📊Business Intelligence: Operational Intelligence (Definitions)

"A business intelligence solution where all the data reflects its most current state in real-time." (Martin Oberhofer et al, "The Art of Enterprise Information Architecture", 2010)

"Operational BI provides time-sensitive, relevant information to operations managers and frontline, customer-facing employees to support daily work processes. These data-driven DSS differ from other DSS in terms of purpose, targeted users, data latency, data detail, and availability." (Ciara Heavin & Daniel J Power, "Decision Support, Analytics, and Business Intelligence" 3rd Ed., 2017)

"Operational Intelligence is the application of data analysis techniques to data that is generated or collected in real-time through an organization's IT infrastructure. The purpose of Operational Intelligence is to gather data from throughout the IT system, analyze it in real-time (as it is created or collected), and present it to IT operators in a simplified format that enables them to take rapid action and make decisions based on the results." (Sumo Logic) [source]

📊Business Intelligence: Data Storytelling (Definitions)

"A narrative way of describing a scenario, product idea, or strategy intended to provide a real-world context to promote decision making and better understanding." (Steven Haines, "The Product Manager's Desk Reference", 2008)

[storytelling:] "A method of communicating and sharing ideas, experiences and knowledge in a specific context." (Darren Dalcher, "Making Sense of IS Failures", Encyclopedia of Information Science and Technology 2nd Ed., 2009)

"A method of explaining a series of events through narrative." (Jonathan Ferrar et al, "The Power of People: Learn How Successful Organizations Use Workforce Analytics To Improve Business Performance", 2017)

"using a combination of data facts and a qualitative 'story' that provides effective communication of a business message." (Daniel J. Power & Ciara Heavin, "Data-Based Decision Making and Digital Transformation", 2018)

"Data storytelling can be defined as a structured approach for communicating data insights using narrative elements and explanatory visuals." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

[storytelling:] "The social and cultural activity of sharing stories, with great application to journalism." (Georgios Vassis et al, "Review and Evaluation of Systems Supporting Data Journalism", 2021)

"Data storytelling forms a compelling narrative by putting data in context to show the challenges, insights and solutions of a specific business problem. It normally highlights a series of changes or trends over time through linked visualizations that combine to tell a story." (Sisense) [source]

"Data storytelling is a method of visually presenting data to make it more understandable and easy to digest. Visualizations such as charts and graphs guide users toward a conclusion about their data and empower them to make a decision based on that conclusion." (Logi Analytics) [source]

"Data storytelling is a methodology for communicating information, tailored to a specific audience, with a compelling narrative. It is the last ten feet of your data analysis and arguably the most important aspect." (Nugit) [source]

"Data storytelling is the practice of building a narrative around a set of data and its accompanying visualizations to help convey the meaning of that data in a powerful and compelling fashion." (TDWI)

📊Business Intelligence: Big Data Analytics (Definitions)

"Big Data Analytics is the process of examining large amounts of data of a variety of types (big data) to uncover hidden patterns, unknown correlations and other useful information using advanced analytic techniques." (Pethuru Raj, "Big Data Analytics Demystified", 2014)

"Big data/analytics is defined as the capability of processing extremely large data sets to identify patterns of relationships (correlation, causality) among data to be used in detecting market trends, consumer behaviour and preferences." (James O Odia & Osaheni T Akpata, "Role of Data Science and Data Analytics in Forensic Accounting and Fraud Detection", 2021)

"Big data analytics is the process of examining large and varied data sets of big data to uncover information including hidden patterns and unknown correlations that can help organizations make better business decisions." (Ahmad M Kabil, Integrating Big Data Technology Into Organizational Decision Support Systems, 2021)

"Big data analytics is the use of advanced techniques to analyze, process and examine big data to uncover hidden patterns, trends and relations in order to assist management decision making." (Steven C S Hui et al, Enhancing Online Repurchase Intention via Application of Big Data Analytics in E-Commerce, 2021)

"Big Data Analytics refers to the intricate process of analyzing vast datasets to uncover hidden patterns, correlations, and customer behaviors from various sources like videos, social networks, and sensors."  (ICT Express, 2023)

"Big data analytics describes the process of uncovering trends, patterns, and correlations in large amounts of raw data to help make data-informed decisions. These processes use familiar statistical analysis techniques - like clustering and regression - and apply them to more extensive datasets with the help of newer tools." (Tableau) [source[

