♜Strategic Management: Strategy Map (Definitions)

"A strategy map is a tool that enables an organization to articulate its strategy through a series of cross-functional cause-and-effect relationships." (Ralph F Smith, "Business Process Management and the Balanced Scorecard: Using Processes as Strategic Drivers", 2007)

"A specific version of a strategy plan that adheres to the Balanced Scorecard methodology. Strategy maps depict objectives in multiple perspectives with corresponding cause and effect linkages." (Intrafocus)

"A strategy map is a visual representation of an organization’s overall objectives and how they relate to one another. The map is created during the strategic planning process and is used as a primary reference material during periodic strategy check-in and review meetings." (ClearPoint Strategy) [source]

"A Strategy Map provides a visual representation of the organization’s strategy. It is a powerful communication tool that enables employees to understand the company’s strategy and translate it into actions they can take, to ensure the achievement of strategic objectives." (The KPI Institute) [source] 

♜Strategic Management: Gap Analysis (Definitions)

"In the managerial planning process, this is the analysis taken following an exercise to determine what improvements in the process are required." (Robert McCrie, "Security Operations Management 2nd Ed.", 2006)

"An assessment of a system in comparison with another system or a set of requirements, listing those items that are not common between them." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"A technique to evaluate the current portfolio mix of components and determine changes needed so components may be added, changed, or terminated to rebalance the portfolio." (Project Management Institute, "The Standard for Portfolio Management" 3rd Ed., 2012)

"Describes the difference between current results and consequences and desired results and consequences." (Joan C Dessinger, "Fundamentals of Performance Improvement 3rd Ed", 2012)

"A formal analysis of the differences between what the policy or regulation requires and what’s actually being done in the organization. Used to generate a list of action items required to become compliant with the policy or regulation." (Mark Rhodes-Ousley, "Information Security: The Complete Reference" 2nd Ed., 2013)

"A comparison between the actual outcome and the desired outcome." (Weiss, "Auditing IT Infrastructures for Compliance" 2nd Ed., 2015)

♜Strategic Management: Competitive Advantage (Definitions)

"The relative advantage that one product or product line has over those products offered by other companies." (Steven Haines, "The Product Manager's Desk Reference", 2008)

"An advantage a company has over its competitors, which is gained by providing consumers with greater value through product or service offerings." (Linda Volonino & Efraim Turban, "Information Technology for Management 8th Ed", 2011)

"A strategic advantage held by one organization that cannot be matched by its competitors. This advantage may or may not be sustainable and, if not, may eventually be replicated by its competitors." (Evan Stubbs, "Big Data, Big Innovation", 2014)

"A strategy whereby companies position themselves ahead of competitors either by charging less or by differentiating their services or products from those of their rivals. " (DK, "The Business Book", 2014)

"The characteristics of an organization that differentiate it from other organizations in the same sector and that cannot easily be replicated. This differentiation may potentially provide the basis for competitive advantage. From a strategic management perspective it is the differentiated competences that are significant because they can be most easily managed strategically through training and development. Thus it is usually a distinctive network of competences that are most likely to provide competitive advantage. These distinctive competences are usually also core competences, but core competences are not necessarily distinctive." (Fran Ackermann et al, "Visual Strategy: Strategy Mapping for Public and Nonprofit Organizations", 2014)

"A sustainable, strategic advantage that an organization possesses over its industry rivals." (Andrew Pham et al, "From Business Strategy to Information Technology Roadmap", 2016)

"The unique set of assets, capabilities, positions and environmental circumstances that enable an organisation to consistently out-perform its competitors in its chosen strategic outcomes." (Duncan Angwin & Stephen Cummings, "The Strategy Pathfinder 3rd Ed.", 2017)

"A firm possesses a competitive advantage over its direct competitors when it earns (or has the potential to earn) a persistently higher rate of profit." (Robert M Grant, "Contemporary Strategy Analysis" 10th Ed., 2018)

♜Strategic Management: Governance (Definitions)

"Addresses the need for a mechanism to ensure compliance with the laws, policies, standards, and procedures under which an organization operates."  (Dominic Cadbury, "UK, Commission Report: Corporate Governance", 1992)

"Organizational chains of responsibility, authority, and communication for executing measurement and control mechanisms to effectively drive the organization and enable people to perform roles their respective roles and responsibilities." (Murray Cantor, "Estimation Variance and Governance", 2006) 

"In general, a term that describes the task of 'making sure that people do what’s right'." (Nicolai M Josuttis, "SOA in Practice", 2007)

"Addresses the need for a mechanism to ensure compliance with the laws, policies, standards, and procedures under which an organization operates." (Tilak Mitra et al, "SOA Governance", 2008)

"System by which organizations [or systems] are directed and controlled." (ISO/IEC, ISO/IEC 38500:2008 "Corporate governance of information technology" , 2008)

"The way we make and act on decisions about managing a shared resource for the common good. Resources can be people, processes, and technology." (Allen Dreibelbis et al, "Enterprise Master Data Management", 2008)

"(1) Planning, influencing, and conducting the decision-making affairs of an enterprise. (2) The processes and systems that ensure proper accountability for the conduct of an enterprise’s business." (David G Hill, "Data Protection: Governance, Risk Management, and Compliance", 2009)

"A kind of direction from a directive describing the boundaries and direction for a business process." (David C Hay, "Data Model Patterns: A Metadata Map", 2010)

"The 'checks-and-balances' method that keeps risks in check; a review of measurements, mitigation methods, and risk monitoring results over a period of time." (Annetta Cortez & Bob Yehling, "The Complete Idiot's Guide To Risk Management", 2010)

"The discipline of tracking, managing, and steering an IS/IT landscape. Architectural governance is concerned with change processes (design governance). Operational governance looks at the operational performance of systems against contracted performance levels, the definition of operational performance levels, and the implementation of systems that ensure the effective operation of systems." (David Lyle & John G Schmidt, "Lean Integration", 2010)

"[...] simultaneously refers to the art of governing, of running an enterprise and of defining its strategy. This term denotes the process of practicing this art, as well as the means implemented for governing: decision rules, suitable information, supervision and checks, relationships nurtured between leaders, administrators, employees and shareholders, where applicable. By extension, governance can be expanded to cover a wider circle, including for example suppliers." (Humbert Lesca & Nicolas Lesca, "Weak Signals for Strategic Intelligence: Anticipation Tool for Managers", 2011)

"Consistent management, cohesive policies, guidance, processes, and decision rights for a given area of responsibility. For example, corporate governance can involve policies on privacy, internal investment, and the use of data." (Craig S Mullins, "Database Administration", 2012)

