferences, as well as some gain [...] there is no whole system without an interconnection of its parts and there is no whole system without an environment." (Francisco Varela, "On being autonomous: The lessons of natural history for systems theory", 1977)

"Systems thinking is a special form of holistic thinking - dealing with wholes rather than parts. One way of thinking about this is in terms of a hierarchy of levels of biological organization and of the different 'emergent' properties that are evident in say, the whole plant (e.g. wilting) that are not evident at the level of the cell (loss of turgor). It is also possible to bring different perspectives to bear on these different levels of organization. Holistic thinking starts by looking at the nature and behaviour of the whole system that those participating have agreed to be worthy of study. This involves: (i) taking multiple partial views of 'reality' […] (ii) placing conceptual boundaries around the whole, or system of interest and (iii) devising ways of representing systems of interest." (C J Pearson and R L Ison, "Agronomy of Grassland Systems", 1987) 

"Systems thinking is a discipline for seeing the 'structures' that underlie complex situations, and for discerning high from low leverage change. That is, by seeing wholes we learn how to foster health. To do so, systems thinking offers a language that begins by restructuring how we think." (Peter Senge, "The Fifth Discipline", 1990)

"Systems thinking is a discipline for seeing wholes. It is a framework for seeing interrelationships rather than things, for seeing patterns of change rather than static 'snapshots'. It is a set of general principles- distilled over the course of the twentieth century, spanning fields as diverse as the physical and social sciences, engineering, and management. [...] During the last thirty years, these tools have been applied to understand a wide range of corporate, urban, regional, economic, political, ecological, and even psychological systems. And systems thinking is a sensibility for the subtle interconnectedness that gives living systems their unique character." (Peter Senge, "The Fifth Discipline", 1990)

"Systems thinking is a framework for seeing interrelationships rather than things, for seeing patterns rather than static snapshots. It is a set of general principles spanning fields as diverse as physical and social sciences, engineering and management." (Peter Senge, "The Fifth Discipline", 1990)

"Systems philosophy brings forth a reorganization of ways of thinking. It creates a new worldview, a new paradigm of perception and explanation, which is manifested in integration, holistic thinking, purpose-seeking, mutual causality, and process-focused inquiry.” (Béla H. Bánáthy, "Systems Design of Education”, 1991)

"The new paradigm may be called a holistic world view, seeing the world as an integrated whole rather than a dissociated collection of parts. It may also be called an ecological view, if the term 'ecological' is used in a much broader and deeper sense than usual. Deep ecological awareness recognizes the fundamental interdependence of all phenomena and the fact that, as individuals and societies we are all embedded in (and ultimately dependent on) the cyclical process of nature." (Fritjof Capra & Gunter A Pauli, "Steering business toward sustainability", 1995)

"In the new systems thinking, the metaphor of knowledge as a building is being replaced by that of the network. As we perceive reality as a network of relationships, our descriptions, too, form an interconnected network of concepts and models in which there are no foundations. For most scientists such a view of knowledge as a network with no firm foundations is extremely unsettling, and today it is by no means generally accepted. But as the network approach expands throughout the scientific community, the idea of knowledge as a network will undoubtedly find increasing acceptance." (Fritjof Capra," The Web of Life: a new scientific understanding of living systems", 1996)

"It [system dynamics] focuses on building system dynamics models with teams in order to enhance team learning, to foster consensus and to create commitment with a resulting decision […] System dynamics can be helpful to elicit and integrate mental models into a more holistic view of the problem and to explore the dynamics of this holistic view […] It must be understood that the ultimate goal of the intervention is not to build a system dynamics model. The system dynamics model is a means to achieve other ends […] putting people in a position to learn about a messy problem … create a shared social reality […] a shared understanding of the problem and potential solutions … to foster consensus within the team [..]" (Jac A M Vennix, "Group Model Building: Facilitating Team Learning Using System Dynamics", 1996)

"Understanding ecological interdependence means understanding relationships. It requires the shifts of perception that are characteristic of systems thinking - from the parts to the whole, from objects to relationships, from contents to patterns. […] Nourishing the community means nourishing those relationships." (Fritjof Capra, "The Web of Life: A New Scientific Understanding of Living Systems", 1996)