"Big Data Analytics examines large and diverse datasets (i.e. big data) to identify patterns, trends, correlations, and other information that lead to insights organizations can harness in support of better decision-making. Big Data Analytics is the science and engineering of problem solving where the nature, size, and shape of the data renders traditional analytics tools difficult or even impossible to use." (Accenture)

"Big data analytics is the process of evaluating that digital information into useful business intelligence." (Talend) [source]

"Big data analytics refers to the methods, tools, and applications used to collect, process, and derive insights from varied, high-volume, high-velocity data sets." (Microsoft) [source]

"Big data analytics refers to the systematic processing and analysis of large amounts of data and complex data sets, known as big data, to extract valuable insights. Big data analytics allows for the uncovering of trends, patterns and correlations in large amounts of raw data to help analysts make data-informed decisions." (IBM) [source]

📊Business Intelligence: Performance Indicator [PI] (Definitions)

"The measurement of the execution of activities. A performance indicator is often compared to recommended practices. It is a quantifiable target for achieving the adopted key performance factors. Metric is the unit of measure, and measure is a specific observation when tracking performance. The terms performance indicator, metric, and measure are often used interchangeably." (Paul C Dinsmore et al, "Enterprise Project Governance", 2012)

"A quantitative or qualitative measure to determine progress." (Fran Ackermann et al, "Visual Strategy: Strategy Mapping for Public and Nonprofit Organizations", 2014)

"A high-level metric of effectiveness and/or efficiency used to guide and control progressive development, e.g. Defect Detection Percentage (DDP) for testing [CMMI]." (Standard Glossary, "ISTQB", 2015)

"Quantifiable metrics used to measure the success of activities undertaken to reach strategic goals." (Gina Abudi & Brandon Toropov, "The Complete Idiot's Guide to Best Practices for Small Business", 2011)

📊Business Intelligence: Predictive Analytics (Definitions)

"Includes a variety of statistical and data mining techniques to analyze historical and current data to make predictions about the future." (Paulraj Ponniah, "Data Warehousing Fundamentals for IT Professionals", 2010)

"An area of statistical analysis that deals with extracting information from data and using it to predict future trends and behavior patterns." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"The branch of data mining that focuses on forecasting trends (e.g., regression analysis) and estimating probabilities of future events. Business analytics, as it is also called, provides the models, which are formulas or algorithms, and procedures to BI." (Linda Volonino & Efraim Turban, "Information Technology for Management 8th Ed", 2011)

"A statistical or data-mining solution consisting of algorithms and techniques that can be used on both structured and unstructured data (together or individually) to determine future outcomes. It can be deployed for prediction, optimization, forecasting, simulation, and many other uses" (Marcia Kaufman et al, "Big Data For Dummies", 2013)

"A methodology for forecasting futures events and trends using a variety of technologies including statistics and artificial intelligence." (Owen P. Hall Jr., "Teaching and Using Analytics in Management Education", 2014)

"A set of data–driven tools and methods to study a system behavior over time and to predict the future outcomes." (Shokoufeh Mirzaei, "Defining a Business-Driven Optimization Problem", 2014) 

"An advanced form of analytics that uses business information to find patterns and predict future outcomes and trends; determining credit scores by looking at a customer’s credit history and other data is a typical use for predictive analytics." (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

"Analytic methods used to make predictions. The practice of using mathematical modeling to predict outcomes." (Meta S Brown, "Data Mining For Dummies", 2014)

"Predictive analytics requires new methods and technologies by an organization to mine data to discover trends/patterns and test large numbers variables for unexpected insight." (Avnish Rastogi, "New Payment Models and Big Data Analytics", 2014)

"The practice of using statistics and data mining to analyze current and historical information to make predictions about what will happen in the future. Predictive modeling, the fitting of some data to some model, is a step in predictive analytics. Typically, predictive analytics also includes applying a model to additional data." (Brenda L Dietrich et al, "Analytics Across the Enterprise", 2014)

"Predictive analytics and modeling are statistical and analytical tools that examine and capture the complex relationships and underlying patterns among variables in the existing data in efforts to predict the future organizational performances, risks, trends, and behavior patterns". (Sema A Kalaian & Rafa M Kasim, "Predictive Analytics", 2015)