"The process of managing change. Involves steering or directing the content, the people who create it, and the systems that support it through both the day-to-day and long-term content lifecycles." (Charles Cooper & Ann Rockley, "Managing Enterprise Content: A Unified Content Strategy" 2nd Ed., 2012)

"The ability to ensure that corporate or governmental rules and regulations are conformed with. Governance is combined with compliance and security issues across computing environments." (Marcia Kaufman et al, "Big Data For Dummies", 2013)

"The process of establishing and enforcing strategic goals and objectives, organizational policies, and performance parameters." (PMI, "Software Extension to the PMBOK® Guide" 5th Ed., 2013)

"Governance is the oversight of process, such as strategy or content life cycle, including policy and management." (Elaine Biech, "ASTD Handbook" 2nd Ed., 2014)

"Set of measurement, management, and steering processes for a business domain or IS that provides the expected level of result." (Gilbert Raymond & Philippe Desfray, "Modeling Enterprise Architecture with TOGAF", 2014)

"The combination of processes and structures implemented by the board to inform, direct, manage, and monitor the activities of the organization toward the achievement of its objectives." (Sally-Anne Pitt, "Internal Audit Quality", 2014)

"Set of measurement, management, and steering processes for a business domain or IS that provides the expected level of result." (Gilbert Raymond & Philippe Desfray, "Modeling Enterprise Architecture with TOGAF", 2014)

"The framework for directing and enabling an organization through its established policies, practices, and other relevant documentation." (Project Management Institute, "Navigating Complexity: A Practice Guide", 2014)

"The process of ensuring compliance with corporate or governmental rules, regulations, and policies. Governance is often associated with risk management and security activities across computing environments." (Judith S Hurwitz et al, "Cognitive Computing and Big Data Analytics", 2015)

"The process through which an organization’s processes and assets are directed and controlled." (Weiss, "Auditing IT Infrastructures for Compliance" 2nd Ed., 2015)

"A broad term referring to the establishment of policies and guidelines, along with continuous monitoring of their proper implementation, by the members of the governing body of an organization." (Jonathan Ferrar et al, "The Power of People", 2017)

"Consists of the systems by which the board ensures that its policies are being effectively implemented. Usually this includes systems to monitor and record what is happening, to identify instances in which policy is not being followed, and to take corrective action in those cases." (Marci S Thomas & Kim Strom-Gottfried, "Best of Boards" 2nd Ed., 2018)

"Generally refers to the management of the business organization itself. Includes the company’s organizing documents, the records of its owners and managers, and the steps required to maintain the company in good standing with the state where it is organized." (Alex D Bennett, "A Freelancer’s Guide to Legal Entities", 2018)

"The mechanisms by which decisions about the [semantic] model and its development, application and evolution are made and executed." (Panos Alexopoulos, "Semantic Modeling for Data", 2020)

"Ensures that stakeholder needs, conditions and options are evaluated to determine balanced, agreed on enterprise objectives to be achieved; setting direction through prioritization and decision making; and monitoring performance and compliance against agreed-on direction and objectives." (ISACA)

♜Strategic Management: Roadmap (Definitions)

"An abstracted plan for business or technology change, typically operating across multiple disciplines over multiple years." (David Lyle & John G Schmidt, "Lean Integration", 2010)

"Techniques that capture market trends, product launches, technology development, and competence building over time in a multilayer, consistent framework." (Gina C O'Connor & V K Narayanan, "Encyclopedia of Technology and Innovation Management", 2010)

"Defines the actions required to move from current to future (target) state. Similar to a high-level project plan." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

[portfolio roadmap:] "A document that provides the high-level strategic direction and portfolio information in a chronological fashion for portfolio management and ensures dependencies within the portfolio are established and evaluated." (Project Management Institute, "The Standard for Portfolio Management" 3rd Ed., 2012)

"Forward-looking plans intended to be taken by the security program over the foreseeable future." (Mark Rhodes-Ousley, "Information Security: The Complete Reference" 2nd Ed., 2013)

"Within the context of business analytics, a defined set of staged initiatives that deliver tactical returns while moving the team toward strategic outcomes." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"High-level action plan for change that will involve several facets of the enterprise (business, organization, technical)." (Gilbert Raymond & Philippe Desfray, "Modeling Enterprise Architecture with TOGAF", 2014)

"An action plan that matches the organization's business goals with specific technology solutions in order to help meet those goals." (David K Pham, "From Business Strategy to Information Technology Roadmap", 2016)

"The Roadmap is a schedule of events and Milestones that communicate planned Solution deliverables over a timeline. It includes commitments for the planned, upcoming Program Increment (PI) and offers visibility into the deliverables forecasted for the next few PIs." (Dean Leffingwell, "SAFe 4.5 Reference Guide: Scaled Agile Framework for Lean Enterprises" 2nd Ed., 2018)

"A product roadmap is a visual summary of a product’s direction to facilitate communication with customers, prospects, partners, and internal stakeholders." (Pendo) [source]

"A Roadmap is a plan to progress toward a set of defined goals. Depending on the purpose of the Roadmap, it may be either high-level or detailed. In terms of Enterprise Architecture, roadmaps are usually developed as abstracted plans for business or technology changes, typically operating across multiple disciplines over multiple years." (Orbus Software)

"A roadmap is a strategic plan that defines a goal or desired outcome and includes the major steps or milestones needed to reach it." (ProductPlan) [source]

♜Strategic Management: Risk Mitigation (Definitions)

"A planning process to identify, prevent, remove, or reduce risk if it occurs and define actions to limit the severity/impact of a risk, should it occur." (Lynne Hambleton, "Treasure Chest of Six Sigma Growth Methods, Tools, and Best Practices", 2007)

"The act of developing advance plans or taking immediate actions to minimize, or prevent known or unknown events (risks) from adversely impacting a strategy or business objective." (Steven G Haines, "The Product Manager's Desk Reference", 2008)

"A risk response strategy whereby the project team acts to reduce the probability of occurrence or impact of a threat. " (Project Management Institute, "The Standard for Portfolio Management" 3rd Ed., 2012)

"Reducing a risk by controlling its likelihood, its cost, or its threats, through the use of security measures designed to provide these controls." (Mark Rhodes-Ousley, "Information Security: The Complete Reference, Second Edition, 2nd Ed.", 2013)

"The process through which decisions are reached and protective measures are implemented for reducing risk to, or maintaining risks within, specified levels." (ISTQB)