"[...] information feedback about the real world not only alters our decisions within the context of existing frames and decision rules but also feeds back to alter our mental models. As our mental models change we change the structure of our systems, creating different decision rules and new strategies. The same information, processed and interpreted by a different decision rule, now yields a different decision. Altering the structure of our systems then alters their patterns of behavior. The development of systems thinking is a double-loop learning process in which we replace a reductionist, narrow, short-run, static view of the world with a holistic, broad, long-term, dynamic view and then redesign our policies and institutions accordingly." (John D Sterman, "Business dynamics: Systems thinking and modeling for a complex world", 2000)

"Systems thinking is based on the theory that a system is, in essence, circular. Using a systems approach in your strategic management, therefore, provides a circular implementing structure that can evolve, with continuously improving, self-checking, and learning capabilities [...]" (Stephen G Haines, "The Systems Thinking Approach to Strategic Planning and Management", 2000)

"The systems approach, on the other hand, provides an expanded structural design of organizations as living systems that more accurately reflects reality." (Stephen G Haines, "The Systems Thinking Approach to Strategic Planning and Management", 2000)

"This is what systems thinking is all about: the idea of building an organization in which each piece, and partial solution of the organization has the fit, alignment, and integrity with your overall organization as a system, and its outcome of serving the customer." (Stephen G Haines, "The Systems Thinking Approach to Strategic Planning and Management", 2000)

"True systems thinking, on the other hand, studies each problem as it relates to the organization’s objectives and interaction with its entire environment, looking at it as a whole within its universe. Taking your organization from a partial systems to a true systems state requires effective strategic management and backward thinking." (Stephen G Haines, "The Systems Thinking Approach to Strategic Planning and Management", 2000)

"Systems thinking is a mental discipline and framework for seeing patterns and interrelationships. It is important to see organizational systems as a whole because of their complexity. Complexity can overwhelm managers, undermining confidence. When leaders can see the structures that underlie complex situations, they can facilitate improvement. But doing that requires a focus on the big picture." (Richard L Daft, "The Leadership Experience", 2008)

"Systems thinking means the ability to see the synergy of the whole rather than just the separate elements of a system and to learn to reinforce or change whole system patterns. Many people have been trained to solve problems by breaking a complex system, such as an organization, into discrete parts and working to make each part perform as well as possible. However, the success of each piece does not add up to the success of the whole. to the success of the whole. In fact, sometimes changing one part to make it better actually makes the whole system function less effectively." (Richard L Daft, "The Leadership Experience", 2002)

"Deep change in mental models, or double-loop learning, arises when evidence not only alters our decisions within the context of existing frames, but also feeds back to alter our mental models. As our mental models change, we change the structure of our systems, creating different decision rules and new strategies. The same information, interpreted by a different model, now yields a different decision. Systems thinking is an iterative learning process in which we replace a reductionist, narrow, short-run, static view of the world with a holistic, broad, long-term, dynamic view, reinventing our policies and institutions accordingly." (John D Sterman, "Learning in and about complex systems", Systems Thinking Vol. 3 2003)

"There exists an alternative to reductionism for studying systems. This alternative is known as holism. Holism considers systems to be more than the sum of their parts. It is of course interested in the parts and particularly the networks of relationships between the parts, but primarily in terms of how they give rise to and sustain in existence the new entity that is the whole whether it be a river system, an automobile, a philosophical system or a quality system." (Michael C. Jackson, "Systems Thinking: Creative Holism for Manager", 2003) 

"In ecology, we are often interested in exploring the behavior of whole systems of species or ecosystem composed of individual components which interact through biological processes. We are interested not simply in the dynamics of each species or component in isolation, but the dynamics of each species or component in the context of all the others and how those coupled dynamics account for properties of the system as a whole, such as its persistence. This is what people seem to mean when they say that ecology is ‘holistic’, an otherwise rather vague term." (John Pastor, "Mathematical Ecology of Populations and Ecosystems", 2008)