"A technique used in many business areas to enable organizations and companies to make more informed business discussions by making inference from analyzing patterns and relationships in consumer behavior data. A term refers to the procedure and technique to enable researchers or businesses to extra information from existing datasets to identify consumer behavioral patterns and insights to predict future trends and outcomes." (Kenneth C C Yang & Yowei Kang, "Real-Time Bidding Advertising: Challenges and Opportunities for Advertising Curriculum, Research, and Practice", 2016)

"A branch of advanced analytics that is used to make forecasts about future events." (Jonathan Ferrar et al, "The Power of People", 2017)

"A general term for using simple and complex models to predict what will happen, to support decision making. A process of using a quantitative model and current real-time or historical data to generate a score that is predictive of future behavior. Statistical analysis of historical data identifies a predictive model to support a specific decision task." (Daniel J Power & Ciara Heavin, "Decision Support, Analytics, and Business Intelligence" 3rd Ed., 2017)

"General term for using simple and complex ­models to support anticipatory decision making. Often a process of using a ­quantitative model and current real-time or historical data to generate a score that is predictive of future behavior." (Daniel J. Power & Ciara Heavin, "Data-Based Decision Making and Digital Transformation", 2018)

"[...] predictive analytics is about predicting the future outcomes. It also involves forecasting demand, sales, and profits for a company. The commonly used techniques for predictive analytics are different types of regression and forecasting models. Some advanced techniques are data mining, machine learning, neural networks, and advanced statistical models." (Amar Sahay, "Business Analytics" Vol. I, 2018)

"Predictive analytics is the branch of data mining concerned with the prediction of future probabilities and trends. The central element of predictive analytics is the predictor, a variable that can be measured for an individual or other entity to predict future behavior." (Thomas Ochs & Ute A Riemann, "IT Strategy Follows Digitalization", 2018)

"A statistical or data mining solution consisting of algorithms and techniques that can be used for both structured and unstructured data to determine future outcomes." (K Hariharanath, "BIG Data: An Enabler in Developing Business Models in Cloud Computing Environments", 2019)

"Predictive analytics is the branch of data mining concerned with the prediction of future probabilities and trends. The central element of predictive analytics is the predictor, a variable that can be measured for an individual or other entity to predict future behavior." (Thomas Ochs & Ute A Riemann, "IT Strategy Follows Digitalization", 2019)

"Predictive analytics represent any solution that supports the identification of meaningful patterns and correlations among variables in complex, structured, unstructured, historical, and potential future data sets for the purposes of predicting events and assessing the attractiveness of various courses of action." (Satyadhyan Chickerur et al, "Forecasting the Demand of Agricultural Crops/Commodity Using Business Intelligence Framework", 2019)

"A process for analyzing data in a manner that seeks to predict a likely future scenario or outcome. It can be used to improve decision making, mitigate risk, improve operations, and identify best practices." (Mike Gregory & Cynthia Roberts, "Maturing an Information Technology Privacy Program: Assessment, Improvement, and Change Leadership", 2020)

"It is a statistical process for denoting the average relationship between two or more factors with the involvement of dependent and independent variables." (Selvan C & S R Balasundaram, "Data Analysis in Context-Based Statistical Modeling in Predictive Analytics", 2021)

"A type of data analytics which identifies trends in historical datasets and uses those trends to forecast future performance, such as predicted sales revenue or demand." (Board International)

"[...] describes the practice of using historical data to predict future outcomes. It combines mathematical models (or 'predictive algorithms') with historical data to calculate the likelihood (or degree to which) something will happen." (Accenture)

"Techniques, tools, and technologies that use data to find models - models that can anticipate outcomes with a significant probability of accuracy." (Forrester)

"the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. Applied to business, predictive models and analysis are used to analyze current data and historical facts in order to better understand customers, products and partners and to identify potential risks and opportunities for a company." (KDnuggets)

"Predictive analytics is a form of advanced analytics that uses both new and historical data to forecast activity, behavior and trends. It involves applying statistical analysis techniques, analytical queries and automated machine learning algorithms to data sets to create predictive models that place a numerical value - or score - on the likelihood of a particular event happening." (Techtarget) [source]