♜Strategic Management: Business Strategy (Definitions)

"Business strategy is the determination of how a company will compete in a given business, and position itself among its competitors." (Kenneth R Andrews, "The Concept of Corporate Strategy", 1980)

"The organization's business strategy is a set of objectives, plans, and policies for the organization to compete successfully in its markets. In effect, the business strategy specifies what an organization's competitive will be and how this advantage will be and sustained." (Scott M Shafer & ‎Jack R Meredith, "Introducing Operations Management: Wall Street Journal", 2003)

"A business strategy is a set of guiding principles that, when communicated and adopted in the organization, generates a desired pattern of decision making. A strategy is therefore about how people throughout the organization should make decisions and allocate resources in order accomplish key objectives." (Michael D Watkins, "Demystifying Strategy: The What, Who, How, and Why", Harvard Business Review, 2007) [source]

"A business strategy identifies how a division or strategic business unit will compete in its product or service domain." (John R Schermerhorn Jr, "Management" 12th Ed., 2012)

"Business strategy is essentially the art and science of formulating. plans to align resources, overcome challenges, and achieve stated objectives." (Carl F Lehman, "Strategy and Business Process Management", 2012)

"Business strategy is the strategic initiatives a company pursues to create value for the organization and its stakeholders and gain a competitive advantage in the market." (Michael Boyles, "What is business strategy & why is it important?", Harvard Business School Online, 2022) [link]

♜Strategic Management: Balanced Scorecard (Definitions)

"An evaluation method, created by Robert Kaplan and David Norton, that consists of four perspectives (customer, learning, business, and financial) and is used to evaluate effectiveness." (Teri Lund & Susan Barksdale, "10 Steps to Successful Strategic Planning", 2006)

"A strategic management system that connects activities to strategic goals and measures how much the activities contribute to achieving those goals. It provides a broader view of the business than merely looking at financial data. Devised by management theorists Robert Kaplan and David Norton." (Steve Williams & Nancy Williams, "The Profit Impact of Business Intelligence", 2007)

"A type of scorecard application that tracks an organization's progress from various perspectives simultaneously." (Ken Withee, "Microsoft® Business Intelligence For Dummies®", 2010)

"A formal approach used to help organizations translate their vision into objectives that can be measured and monitored using both financial and non-financial performance measures." (Leslie G Eldenburg & Susan K. Wolcott, "Cost Management" 2nd Ed., 2011)

"A performance measurement approach that links business goals to performance metrics." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"A management tool that measures and manages an organization's progress toward strategic goals and objectives. Incorporates financial indicators with three other perspectives: customer, internal business processes, and learning and growth." (Joan C Dessinger, "Fundamentals of Performance Improvement" 3rd Ed., 2012)

"A balanced scorecard tallies organizational performance in financial, customer service, internal process, and innovation and learning areas." (John R Schermerhorn Jr, "Management" 12th Ed., 2012)

"First proposed by Kaplan and Norton in 1992, the balanced scorecard focused on translating strategy into actions, and promoted a move away from traditional financial measures. Instead, organizations were encouraged to develop a broad range of financial and nonfinancial lead and lag measures that provided insight into overall operating performance." (Sally-Anne Pitt, "Internal Audit Quality", 2014)

"One of the widely adopted performance management frameworks is the balanced scorecard technique designed by Kaplan and Norton. Balanced scorecards involve looking at an enterprise (private, public, or nonprofit) through four perspectives: financial, customer, learning and growth, and operations." (Saumya Chaki, "Enterprise Information Management in Practice", 2015)

"A tool for linking strategic goals to performance indicators. These performance indicators combine performance indicators relating to financial performance, consumer satisfaction, internal efficiency, and learning and innovation." (Robert M Grant, "Contemporary Strategy Analysis" 10th Ed., 2018)

"A balanced scorecard (BSC) is a performance measurement and management approach that recognizes that financial measures by themselves are not sufficient and that an enterprise needs a more holistic, balanced set of measures which reflects the different drivers that contribute to superior performance and the achievement of the enterprise’s strategic goals. The balanced scorecard is driven by the premise that there is a cause-and-effect link between learning, internal efficiencies and business processes, customers, and financial results." (Gartner)

"A strategic tool for measuring whether the operational activities of a company are aligned with its objectives in terms of business vision and strategy." (ISQTB)

"An integrated framework for describing strategy through the use of linked performance measures in four, balanced perspectives ‐ Financial, Customer, Internal Process, and Employee Learning and Growth. The Balanced Scorecard acts as a measurement system, strategic management system, and communication tool." (Intrafocus) 

♜Strategic Management: Risk Management (Definitions)

"An organized, analytic process to identify what might cause harm or loss (identify risks); to assess and quantify the identified risks; and to develop and, if needed, implement an appropriate approach to prevent or handle causes of risk that could result in significant harm or loss." (Sandy Shrum et al, "CMMI: Guidelines for Process Integration and Product Improvement", 2003)

"The organized, analytic process to identify future events (risks) that might cause harm or loss, assess and quantify the identified risks, and decide if, how, and when to prevent or reduce the risk. Also includes the implementation of mitigation actions at the appropriate times." (Richard D Stutzke, "Estimating Software-Intensive Systems: Projects, Products, and Processes", 2005)

"Identifying a situation or problem that may put specific plans or outcomes in jeopardy, and then organizing actions to mitigate it." (Teri Lund & Susan Barksdale, "10 Steps to Successful Strategic Planning", 2006)

"The process of identifying hazards of property insured; the casualty contemplated in a specific contract of insurance; the degree of hazard; a specific contingency or peril. Generally not the same as security management, but may be related in concerns and activities. Work is done by a risk manager." (Robert McCrie, "Security Operations Management" 2nd Ed., 2006)

"Systematic application of procedures and practices to the tasks of identifying, analyzing, prioritizing, and controlling risk." (Tilo Linz et al, "Software Testing Practice: Test Management", 2007)

"Risk management is a continuous process to be performed throughout the entire life of a project, and an important part of project management activities. The objective of risk management is to identify and prevent risks, to reduce their probability of occurrence, or to mitigate the effects in case of risk occurrence." (Lars Dittmann et al, "Automotive SPICE in Practice", 2008)

"A structured process for managing risk." (David G Hill, "Data Protection: Governance, Risk Management, and Compliance", 2009)

"The process organizations employ to reduce different types of risks. A company manages risk to avoid losing money, protect against breaking government or regulatory body rules, or even assure that adverse weather does not interrupt the supply chain." (Tony Fisher, "The Data Asset", 2009)