"A systems approach is one that focuses on the system as a whole, specifically linking value judgments (what is desired) and design decisions (what is feasible). A true systems approach means that the design process includes the 'problem' as well as the solution. The architect seeks a joint problem–solution pair and understands that the problem statement is not fixed when the architectural process starts. At the most fundamental level, systems are collections of different things that together produce results unachievable by the elements alone."  (Mark W Maier, "The Art Systems of Architecting" 3rd Ed., 2009)

"Taking a systems approach means paying close attention to results, the reasons we build a system. Architecture must be grounded in the client’s/user’s/customer’s purpose. Architecture is not just about the structure of components. One of the essential distinguishing features of architectural design versus other sorts of engineering design is the degree to which architectural design embraces results from the perspective of the client/user/customer. The architect does not assume some particular problem formulation, as 'requirements'  is fixed. The architect engages in joint exploration, ideally directly with the client/user/customer, of what system attributes will yield results worth paying for."  (Mark W Maier, "The Art Systems of Architecting" 3rd Ed., 2009)

"Systems thinking focuses on optimizing for the whole, looking at the overall flow of work, identifying what the largest bottleneck is today, and eliminating it." (Matthew Skelton & Manuel Pais, "Team Topologies: Organizing Business and Technology Teams for Fast Flow", 2019)

More on "Systems Thinking" at the-web-of-knowledge.blogspot.com.

🕸Systems Engineering: Emergence (Just the Quotes)

"[Hierarchy is] the principle according to which entities meaningfully treated as wholes are built up of smaller entities which are themselves wholes […] and so on. In hierarchy, emergent properties denote the levels." (Peter Checkland, "Systems Thinking, Systems Practice", 1981)

"[…] self-organization is the spontaneous emergence of new structures and new forms of behavior in open systems far from equilibrium, characterized by internal feedback loops and described mathematically by nonlinear equations." (Fritjof Capra, "The web of life: a new scientific understanding of living systems", 1996)

"It may not be obvious at first, but the study of emergence and model-building go hand in hand. The essence of model-building is shearing away detail to get at essential elements. A model, by concentrating on selected aspects of the world, makes possible the prediction and planning that reveal new possibilities. That is exactly the problem we face in trying to develop a scientific understanding of emergence." (John H Holland, "Emergence" , Philosophica 59, 1997)

"When the behavior of the system depends on the behavior of the parts, the complexity of the whole must involve a description of the parts, thus it is large. The smaller the parts that must be described to describe the behavior of the whole, the larger the complexity of the entire system. […] A complex system is a system formed out of many components whose behavior is emergent, that is, the behavior of the system cannot be simply inferred from the behavior of its components." (Yaneer Bar-Yamm, "Dynamics of Complexity", 1997)

"Emergent self-organization in multi-agent systems appears to contradict the second law of thermodynamics. This paradox has been explained in terms of a coupling between the macro level that hosts self-organization (and an apparent reduction in entropy), and the micro level (where random processes greatly increase entropy). Metaphorically, the micro level serves as an entropy 'sink', permitting overall system entropy to increase while sequestering this increase from the interactions where self-organization is desired." (H Van Dyke Parunak & Sven Brueckner, "Entropy and Self-Organization in Multi-Agent Systems", Proceedings of the International Conference on Autonomous Agents, 2001)

"The phenomenon of emergence takes place at critical points of instability that arise from fluctuations in the environment, amplified by feedback loops." (Fritjof Capra, "The Hidden Connections", 2002)

"This spontaneous emergence of order at critical points of instability is one of the most important concepts of the new understanding of life. It is technically known as self-organization and is often referred to simply as ‘emergence’. It has been recognized as the dynamic origin of development, learning and evolution. In other words, creativity-the generation of new forms-is a key property of all living systems. And since emergence is an integral part of the dynamics of open systems, we reach the important conclusion that open systems develop and evolve. Life constantly reaches out into novelty." (Fritjof  Capra, "The Hidden Connections", 2002)

"Emergence is not really mysterious, although it may be complex. Emergence is brought about by the interactions between the parts of a system. The galloping horse illusion depends upon the persistence of the human retina/brain combination, for instance. Elemental gases bond in combination by sharing outer electrons, thereby altering the appearance and behavior of the combination. In every case of emergence, the source is interaction between the parts - sometimes, as with the brain, very many parts - so that the phenomenon defies simple explanation." (Derek Hitchins, "Advanced Systems Thinking, Engineering and Management", 2003)