"Predictive analytics is a set of methods and technologies that can be used to analyze current and historical data with the goal of making predictions about future events. Predictive analytics includes a wide variety of mathematical modeling and computer science techniques with the common goal of using past events to indicate the probability or likelihood of a future event." (Sumo Logic) [source]

"Predictive analytics is a sub-division of advanced analytics and focuses on the identification of future events and values with their respective probabilities." (BI Survey) [source]

"Predictive analytics is an area of data mining that is related to the overall prediction of future probabilities and trends. It uses historical data, machine learning, and AI to predict what will happen in the future." (Logi Analytics) [source]

"Predictive Analytics is the practice of employing statistics and modeling techniques to extract information from current and historical datasets in order to predict potential future outcomes and trends." (OmiSci) [source]

"Predictive analytics is the umbrella term for analyzing patterns found in data to predict future behavior or results. It includes techniques and algorithms found in statistics, machine learning, artificial intelligence, and data mining." (TDWI)

📊Business Intelligence: Measurement (Definitions)

[process measurement] "The set of definitions, methods, and activities used to take measurements of a process and its resulting products for the purpose of characterizing and understanding the process." (Sandy Shrum et al, "CMMI: Guidelines for Process Integration and Product Improvement, Second Edition", 2006)

"Measurement is understood as a continuous process during which process metrics are defined and measurement data are collected, analyzed, and evaluated. The objective is to understand, control, and optimize processes, for instance, to improve project control, reduce development effort and cost, or to improve on work products." (Lars Dittmann et al, "Automotive SPICE in Practice", 2008)

[process measurement] "An evaluation of the performance of a system process.  A measurement from the system process is compared to determine whether it is below the 'Minimum value' or above the 'Maximum value' of the success criterion for that system process. If so, it is the source of a system event type that is the trigger of another system process to correct the situation." (David C Hay, "Data Model Patterns: A Metadata Map", 2010)

"Systematically determining or estimating dimension, quantity, and capacity in order to assign value." (Joan C Dessinger, "Fundamentals of Performance Improvement." 3rd Ed, 2012)

"The process of measurement is the act of ascertaining the size, amount, or degree of something. Measurements are the results of the process of measuring." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement ", 2012)

"The process of determining the monetary amounts at which the elements of the financial statements are to be recognised and carried in the balance sheet [statement of financial position] and income statement [statement of comprehensive income]." (Project Management Institute, "The Standard for Program Management  3rd Ed..", 2013)

"(1) An instance of a measurement (a 'data point'). (2) The activity or process of making a measurement; for example, mapping empirical values to numbers or symbols of a measurement scale." (Richard D Stutzke, "Estimating Software-Intensive Systems: Projects, Products, and Processes", 2005)

"The process of assigning a number or category to an entity to describe an attribute of that entity." (ISO 14598)

📊Business Intelligence: Measures (Definitions)

"A quantitative, numerical column in a fact table. Measures typically represent the values that are analyzed. See also dimension." (Microsoft Corporation, "SQL Server 7.0 System Administration Training Kit", 1999)

"A metric is a measurable or quantitative value." (Microsoft Corporation, "Microsoft SQL Server 7.0 Data Warehouse Training Kit", 2000)

"A measure is a dimensional modeling term that refers to values, usually numeric, that measure some aspect of the business. Measures reside in fact tables. The dimensional terms measure and attribute, taken together, are equivalent to the relational modeling use of the term attribute." (Claudia Imhoff et al, "Mastering Data Warehouse Design", 2003)

"(1) A mapping from empirical properties to quantities in a formal mathematical model called a measurement scale. (2) To obtain a measurement." (Richard D Stutzke, "Estimating Software-Intensive Systems: Projects, Products, and Processes", 2005)

"In Dimensional modeling, a specific data item that describes a fact or aggregation of facts. Measures are implemented as metric facts." (Sharon Allen & Evan Terry, "Beginning Relational Data Modeling" 2nd Ed., 2005)

"A summarizable numerical value used to monitor business activity; it is also known as a fact. " (Reed Jacobsen & Stacia Misner, "Microsoft SQL Server 2005 Analysis Services Step by Step", 2006)

"A column of quantifiable data mapped to a dimension within a cube. Measures are often used to provide access to aggregations of data (such as annual sales of a product or a store), while also giving the ability to drill down into the details (such as quarterly or monthly sales)." (Robert D. Schneider and Darril Gibson, "Microsoft SQL Server 2008 All-In-One Desk Reference For Dummies", 2008)