"Systematic application of procedures and practices to the tasks of identifying, analyzing, prioritizing, and controlling risk." (IQBBA, "Standard glossary of terms used in Software Engineering", 2011)

"The process of identifying what can go wrong, determining how to respond to risks should they occur, monitoring a project for risks that do occur, and taking steps to respond to the events that do occur." (Bonnie Biafore, "Successful Project Management: Applying Best Practices and Real-World Techniques with Microsoft® Project", 2011)

"Risk management is using managerial resources to integrate risk identification, risk assessment, risk prioritization, development of risk-handling strategies, and mitigation of risk to acceptable levels (ASQ)." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement ", 2012)

"The process of identifying negative and positive risks to a project, analyzing the likelihood and impact of those risks, planning responses to higher priority risks, and tracking risks." (Bonnie Biafore & Teresa Stover, "Your Project Management Coach: Best Practices for Managing Projects in the Real World", 2012)

"A policy of determining the greatest potential failure associated with a project." (James Robertson et al, "Complete Systems Analysis: The Workbook, the Textbook, the Answers", 2013)

"Controlling vulnerabilities, threats, likelihood, loss, or impact with the use of security measures. See also risk, threat, and vulnerability." (Mark Rhodes-Ousley, "Information Security: The Complete Reference, Second Edition" 2nd Ed., 2013)

"A process to identify, assess, manage, and control potential events or situations to provide reasonable assurance regarding the achievement of the organization's objectives." (Sally-Anne Pitt, "Internal Audit Quality", 2014)

"Managing the financial impacts of unusual events." (Manish Agrawal, "Information Security and IT Risk Management", 2014)

"Systematic application of policies, procedures, methods and practices to the tasks of identifying, analysing, evaluating, treating and monitoring risk." (Chartered Institute of Building, "Code of Practice for Project Management for Construction and Development, 5th Ed.", 2014)

"The coordinated activities to direct and control an organisation with regard to risk." (David Sutton, "Information Risk Management: A practitioner’s guide", 2014)

"The process of reducing risk to an acceptable level by implementing security controls. Organizations implement risk management programs to identify risks and methods to reduce it. The risk that remains after risk has been mitigated to an acceptable level is residual risk." (Darril Gibson, "Effective Help Desk Specialist Skills", 2014)

"Risk management is a structured approach to monitoring, meas­uring, and managing exposures to reduce the potential impact of an uncertain happening." (Christopher Donohue et al, "Foundations of Financial Risk: An Overview of Financial Risk and Risk-based Financial Regulation, 2nd Ed", 2015)

"Systematic application of procedures and practices to the tasks of identifying, analyzing, prioritizing, and controlling risk. " (ISTQB, "Standard Glossary", 2015)

"The practice of identifying, assessing, controlling, and mitigating risks. Techniques to manage risk include avoiding, transferring, mitigating, and accepting the risk." (Weiss, "Auditing IT Infrastructures for Compliance, 2nd Ed", 2015)

"The discipline and methods used to quantify, track, and reduce where possible various types of defined risk." (Gregory Lampshire, "The Data and Analytics Playbook", 2016)

"The process of identifying individual risks, understanding and analyzing them, and then managing them." (Paul H Barshop, "Capital Projects", 2016)

"Coordinated activities to direct and control an organization with regard to risk." (William Stallings, "Effective Cybersecurity: A Guide to Using Best Practices and Standards", 2018)

"Process of identifying and monitoring business risks in a manner that offers a risk/return relationship that is acceptable to an entity's operating philosophy." (Tom Klammer, "Statement of Cash Flows: Preparation, Presentation, and Use", 2018)

"Coordinated activities to direct and control an organisation with regard to risk." (ISO Guide 73:2009)

"Risk management is the identification, assessment and prioritisation of risks [...] followed by coordinated and economical application of resources to minimise, monitor and control the probability and/or impact of unfortunate events or to maximise the realisation of opportunities." (David Sutton, "Information Risk Management: A practitioner’s guide", 2014)

♜Strategic Management: Enterprise Architecture (Definitions)

"[Enterprise Architecture is] the set of descriptive representations (i. e., models) that are relevant for describing an Enterprise such that it can be produced to management's requirements (quality) and maintained over the period of its useful life. (John Zachman, 1987)

"An enterprise architecture is an abstract summary of some organizational component's design. The organizational strategy is the basis for deciding where the organization wants to be in three to five years. When matched to the organizational strategy, the architectures provide the foundation for deciding priorities for implementing the strategy." (Sue A Conger, "The new software engineering", 1994)

"An enterprise architecture is a snapshot of how an enterprise operates while performing its business processes. The recognition of the need for integration at all levels of an organisation points to a multi-dimensional framework that links both the business processes and the data requirements." (John Murphy & Brian Stone [Eds.], 1995)

"The Enterprise Architecture is the explicit description of the current and desired relationships among business and management process and information technology. It describes the 'target' situation which the agency wishes to create and maintain by managing its IT portfolio." (Franklin D Raines, 1997)

"Enterprise architecture is a family of related architecture components. This include information architecture, organization and business process architecture, and information technology architecture. Each consists of architectural representations, definitions of architecture entities, their relationships, and specification of function and purpose. Enterprise architecture guides the construction and development of business organizations and business processes, and the construction and development of supporting information systems." (Gordon B Davis, "The Blackwell encyclopedic dictionary of management information systems"‎, 1999)

"Enterprise architecture is a holistic representation of all the components of the enterprise and the use of graphics and schemes are used to emphasize all parts of the enterprise, and how they are interrelated." (Gordon B Davis," The Blackwell encyclopedic dictionary of management information systems"‎, 1999)

"Enterprise Architecture is the discipline whose purpose is to align more effectively the strategies of enterprises together with their processes and their resources (business and IT)." (Alain Wegmann, "On the systemic enterprise architecture methodology", 2003)

"An enterprise architecture is a blueprint for organizational change defined in models [using words, graphics, and other depictions] that describe (in both business and technology terms) how the entity operates today and how it intends to operate in the future; it also includes a plan for transitioning to this future state." (US Government Accountability Office, "Enterprise Architecture: Leadership Remains Key to Establishing and Leveraging Architectures for Organizational Transformation", GAO-06-831, 2006)

"Enterprise architecture is the organizing logic for business processes and IT infrastructure reflecting the integration and standardization requirements of a company's operation model." (Jeanne W. Ross et al, "Enterprise architecture as strategy: creating a foundation for business", 2006)