"Emergence is the phenomenon of properties, capabilities and behaviours evident in the whole system that are not exclusively ascribable to any of its parts." (Derek Hitchins, "Advanced Systems Thinking, Engineering and Management", 2003)

"Another typical feature of theories of emergence is the layered view of nature. On this view, all things in nature belong to a certain level of existence, each according to its characteristic properties. These levels of existence constitute a hierarchy of increasing complexity that also corresponds to their order of appearance in the course of evolution." (Markus Eronen, "Emergence in the Philosophy of Mind", 2004)

"The basic concept of complexity theory is that systems show patterns of organization without organizer (autonomous or self-organization). Simple local interactions of many mutually interacting parts can lead to emergence of complex global structures. […] Complexity originates from the tendency of large dynamical systems to organize themselves into a critical state, with avalanches or 'punctuations' of all sizes. In the critical state, events which would otherwise be uncoupled became correlated." (Jochen Fromm, "The Emergence of Complexity", 2004)

"Complexity arises when emergent system-level phenomena are characterized by patterns in time or a given state space that have neither too much nor too little form. Neither in stasis nor changing randomly, these emergent phenomena are interesting, due to the coupling of individual and global behaviours as well as the difficulties they pose for prediction. Broad patterns of system behaviour may be predictable, but the system's specific path through a space of possible states is not." (Steve Maguire et al, "Complexity Science and Organization Studies", 2006)

"The beauty of nature insists on taking its time. Everything is prepared. Nothing is rushed. The rhythm of emergence is a gradual, slow beat; always inching its way forward, change remains faithful to itself until the new unfolds in the full confidence of true arrival. Because nothing is abrupt, the beginning of spring nearly always catches us unawares. It is there before we see it; and then we can look nowhere without seeing it. (John O'Donohue, "To Bless the Space Between Us: A Book of Blessings", 2008)

"Although the potential for chaos resides in every system, chaos, when it emerges, frequently stays within the bounds of its attractor(s): No point or pattern of points is ever repeated, but some form of patterning emerges, rather than randomness. Life scientists in different areas have noticed that life seems able to balance order and chaos at a place of balance known as the edge of chaos. Observations from both nature and artificial life suggest that the edge of chaos favors evolutionary adaptation." (Terry Cooke-Davies et al, "Exploring the Complexity of Projects", 2009)

"If universality is one of the observed characteristics of complex dynamical systems in many fields of study, a second characteristic that flows from the study of these systems is that of emergence. As self-organizing systems go about their daily business, they are constantly exchanging matter and energy with their environment, and this allows them to remain in a state that is far from equilibrium. That allows spontaneous behavior to give rise to new patterns." (Terry Cooke-Davies et al, "Exploring the Complexity of Projects", 2009)

"The notion of emergence is used in a variety of disciplines such as evolutionary biology, the philosophy of mind and sociology, as well as in computational and complexity theory. It is associated with non-reductive naturalism, which claims that a hierarchy of levels of reality exist. While the emergent level is constituted by the underlying level, it is nevertheless autonomous from the constituting level. As a naturalistic theory, it excludes non-natural explanations such as vitalistic forces or entelechy. As non-reductive naturalism, emergence theory claims that higher-level entities cannot be explained by lower-level entities." (Martin Neumann, "An Epistemological Gap in Simulation Technologies and the Science of Society", 2011)

"System theorists know that it's easy to couple simple-to-understand systems into a ‘super system’ that's capable of displaying behavioral modes that cannot be seen in any of its constituent parts. This is the process called ‘emergence’." (John L Casti, [interview with Austin Allen], 2012)

"Every system that has existed emerged somehow, from somewhere, at some point. Complexity science emphasizes the study of how systems evolve through their disorganized parts into an organized whole." (Lawrence K Samuels, "Defense of Chaos: The Chaology of Politics, Economics and Human Action", 2013)