[business measure:] "Business performance metric captured by an operational system and represented as a physical or computed fact in a dimensional model." (Ralph Kimball, "The Data Warehouse Lifecycle Toolkit", 2008)

"A set of usually numeric values from a fact table that is aggregated in a cube across all dimensions." (Jim Joseph et al, Microsoft® SQL Server 2008 Reporting Services Unleashed, 2009)

[business measures:] "The complete set of facts, base and derived, that are defined and made available for reporting and analysis." (Laura Reeves, "A Manager's Guide to Data Warehousing", 2009)

"A quantitative performance indicator or success factor that can be traced on an ongoing basis to determine successful operation and progress toward objectives and goals." (David Lyle & John G. Schmidt, "Lean Integration", 2010)

"1.Loosely used, a metric. 2.In data modeling, a quantified characteristic; the unit used to quantify the dimensions, capacity, or amount of something." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Value assigned (noun) or the process of assigning a value (verb) to an object through calculation, appraisal, estimation, or some other method." (Leslie G Eldenburg & Susan K. Wolcott, "Cost Management" 2nd Ed., 2011)

"In a cube, a set of values that are usually numeric and are based on a column in the fact table of the cube. Measures are the central values that are aggregated and analyzed." (Microsoft, "SQL Server 2012 Glossary", 2012)

"The act of identifying what to measure as well as actually collecting the measures that would help an organization understand if the process is operating within acceptable limits." (Project Management Institute, "Organizational Project Management Maturity Model (OPM3®)" 3rd Ed., 2013)

"Metrics such as count, maximum, minimum, sum, or average that are used in a fact table. Measures can be calculated with an SQL expression or mapped directly to a numeric value in a column." (Sybase, "Open Server Server-Library/C Reference Manual", 2019)

"The number or category assigned to an attribute of an entity by making a measurement. (ISO 14598)

📊Business Intelligence: Metric (Definitions)

"(1) The degree to which a product, process, or project possesses some attribute of interest. (2) A measured quantity (such as size, effort, duration, or quality). (3) The distance between two points in a vector space." (Richard D Stutzke, "Estimating Software-Intensive Systems: Projects, Products, and Processes", 2005)

"A summarizable numerical value used to monitor business activity; it is also known as a fact." (Reed Jacobsen & Stacia Misner, "Microsoft SQL Server 2005 Analysis Services Step by Step", 2006)

"A metric is a measurement. When a plan is put into place, a way to measure the outcome is needed. When a market share forecast is created and the outcomes are measured at a future date, the planned metric is compared with the actual metric to determine the degree to which the metric was met. From this data, strategies can be revised and tactical options can be reconsidered." (Steven Haines, "The Product Manager's Desk Reference", 2008)

"A numerical value describing a procedure, process, product attribute, or goal. A distinction is made between basic metrics (that can be measured directly) and derived metrics which result from mathematical operations using basic metrics." (Lars Dittmann et al, "Automotive SPICE in Practice", 2008)

"a measurement of some parameter, usually used in the assessment of a technology, approach, or design." (Bruce P Douglass, "Real-Time Agility: The Harmony/ESW Method for Real-Time and Embedded Systems Development", 2009)

"A metric is a standard unit of measure, such as meter or mile for length, or gram or ton for weight, or, more generally, part of a system of parameters, or systems of measurement, or a set of ways of quantitatively and periodically measuring, assessing, controlling, or selecting a person, process, event, or institution, along with the procedures to carry out measurements and the procedures for the interpretation of the assessment in the light of previous or comparable assessments." (Mark S Merkow & Lakshmikanth Raghavan, "Secure and Resilient Software Development", 2010)

"Groupings of data, or numbers, that reflect specific measures or subjects." (Annetta Cortez & Bob Yehling, "The Complete Idiot's Guide To Risk Management", 2010)

"a calculated value based on measurements used to monitor and control a process or business activity. Most metrics are ratios comparing one measurement to another." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"A specific, measurable standard against which actual performance is compared." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011) 

"Generally, a unit of measure selected used to monitor and control a process." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"In a data warehouse, numeric facts that measure a business characteristic of interest to the end user." (Carlos Coronel et al, "Database Systems: Design, Implementation, and Management" 9th Ed., 2011)