"Enterprise-architecture is the integration of everything the enterprise is and does." (Tom Graves, "Real Enterprise-Architecture : Beyond IT to the whole enterprise", 2007)

"Enterprise architecture is the organizing logic for business processes and IT infrastructure reflecting the integration and standardization requirements of the company's operating model. The operating model is the desired state of business process integration and business process standardization for delivering goods and services to customers." (Peter Weill, "Innovating with Information Systems Presentation", 2007)

"Enterprise architecture is the process of translating business vision and strategy into effective enterprise change by creating, communicating and improving the key requirements, principles and models that describe the enterprise's future state and enable its evolution. The scope of the enterprise architecture includes the people, processes, information and technology of the enterprise, and their relationships to one another and to the external environment. Enterprise architects compose holistic solutions that address the business challenges of the enterprise and support the governance needed to implement them." (Anne Lapkin et al, "Gartner Clarifies the Definition of the Term 'Enterprise Architecture", 2008)

"Enterprise architecture [is] a coherent whole of principles, methods, and models that are used in the design and realisation of an enterprise's organisational structure, business processes, information systems, and infrastructure." (Marc Lankhorst, "Enterprise Architecture at Work: Modelling, Communication and Analysis", 2009)

"Enterprise architecture (EA) is the definition and representation of a high-level view of an enterprise‘s business processes and IT systems, their interrelationships, and the extent to which these processes and systems are shared by different parts of the enterprise. EA aims to define a suitable operating platform to support an organisation‘s future goals and the roadmap for moving towards this vision." (Toomas Tamm et al, "How Does Enterprise Architecture Add Value to Organisations?", Communications of the Association for Information Systems Vol. 28 (10), 2011)

"Enterprise architecture (EA) is a discipline for proactively and holistically leading enterprise responses to disruptive forces by identifying and analyzing the execution of change toward desired business vision and outcomes. EA delivers value by presenting business and IT leaders with signature-ready recommendations for adjusting policies and projects to achieve target business outcomes that capitalize on relevant business disruptions. EA is used to steer decision making toward the evolution of the future state architecture." (Gartner)

"Enterprise Architecture [...] is a way of thinking enabled by patterns, frameworks, standards etc. essentially seeking to align both the technology ecosystem and landscape with the business trajectory driven by both the internal and external forces." (Daljit R Banger)

♜Strategic Management: Strategy (Definitions)

"Strategy can be defined as the determination of the long-term goals and objectives of an enterprise, and the adoption of courses of action and the allocation of resources necessary for carrying out these goals." (Alfred D. Chandler Jr., "Strategy and Structure", 1962)

"Strategy is the pattern of objectives, purposes or goals and major policies and plans for achieving these goals, stated in such a way as to define what businesses the company is in or is to be in and the kind of company it is or is to be." (Edmund P Learned et al, "Business Policy: Text and Cases", 1965)

"Strategies are forward-looking plans that anticipate change and initiate actions to take advantage of opportunities that are integrated into the concept or mission of the company." (William A Newman & J. P Logan, "Strategy, Policy, and Central Management", 1971) 

"Strategy is the basic goals and objectives of the organization, the major programs of action chosen to reach these goals and objectives, and the major pattern of resource allocation used to relate the organization to its environment." (Dan E Schendel & K J Hatten, "Business Policy or Strategic Management: A View for an Emerging Discipline", 1972)

"Strategy is a unified, comprehensive, and integrative plan designed to assure that the basic objectives of the enterprise are achieved." (William F Glueck, "Business Policy, Strategy Formation, and Management Action", 1976) 

"Strategy is the forging of company missions, setting objectives for the organization in light of external and internal forces, formulating specific policies and strategies to achieve objectives, and ensuring their proper implementation so that the basic purposes and objectives of the organization will be achieved." (George A  Steiner & John B. Miner,"Management Policy and Strategy", 1977)

"Strategy is a mediating force between the organization and its environment: consistent patterns of streams of organizational decisions to deal with the environment." (Henry Mintzberg, "The Structuring of Organizations", 1979)

"Strategy is defined as orienting 'metaphases' or frames of reference that allow the organization and its environment to be understood by organizational stakeholders. On this basis, stakeholders are motivated to believe and to act in ways that are expected to produce favorable results for the organization." (Ellen E Chaffee, "Three Models of Strategy," Academy of Management Review Vol. 10 (1), 1985) 

"Strategy is the creation of a unique and valuable position, involving a different set of activities. [...] Strategy is creating fit among a company’s activities." (Michael E Porter, "What is Strategy?", Harvard Business Review, 1996)

"General direction set for the organization and its various components to achieve a desired state in the future, resulting from the detailed strategic planning process." (Alan Wa Steiss, "Strategic Management for Public and Nonprofit Organizations", 2003)

"An organization's overall plan of development, describing the effective use of resources in support of the organization in its future activities. It involves setting objectives and proposing initiatives for action." (ISO/IEC 38500:2008, 2008)

"An organized set of initiation programs and projects undertaken in order to achieve the organization ’ s vision." (Terry Schimidt, "Strategic Management Made Simple", 2009)

"This is a plan of action stating how an organisation will achieve its long-term objectives." (Bernard Burnes, "Managing change : a strategic approach to organisational dynamics" 5th Ed., 2009)

"The essential course of action attempted to achieve an enterprise’s end - particularly goals. Moreover, a strategy must be to carry out exactly one mission. In general, strategies address goals, and tactics address objectives." (David C Hay, "Data Model Patterns: A Metadata Map", 2010)

"A broad-based formula for how a business is going to accomplish its mission, what its goals should be, and what plans and policies will be needed to carry out those goals."  (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"denotes, by an extension of military language, the development of a policy by the enterprise (its objectives, structure, and operation), defined on one hand on the basis of its strengths and weaknesses and, on the other hand, taking into account threats and opportunities identified in its environment." (Humbert Lesca & Nicolas Lesca, "Weak Signals for Strategic Intelligence", 2011)

"A comprehensive plan that states how a corporation will achieve its mission and objectives." (Thomas L Wheelen & J David Hunger., "Strategic management and business policy: toward global sustainability" 13th Ed., 2012)

"The proposed direction an organization will achieve over the long term, through the configuration of resources in a challenging environment, to meet the needs of markets and to fulfill stakeholder expectations." (Paul C Dinsmore et al, "Enterprise Project Governance", 2012)

"A strategy is a comprehensive plan guiding resource allocation to achieve long-term organization goals." (John R Schermerhorn Jr, "Management" 12th Ed., 2012)