"Things evolve to evolve. Evolutionary processes are the linchpin of change. These processes of discovery represent a complexity of simple systems that flux in perpetual tension as they teeter at the edge of chaos. This whirlwind of emergence is responsible for the spontaneous order and higher, organized complexity so noticeable in biological evolution - one–celled critters beefing up to become multicellular organisms." (Lawrence K Samuels, "Defense of Chaos: The Chaology of Politics, Economics and Human Action", 2013)

"This spontaneous emergence of order at critical points of instability, which is often referred to simply as 'emergence', is one of the hallmarks of life. It has been recognized as the dynamic origin of development, learning, and evolution. In other words, creativity-the generation of new forms-is a key property of all living systems." (Fritjof Capra, "The Systems View of Life: A Unifying Vision", 2014)

More quotes on "Emergence" at the-web-of-knowledge.blogspot.com.

🕸Systems Engineering: Connectedness (Just the Quotes)

"The first attempts to consider the behavior of so-called 'random neural nets' in a systematic way have led to a series of problems concerned with relations between the 'structure' and the 'function' of such nets. The 'structure' of a random net is not a clearly defined topological manifold such as could be used to describe a circuit with explicitly given connections. In a random neural net, one does not speak of 'this' neuron synapsing on 'that' one, but rather in terms of tendencies and probabilities associated with points or regions in the net." (Anatol Rapoport, "Cycle distributions in random nets", The Bulletin of Mathematical Biophysics 10(3), 1948)

"A NETWORK is a collection of connected lines, each of which indicates the movement of some quantity between two locations. Generally, entrance to a network is via a source (the starting point) and exit from a network is via a sink (the finishing point); the lines which form the network are called links (or arcs), and the points at which two or more links meet are called nodes." (Cecil W Lowe, "Critical Path Analysis by Bar Chart", 1966)

"The essential vision of reality presents us not with fugitive appearances but with felt patterns of order which have coherence and meaning for the eye and for the mind. Symmetry, balance and rhythmic sequences express characteristics of natural phenomena: the connectedness of nature - the order, the logic, the living process. Here art and science meet on common ground." (Gyorgy Kepes, "The New Landscape: In Art and Science", 1956)

"In fact, it is empirically ascertainable that every event is actually produced by a number of factors, or is at least accompanied by numerous other events that are somehow connected with it, so that the singling out involved in the picture of the causal chain is an extreme abstraction. Just as ideal objects cannot be isolated from their proper context, material existents exhibit multiple interconnections; therefore the universe is not a heap of things but a system of interacting systems." (Mario Bunge, "Causality: The place of the casual principles in modern science", 1959)

"To say a system is 'self-organizing' leaves open two quite different meanings. There is a first meaning that is simple and unobjectionable. This refers to the system that starts with its parts separate (so that the behavior of each is independent of the others' states) and whose parts then act so that they change towards forming connections of some type. Such a system is 'self-organizing' in the sense that it changes from 'parts separated' to 'parts joined'. […] In general such systems can be more simply characterized as 'self-connecting', for the change from independence between the parts to conditionality can always be seen as some form of 'connection', even if it is as purely functional […]  'Organizing' […] may also mean 'changing from a bad organization to a good one' […] The system would be 'self-organizing' if a change were automatically made to the feedback, changing it from positive to negative; then the whole would have changed from a bad organization to a good." (W Ross Ashby, "Principles of the self-organizing system", 1962)

"Information is recorded in vast interconnecting networks. Each idea or image has hundreds, perhaps thousands, of associations and is connected to numerous other points in the mental network." (Peter Russell, "The Brain Book: Know Your Own Mind and How to Use it", 1979)

"All certainty in our relationships with the world rests on acknowledgement of causality. Causality is a genetic connection of phenomena through which one thing (the cause) under certain conditions gives rise to, causes something else (the effect). The essence of causality is the generation and determination of one phenomenon by another." (Alexander Spirkin, "Dialectical Materialism", 1983)