"Measurement of a particular characteristic of a task (for example, duration, effort, quality, cost, value delivered, or customer satisfaction)." (Charles Cooper & Ann Rockley, "Managing Enterprise Content: A Unified Content Strategy" 2nd Ed., 2012)

"1. A value from measuring a certain program or component attribute. Finding metrics is a task for static analysis. 2. A measurement scale and the method used for measurement." (Tilo Linz et al, "Software Testing Foundations" 4th Ed., 2014)

"A method of measuring something. It provides quantifiable data used to gauge the effectiveness of a process; metrics are commonly used to measure the effectiveness of a help desk." (Darril Gibson, "Effective Help Desk Specialist Skills", 2014)

"A value that you use to study some aspect of a project. A metric can be an attribute (such as the number of bugs) or a calculated value (such as the number of bugs per line of code)." (Rod Stephens, "Beginning Software Engineering", 2015)

"A measurement used to support the monitoring of a key performance indicator (KPI). A metric can have targets and can be used as a service level." (by Brian Johnson & Leon-Paul de Rouw, "Collaborative Business Design", 2017)

"Facts and figures representing the effectiveness of business processes that organizations track and monitor to assess the state of the company." (Jonathan Ferrar et al, "The Power of People: Learn How Successful Organizations Use Workforce Analytics To Improve Business Performance", 2017)

"A metric is the measurement of a particular characteristic of a company’s performance or efficiency. Metrics are the variables whose measured values are tied to the performance of the organization. They are also known as the performance metrics because they are performance indicators." (Amar Sahay, "Business Analytics" Vol. I, 2018)

"A measurable quantity that indicates progress toward some goal." (O Sami Saydjari, "Engineering Trustworthy Systems: Get Cybersecurity Design Right the First Time", 2018)

"Any number (often one calculated using two or more input numbers) used to evaluate some part of an organization's performance." (Marci S. Thomas & Kim Strom-Gottfried, "Best of Boards" 2nd Ed., 2018)

"Metrics are agreed-upon measures used to evaluate how well the organization is progressing toward the Portfolio, Large Solution, Program, and Team’s business and technical objectives." (Dean Leffingwell, "SAFe 4.5 Reference Guide: Scaled Agile Framework for Lean Enterprises" 2nd Ed., 2018)

"In a machine learning context, a metric is a measure of how good or bad a particular model is at its task. In a software context, a metric is a measure defined for an application, program, or function." (Alex Thomas, "Natural Language Processing with Spark NLP", 2020)

"A business calculation defined by an expression built with functions, facts, attributes, or other metrics." (Microstrategy)

"A measurement scale and the method used for measurement" (ISO 14598)

"Quantifiable measures used to track, monitor, and gauge the results and success of various business processes. Metrics are meant to communicate a company’s progression toward certain long and short term objectives. This often requires the input of key stakeholders in the business as to which metrics matter to them." (Insight Software)

"Tools designed to facilitate decision making and improve performance and accountability through collection, analysis, and reporting of relevant performance-related data." (NIST SP 800-55)

📊Business Intelligence: BI Boards (Definitions)

BICC: "Business Intelligence Competency Center, an organization concerned with managing and distributing data within a business as well as BI projects." (Fernando Iafrate, "From Big Data to Smart Data", 2015)

BICoE: "A permanent, cross-functional, virtual or physical organizational structure, loosely coupled for flexibility and agility, that is responsible for the governance and processes necessary to deliver or facilitate the delivery of successful BI solutions; it's also an institutional steward of, protector of, and forum for BI best practices." (Forrester)

BISC: "A permanent, cross-functional, virtual, or physical organizational structure, loosely coupled for flexibility and agility, responsible for the governance and processes necessary to deliver or facilitate the delivery of successful BI solutions, as well as an institutional steward of, protector of, and forum for BI best practices." (Forrester)

📊Business Intelligence: Reporting (Definitions)

"An automated business process or related functionality that provides a detailed, formal account of relevant or requested information." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

[enterprise reporting:] "1.The process of producing reports using unified views of enterprise data. 2.A category of software tools used to produce reports; a term for what were simply known as reporting tools." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