"The definition of the model’s goals, the high-level approach to achieve these goals, and the decision making mechanisms to execute this approach." (Panos Alexopoulos, "Semantic Modeling for Data", 2020)

"Strategy is a style of thinking, a conscious and deliberate process, an intensive implementation system, the science of insuring future success." (Pete Johnson)

"Strategy is the way an organization seeks to achieve its vision and mission. It is a forward-looking statement about an organization’s planned use of resources and deployment capabilities. Strategy becomes real when it is associated with: 1) a concrete set of goals and objectives; and 2) a method involving people, resources and processes." (Intrafocus)

🪙Business Intelligence: Complexity (Just the Quotes)

"The more complex the shape of any object. the more difficult it is to perceive it. The nature of thought based on the visual apprehension of objective forms suggests, therefore, the necessity to keep all graphics as simple as possible. Otherwise, their meaning will be lost or ambiguous, and the ability to convey the intended information and to persuade will be inhibited." (Robert Lefferts, "Elements of Graphics: How to prepare charts and graphs for effective reports", 1981)

"Once these different measures of performance are consolidated into a single number, that statistic can be used to make comparisons […] The advantage of any index is that it consolidates lots of complex information into a single number. We can then rank things that otherwise defy simple comparison […] Any index is highly sensitive to the descriptive statistics that are cobbled together to build it, and to the weight given to each of those components. As a result, indices range from useful but imperfect tools to complete charades." (Charles Wheelan, "Naked Statistics: Stripping the Dread from the Data", 2012)

"The urge to tinker with a formula is a hunger that keeps coming back. Tinkering almost always leads to more complexity. The more complicated the metric, the harder it is for users to learn how to affect the metric, and the less likely it is to improve it." (Kaiser Fung, "Numbersense: How To Use Big Data To Your Advantage", 2013)

"Any presentation of data, whether a simple calculated metric or a complex predictive model, is going to have a set of assumptions and choices that the producer has made to get to the output. The more that these can be made explicit, the more the audience of the data will be open to accepting the message offered by the presenter." (Zach Gemignani et al, "Data Fluency", 2014)

"Decision trees are also considered nonparametric models. The reason for this is that when we train a decision tree from data, we do not assume a fixed set of parameters prior to training that define the tree. Instead, the tree branching and the depth of the tree are related to the complexity of the dataset it is trained on. If new instances were added to the dataset and we rebuilt the tree, it is likely that we would end up with a (potentially very) different tree." (John D Kelleher et al, "Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies", 2015)

"When datasets are small, a parametric model may perform well because the strong assumptions made by the model - if correct - can help the model to avoid overfitting. However, as the size of the dataset grows, particularly if the decision boundary between the classes is very complex, it may make more sense to allow the data to inform the predictions more directly. Obviously the computational costs associated with nonparametric models and large datasets cannot be ignored. However, support vector machines are an example of a nonparametric model that, to a large extent, avoids this problem. As such, support vector machines are often a good choice in complex domains with lots of data." (John D Kelleher et al, "Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies", 2015)

"The tension between bias and variance, simplicity and complexity, or underfitting and overfitting is an area in the data science and analytics process that can be closer to a craft than a fixed rule. The main challenge is that not only is each dataset different, but also there are data points that we have not yet seen at the moment of constructing the model. Instead, we are interested in building a strategy that enables us to tell something about data from the sample used in building the model." (Jesús Rogel-Salazar, "Data Science and Analytics with Python", 2017) 

"Data lake architecture suffers from complexity and deterioration. It creates complex and unwieldy pipelines of batch or streaming jobs operated by a central team of hyper-specialized data engineers. It deteriorates over time. Its unmanaged datasets, which are often untrusted and inaccessible, provide little value. The data lineage and dependencies are obscured and hard to track." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"Decision-makers are constantly provided data in the form of numbers or insights, or similar. The challenge is that we tend to believe every number or piece of data we hear, especially when it comes from a trusted source. However, even if the source is trusted and the data is correct, insights from the data are created when we put it in context and apply meaning to it. This means that we may have put incorrect meaning to the data and then made decisions based on that, which is not ideal. This is why anyone involved in the process needs to have the skills to think critically about the data, to try to understand the context, and to understand the complexity of the situation where the answer is not limited to just one specific thing. Critical thinking allows individuals to assess limitations of what was presented, as well as mitigate any cognitive bias that they may have." (Angelika Klidas & Kevin Hanegan, "Data Literacy in Practice", 2022)

🪙Business Intelligence: Measurement (Just the Quotes)

"There is no inquiry which is not finally reducible to a question of Numbers; for there is none which may not be conceived of as consisting in the determination of quantities by each other, according to certain relations." (Auguste Comte, “The Positive Philosophy”, 1830)

"When you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot express it in numbers, your knowledge is of a meager and unsatisfactory kind; it may be the beginning of knowledge, but you have scarcely in your thoughts advanced to the state of science.” (Lord Kelvin, "Electrical Units of Measurement", 1883)

“Of itself an arithmetic average is more likely to conceal than to disclose important facts; it is the nature of an abbreviation, and is often an excuse for laziness.” (Arthur Lyon Bowley, “The Nature and Purpose of the Measurement of Social Phenomena”, 1915)

“Science depends upon measurement, and things not measurable are therefore excluded, or tend to be excluded, from its attention.” (Arthur J Balfour, “Address”, 1917)

“It is important to realize that it is not the one measurement, alone, but its relation to the rest of the sequence that is of interest.” (William E Deming, “Statistical Adjustment of Data”, 1943)

“The purpose of computing is insight, not numbers […] sometimes […] the purpose of computing numbers is not yet in sight.” (Richard Hamming, “Numerical Methods for Scientists and Engineers”, 1962)

“A quantity like time, or any other physical measurement, does not exist in a completely abstract way. We find no sense in talking about something unless we specify how we measure it. It is the definition by the method of measuring a quantity that is the one sure way of avoiding talking nonsense...” (Hermann Bondi, “Relativity and Common Sense”, 1964)

“Measurement, we have seen, always has an element of error in it. The most exact description or prediction that a scientist can make is still only approximate.” (Abraham Kaplan, “The Conduct of Inquiry: Methodology for Behavioral Science”, 1964)

“A mature science, with respect to the matter of errors in variables, is not one that measures its variables without error, for this is impossible. It is, rather, a science which properly manages its errors, controlling their magnitudes and correctly calculating their implications for substantive conclusions.” (Otis D Duncan, “Introduction to Structural Equation Models”, 1975)