"When loops are present, the network is no longer singly connected and local propagation schemes will invariably run into trouble. [...] If we ignore the existence of loops and permit the nodes to continue communicating with each other as if the network were singly connected, messages may circulate indefinitely around the loops and process may not converges to a stable equilibrium. […] Such oscillations do not normally occur in probabilistic networks […] which tend to bring all messages to some stable equilibrium as time goes on. However, this asymptotic equilibrium is not coherent, in the sense that it does not represent the posterior probabilities of all nodes of the network." (Judea Pearl, "Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference", 1988)

"A self-organizing system not only regulates or adapts its behavior, it creates its own organization. In that respect it differs fundamentally from our present systems, which are created by their designer. We define organization as structure with function. Structure means that the components of a system are arranged in a particular order. It requires both connections, that integrate the parts into a whole, and separations that differentiate subsystems, so as to avoid interference. Function means that this structure fulfils a purpose." (Francis Heylighen & Carlos Gershenson, "The Meaning of Self-organization in Computing", IEEE Intelligent Systems, 2003)

"Nodes and connectors comprise the structure of a network. In contrast, an ecology is a living organism. It influences the formation of the network itself." (George Siemens, "Knowing Knowledge", 2006)

"If a network is solely composed of neighborhood connections, information must traverse a large number of connections to get from place to place. In a small-world network, however, information can be transmitted between any two nodes using, typically, only a small number of connections. In fact, just a small percentage of random, long-distance connections is required to induce such connectivity. This type of network behavior allows the generation of 'six degrees of separation' type results, whereby any agent can connect to any other agent in the system via a path consisting of only a few intermediate nodes." (John H Miller & Scott E Page, "Complex Adaptive Systems", 2007)

"Networks may also be important in terms of view. Many models assume that agents are bunched together on the head of a pin, whereas the reality is that most agents exist within a topology of connections to other agents, and such connections may have an important influence on behavior. […] Models that ignore networks, that is, that assume all activity takes place on the head of a pin, can easily suppress some of the most interesting aspects of the world around us. In a pinhead world, there is no segregation, and majority rule leads to complete conformity - outcomes that, while easy to derive, are of little use." (John H Miller & Scott E Page, "Complex Adaptive Systems", 2007)

"Complexity theory embraces things that are complicated, involve many elements and many interactions, are not deterministic, and are given to unexpected outcomes. […] A fundamental aspect of complexity theory is the overall or aggregate behavior of a large number of items, parts, or units that are entangled, connected, or networked together. […] In contrast to classical scientific methods that directly link theory and outcome, complexity theory does not typically provide simple cause-and-effect explanations." (Robert E Gunther et al, "The Network Challenge: Strategy, Profit, and Risk in an Interlinked World", 2009)

"The simplest basic architecture of an artificial neural network is composed of three layers of neurons - input, output, and intermediary (historically called perceptron). When the input layer is stimulated, each node responds in a particular way by sending information to the intermediary level nodes, which in turn distribute it to the output layer nodes and thereby generate a response. The key to artificial neural networks is in the ways that the nodes are connected and how each node reacts to the stimuli coming from the nodes it is connected to. Just as with the architecture of the brain, the nodes allow information to pass only if a specific stimulus threshold is passed. This threshold is governed by a mathematical equation that can take different forms. The response depends on the sum of the stimuli coming from the input node connections and is 'all or nothing'." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)

"System dynamics is an approach to understanding the behaviour of over time. It deals with internal feedback loops and time delays that affect the behaviour of the entire system. It also helps the decision maker untangle the complexity of the connections between various policy variables by providing a new language and set of tools to describe. Then it does this by modeling the cause and effect relationships among these variables." (Raed M Al-Qirem & Saad G Yaseen, "Modelling a Small Firm in Jordan Using System Dynamics", 2010)

"We are beginning to see the entire universe as a holographically interlinked network of energy and information, organically whole and self-referential at all scales of its existence. We, and all things in the universe, are non-locally connected with each other and with all other things in ways that are unfettered by the hitherto known limitations of space and time." (Ervin László, "Cosmos: A Co-creator's Guide to the Whole-World", 2010)

"Information is recorded in vast interconnecting networks. Each idea or image has hundreds, perhaps thousands, of associations and is connected to numerous other points in the mental network." (Peter Russell, "The Brain Book: Know Your Own Mind and How to Use it", 2013)