[ad hoc reporting:] "A reporting system that enables end users to run queries and create custom reports without having to know the technicalities of the underlying database schema and query syntax." (Microsoft, "SQL Server 2012 Glossary", 2012)

"A process by which insight is presented in a visually appealing and informative manner." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"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 descriptive analytics and business intelligence." (Brenda L Dietrich et al, "Analytics Across the Enterprise", 2014)

"The process of collecting data from various sources and presenting it to business people in an understandable way." (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

"A common interaction with an organizing system." (Robert J Glushko, "The Discipline of Organizing: Professional Edition" 4th Ed., 2016)

"The function or activity for generating documents that contain information organized in a narrative, graphic, or tabular form, often in a repeatable and regular fashion." (Jonathan Ferrar et al., 2017)

"Business intelligence reporting, or BI reporting, is the process of gathering data by utilizing different software and tools to extract relevant insights. Ultimately, it provides suggestions and observations about business trends, empowering decision-makers to act." (Data Pine) [source] 

"When we talk about reporting in business intelligence (BI), we are talking about two things. One is reporting strictly defined. The other is 'reporting' taken in a more general meaning. In the first case, reporting is the art of collecting data from various data sources and presenting it to end-users in a way that is understandable and ready to be analyzed. In the second sense, reporting means presenting data and information, so it also includes analysis–in other words, allowing end-users to both see and understand the data, as well as act on it." (Logi Analytics) [source] 

📊Business Intelligence: Report Model (Definitions)

"A semantic description of business entities and their relationships in a SQL Server Reporting Services solution. Used to create ad hoc reports through the Report Builder application." (Marilyn Miller-White et al, "MCITP Administrator: Microsoft® SQL Server™ 2005 Optimization and Maintenance 70-444", 2007)

"A 'blueprint' of a report. A report model includes the data source (such as a SQL Server database) and a data view (the tables and/or views that can be used in the report). Users can then use the report model to create their own reports, picking and choosing what data they want to include from the data view." (Robert D. Schneider and Darril Gibson, "Microsoft SQL Server 2008 All-In-One Desk Reference For Dummies", 2008)

"Report models are templates used to create reports with Report Builder. They include the data source definitions (such as which server and which database to connect to for the model) and data source VIEW definitions (such as which tables or VIEWs to include in the model). Reports can't be viewed from a report model. Instead, the report model must be used to create a report using Report Builder." (Darril Gibson, "MCITP SQL Server 2005 Database Developer All-in-One Exam Guide", 2008)

"A metadata description of business data used for creating ad hoc reports in Report Builder." (Jim Joseph, "Microsoft SQL Server 2008 Reporting Services Unleashed", 2009)

"A metadata description of business data used for creating ad hoc reports." (Microsoft, "SQL Server 2012 Glossary", 2012)

 "A metadata description of business data used for creating ad hoc reports in Report Builder." (Microsoft Technet)

📊Business Intelligence: Dashboards (Definitions)

"A dashboard is a visual display of the most important information needed to achieve one or more objectives; consolidated and arranged on a single screen so the information can be monitored at a glance." (Stephen Few, "Dashboard Confusion", Intelligent Enterprise, 2004)

Dashboard reports: "Highly summarized, often graphical, representations of the state of the business that are often used by executives and strategic decision makers." (Reed Jacobsen & Stacia Misner, "Microsoft SQL Server 2005 Analysis Services Step by Step", 2006)

Dashboard: "A means of providing information in a straightforward way. Like the part in a car it is named after, a business dashboard allows executives to see key metrics about anything from monthly sales to manufacturing downtime." (Tony Fisher, "The Data Asset", 2009)

Dashboard (also called performance dashboard): "The presentation of key business measurements on a single interface designed for quick interpretation, often using graphics. The most effective dashboards are supported by a full data mart that enables drilling down into more detailed data to better understand the indicators." (Laura Reeves, "A Manager's Guide to Data Warehousing", 2009)

Dashboard: "A visual display mechanism to enable business users at every level to receive the information they need to make better decisions that improve business performance." (Paulraj Ponniah, "Data Warehousing Fundamentals for IT Professionals", 2010)

Dashboard: "A BI tool that provides a comprehensive, at-a-glance view of corporate performance with graphical presentations, resembling a dashboard of a car. These graphical presentations show performance measures, trends, and exceptions, and integrate information from multiple business areas." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