“Data in isolation are meaningless, a collection of numbers. Only in context of a theory do they assume significance […]” (George Greenstein, “Frozen Star”, 1983)

"Changing measures are a particularly common problem with comparisons over time, but measures also can cause problems of their own. [...] We cannot talk about change without making comparisons over time. We cannot avoid such comparisons, nor should we want to. However, there are several basic problems that can affect statistics about change. It is important to consider the problems posed by changing - and sometimes unchanging - measures, and it is also important to recognize the limits of predictions. Claims about change deserve critical inspection; we need to ask ourselves whether apples are being compared to apples - or to very different objects." (Joel Best, "Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists", 2001)

"Measurement is often associated with the objectivity and neatness of numbers, and performance measurement efforts are typically accompanied by hope, great expectations and promises of change; however, these are then often followed by disbelief, frustration and what appears to be sheer madness." (Dina Gray et al, "Measurement Madness: Recognizing and avoiding the pitfalls of performance measurement", 2015)

"Measuring anything subjective always prompts perverse behavior. [...] All measurement systems are subject to abuse." (Kaiser Fung, "Numbersense: How To Use Big Data To Your Advantage", 2013)

“The value of having numbers - data - is that they aren't subject to someone else's interpretation. They are just the numbers. You can decide what they mean for you.” (Emily Oster, “Expecting Better”, 2013)

"Until a new metric generates a body of data, we cannot test its usefulness. Lots of novel measures hold promise only on paper." (Kaiser Fung, "Numbersense: How To Use Big Data To Your Advantage", 2013)

"Usually, it is impossible to restate past data. As a result, all history must be whitewashed and measurement starts from scratch." (Kaiser Fung, "Numbersense: How To Use Big Data To Your Advantage", 2013)

🪙Business Intelligence: Data Mesh (Just the quotes)

"Another myth is that we shall have a single source of truth for each concept or entity. […] This is a wonderful idea, and is placed to prevent multiple copies of out-of-date and untrustworthy data. But in reality it’s proved costly, an impediment to scale and speed, or simply unachievable. Data Mesh does not enforce the idea of one source of truth. However, it places multiple practices in place that reduces the likelihood of multiple copies of out-of-date data." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"Data Mesh attempts to strike a balance between team autonomy and inter-term interoperability and collaboration, with a few complementary techniques. It gives domain teams autonomy to have control of their local decision making, such as choosing the best data model for their data products. While it uses the computational governance policies to impose a consistent experience across all data products; for example, standardizing on the data modeling language that all domains utilize." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"Data mesh is a solution for organizations that experience scale and complexity, where existing data warehouse or lake solutions have become blockers in their ability to get value from data at scale and across many functions of their business, in a timely fashion and with less friction." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"Data Mesh must allow for data models to change continuously without fatal impact to downstream data consumers, or slowing down access to data as a result of synchronizing change of a shared global canonical model. Data Mesh achieves this by localizing change to domains by providing autonomy to domains to model their data based on their most intimate understanding of the business without the need for central coordinations of change to a single shared canonical model." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"Data mesh [...] reduces points of centralization that act as coordination bottlenecks. It finds a new way of decomposing the data architecture without slowing the organization down with synchronizations. It removes the gap between where the data originates and where it gets used and removes the accidental complexities - aka pipelines - that happen in between the two planes of data. Data mesh departs from data myths such as a single source of truth, or one tightly controlled canonical data model." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"Data mesh relies on a distributed architecture that consists of domains. Each domain is an independent unit of data and its associated storage and compute components. When an organization contains various product units, each with its own data needs, each product team owns a domain that is operated and governed independently by the product team. […] Data mesh has a unique value proposition, not just offering scale of infrastructure and scenarios but also helping shift the organization’s culture around data," (Rukmani Gopalan, "The Cloud Data Lake: A Guide to Building Robust Cloud Data Architecture", 2022)

"Data has historically been treated as a second-class citizen, as a form of exhaust or by-product emitted by business applications. This application-first thinking remains the major source of problems in today’s computing environments, leading to ad hoc data pipelines, cobbled together data access mechanisms, and inconsistent sources of similar-yet-different truths. Data mesh addresses these shortcomings head-on, by fundamentally altering the relationships we have with our data. Instead of a secondary by-product, data, and the access to it, is promoted to a first-class citizen on par with any other business service." (Adam Bellemare,"Building an Event-Driven Data Mesh: Patterns for Designing and Building Event-Driven Architectures", 2023)

"Data mesh architectures are inherently decentralized, and significant responsibility is delegated to the data product owners. A data mesh also benefits from a degree of centralization in the form of data product compatibility and common self-service tooling. Differing opinions, preferences, business requirements, legal constraints, technologies, and technical debt are just a few of the many factors that influence how we work together." (Adam Bellemare, "Building an Event-Driven Data Mesh: Patterns for Designing and Building Event-Driven Architectures", 2023)

"The data mesh is an exciting new methodology for managing data at large. The concept foresees an architecture in which data is highly distributed and a future in which scalability is achieved by federating responsibilities. It puts an emphasis on the human factor and addressing the challenges of managing the increasing complexity of data architectures." (Piethein Strengholt, "Data Management at Scale: Modern Data Architecture with Data Mesh and Data Fabric" 2nd Ed., 2023)

"A data mesh splits the boundaries of the exchange of data into multiple data products. This provides a unique opportunity to partially distribute the responsibility of data security. Each data product team can be made responsible for how their data should be accessed and what privacy policies should be applied." (Aniruddha Deswandikar,"Engineering Data Mesh in Azure Cloud", 2024)

"A data mesh is a decentralized data architecture with four specific characteristics. First, it requires independent teams within designated domains to own their analytical data. Second, in a data mesh, data is treated and served as a product to help the data consumer to discover, trust, and utilize it for whatever purpose they like. Third, it relies on automated infrastructure provisioning. And fourth, it uses governance to ensure that all the independent data products are secure and follow global rules."(James Serra, "Deciphering Data Architectures", 2024)

"At its core, a data fabric is an architectural framework, designed to be employed within one or more domains inside a data mesh. The data mesh, however, is a holistic concept, encompassing technology, strategies, and methodologies." (James Serra, "Deciphering Data Architectures", 2024)

"It is very important to understand that data mesh is a concept, not a technology. It is all about an organizational and cultural shift within companies. The technology used to build a data mesh could follow the modern data warehouse, data fabric, or data lakehouse architecture - or domains could even follow different architectures." (James Serra, "Deciphering Data Architectures", 2024)

