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06 February 2015
📊Business Intelligence: Dashboards (Definitions)
05 February 2015
📊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)
03 February 2015
📊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)
29 January 2015
📊Business Intelligence: Descriptive Analytics (Definitions)
27 January 2015
📊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]
25 January 2015
📊Business Intelligence: Prescriptive Analytics (Definitions)
20 January 2015
📊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)
17 January 2015
♜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)
15 January 2015
📊Business Intelligence: Key Performance Indicator [KPI] (Definitions)
📊Business Intelligence: Single Version of Truth [SVoT]/Single Source of Truth [SSoT] (Definitions)
10 January 2015
📊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]
05 January 2015
📊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)
02 January 2015
📊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.)
📊Business Intelligence: Business Intelligence [BI] (Definitions)
"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]
30 December 2014
🕸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)
About Me

- Adrian
- Koeln, NRW, Germany
- IT Professional with more than 25 years experience in IT in the area of full life-cycle of Web/Desktop/Database Applications Development, Software Engineering, Consultancy, Data Management, Data Quality, Data Migrations, Reporting, ERP implementations & support, Team/Project/IT Management, etc.