Dashboard: "A technique to represent vast amounts of decision-support information at an amalgamated level using tabular and graphic representation, such as graphs and traffic lights." (Paul C Dinsmore et al, "Enterprise Project Governance", 2012)

Dashboards: "Business intelligence tools that display performance indicators, present data and information at both summary and detailed levels, and assist decision-makers employing them to act on the information they present." (Joan C Dessinger, "Fundamentals of Performance Improvement" 3rd Ed., 2012)

Dashboard: "A view that displays ranges of data in a graphical format. Key performance indicators (KPIs) or any element can be displayed in a dashboard. Each element is represented by a gauge that displays the data ranges that are defined. Links to comments, trend data, and element properties can also be provided." (Jim Davis & Aiman Zeid, "Business Transformation", 2014)

"[...] dashboards indicate the status of a performance metric at a given point in time. [...] dashboards are used to represent actual granular data, they contain data that is more recent than that of scorecards." (Saumya Chaki, "Enterprise Information Management in Practice", 2015)

Data dashboard: "A management-level online report capturing data conditions and trends."(Gregory Lampshire, "The Data and Analytics Playbook", 2016)

"A dashboard is a visual display of data used to monitor conditions and/or facilitate understanding."

(Steve Wexler et al, "The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios", 2017)

"A dashboard is a reporting tool that consolidates, aggregates and arranges measurements, metrics (measurements compared to a goal) and sometimes scorecards on a single screen so information can be monitored at a glance. Dashboards differ from scorecards in being tailored to monitor a specific role or generate metrics reflecting a particular point of view; typically they do not conform to a specific management methodology." (Information Management) [also (Intrafocus)] 

"Dashboards are a reporting mechanism that aggregate and display metrics and key performance indicators (KPIs), enabling them to be examined at a glance by all manner of users before further exploration via additional business analytics (BA) tools." (Gartner)

📊Business Intelligence: Trend Analysis (Definitions)

"The process of looking at homogeneous data over a duration of time to find recurring and predictable behavior." (Sharon Allen & Evan Terry, "Beginning Relational Data Modeling" 2nd Ed., 2005)

"the process of looking at homogeneous data over a spectrum of time." (William H Inmon, "Building the Data Warehouse", 2005)

"An analytical technique that uses mathematical models to forecast future outcomes based on historical results. It is a method of determining the variance from a baseline of a budget, cost, schedule, or scope parameter by using prior progress reporting periods' data and projecting how much that parameter's variance from baseline might be at some future point in the project if no changes are made in executing the project." (Cynthia Stackpole, "PMP® Certification All-in-One For Dummies®", 2011)

[trending] "A process by which underlying trends are identified within time-related data. These trends may be manually, algorithmically, or statistically identified and may be extrapolated into the future to aid planning." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"A form of statistical analysis used to analyze activities over a period of time." (Sally-Anne Pitt, "Internal Audit Quality", 2014)

"Using tools and statistics to identify consistent movement in one direction or another. The analysis might show a consistent upward trend or a consistent downward trend, but either way it indicates a change worth investigating." (Darril Gibson, "Effective Help Desk Specialist Skills", 2014)

"A data analysis technique that examines project performance over time to determine if performance is improving or deteriorating." (Cate McCoy & James L Haner, "CAPM Certified Associate in Project Management Practice Exams", 2018)

"An analytical technique that uses mathematical models to forecast future outcomes based on historical results." (Project Management Institute, "The Standard for Organizational Project Management (OPM)", 2018)

 "analysis of data to identify time-related patters" (ITIL)

📊Business Intelligence: Indicator (Definitions)

"An indicator is an objective feature or attribute supporting the implementation of a process. Indicators are used for the rating of process attributes. The indicators of the process dimension are base practices and work products; the indicators of the capability dimension are generic practices and generic resources." (Lars Dittmann et al, "Automotive SPICE in Practice", 2008)

"1.An attribute type that is considered to be binary: On or Off, True or False, Yes or No." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"A metric that you can use to predict the project’s future. For example, if the metric 'comments per KLOC' is 3, that may be an indicator that the project will be hard to maintain." (Rod Stephens, "Beginning Software Engineering", 2015)

"A measure that can be used to estimate or predict another measure." (ISO 14598)

📊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]