📊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)