"To explain a data mesh in one sentence, a data mesh is a centrally managed network of decentralized data products. The data mesh breaks the central data lake into decentralized islands of data that are owned by the teams that generate the data. The data mesh architecture proposes that data be treated like a product, with each team producing its own data/output using its own choice of tools arranged in an architecture that works for them. This team completely owns the data/output they produce and exposes it for others to consume in a way they deem fit for their data." (Aniruddha Deswandikar,"Engineering Data Mesh in Azure Cloud", 2024)

"With all the hype, you would think building a data mesh is the answer to all of these 'problems' with data warehousing. The truth is that while data warehouse projects do fail, it is rarely because they can’t scale enough to handle big data or because the architecture or the technology isn’t capable. Failure is almost always because of problems with the people and/or the process, or that the organization chose the completely wrong technology." (James Serra, "Deciphering Data Architectures", 2024)

🪙Business Intelligence: Data Lakes (Just the Quotes)

"If you think of a Data Mart as a store of bottled water, cleansed and packaged and structured for easy consumption, the Data Lake is a large body of water in a more natural state. [...] The contents of the Data Lake stream in from a source to fill the lake, and various users of the lake can come to examine, dive in, or take samples." (James Dixon, "Pentaho, Hadoop, and Data Lakes", 2010) [sorce] [first known usage]

"A data lake represents an environment that collects and stores large volumes of structured and unstructured datasets, typically in their original, unaltered forms. More than a data depository, the data lake architecture enables the various users and data science teams to conduct data exploration and related analytical activities." (EMC Education Services, "Data Science & Big Data Analytics", 2015)

"A data lake strategy supports the introduction of a separate analytics environment that off-loads the analytics being done today on your overly expensive data warehouse. This separate analytics environment provides the data science team an on-demand, fail-fast environment for quickly ingesting and analyzing a wide variety of data sources in an attempt to address immediate business opportunities independent of the data warehouse's production schedule and service level agreement (SLA) rules." (Billl Schmarzo, "Driving Business Strategies with Data Science: Big Data MBA" 1st Ed., 2015)

"At its core, it is a data storage and processing repository in which all of the data in an organization can be placed so that every internal and external systems', partners', and collaborators' data flows into it and insights spring out. [...] Data Lake is a huge repository that holds every kind of data in its raw format until it is needed by anyone in the organization to analyze." (Beulah S Purra & Pradeep Pasupuleti, "Data Lake Development with Big Data", 2015) 

"Having multiple data lakes replicates the same problems that were created with multiple data warehouses - disparate data siloes and data fiefdoms that don't facilitate sharing of the corporate data assets across the organization. Organizations need to have a single data lake from which they can source the data for their BI/data warehousing and analytic needs. The data lake may never become the 'single version of the truth' for the organization, but then again, neither will the data warehouse. Instead, the data lake becomes the 'single or central repository for all the organization's data' from which all the organization's reporting and analytic needs are sourced." (Billl Schmarzo, "Driving Business Strategies with Data Science: Big Data MBA" 1st Ed., 2015)

"[...] the real power of the data lake is to enable advanced analytics or data science on the detailed and complete history of data in an attempt to uncover new variables and metrics that are better predictors of business performance." (Billl Schmarzo, "Driving Business Strategies with Data Science: Big Data MBA" 1st Ed., 2015)

"The data lake is not an incremental enhancement to the data warehouse, and it is NOT data warehouse 2.0. The data lake enables entirely new capabilities that allow your organization to address data and analytic challenges that the data warehouse could not address." (Billl Schmarzo, "Driving Business Strategies with Data Science: Big Data MBA" 1st Ed., 2015)

"Unfortunately, some organizations are replicating the bad data warehouse practice by creating special-purpose data lakes - data lakes to address a specific business need. Resist that urge! Instead, source the data that is needed for that specific business need into an 'analytic sandbox' where the data scientists and the business users can collaborate to find those data variables and analytic models that are better predictors of the business performance. Within the 'analytic sandbox', the organization can bring together (ingest and integrate) the data that it wants to test, build the analytic models, test the model's goodness of fit, acquire new data, refine the analytic models, and retest the goodness of fit." (Billl Schmarzo, "Driving Business Strategies with Data Science: Big Data MBA" 1st Ed., 2015)

"A data lake is a storage repository that holds a very large amount of data, often from diverse sources, in native format until needed. In some respects, a data lake can be compared to a staging area of a data warehouse, but there are key differences. Just like a staging area, a data lake is a conglomeration point for raw data from diverse sources. However, a staging area only stores new data needed for addition to the data warehouse and is a transient data store. In contrast, a data lake typically stores all possible data that might be needed for an undefined amount of analysis and reporting, allowing analysts to explore new data relationships. In addition, a data lake is usually built on commodity hardware and software such as Hadoop, whereas traditional staging areas typically reside in structured databases that require specialized servers." (Mike Fleckenstein & Lorraine Fellows, "Modern Data Strategy", 2018)

"A data warehouse follows a pre-built static structure to model source data. Any changes at the structural and configuration level must go through a stringent business review process and impact analysis. Data lakes are very agile. Consumption or analytical layer can be modified to fit in the model requirements. Consumers of a data lake are not constant; therefore, schema and modeling lies at the liberty of analysts and scientists." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"Data in the data lake should never get disposed. Data driven strategy must define steps to version the data and handle deletes and updates from the source systems." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"Data governance policies must not enforce constraints on data - Data governance intends to control the level of democracy within the data lake. Its sole purpose of existence is to maintain the quality level through audits, compliance, and timely checks. Data flow, either by its size or quality, must not be constrained through governance norms. [...] Effective data governance elevates confidence in data lake quality and stability, which is a critical factor to data lake success story. Data compliance, data sharing, risk and privacy evaluation, access management, and data security are all factors that impact regulation." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"Data Lake induces accessibility and catalyzes availability. It warrants data discovery platforms to soak the data trends at a horizontal scale and produce visual insights. It largely cuts down the time that goes into data preparation and exhaustive data analysis." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"Data Lake is a single window snapshot of all enterprise data in its raw format, be it structured, semi-structured, or unstructured. Starting from curating the data ingestion pipeline to the transformation layer for analytical consumption, every aspect of data gets addressed in a data lake ecosystem. It is supposed to hold enormous volumes of data of varied structures." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"Data swamp, on the other hand, presents the devil side of a lake. A data lake in a state of anarchy is nothing but turns into a data swamp. It lacks stable data governance practices, lacks metadata management, and plays weak on ingestion framework. Uncontrolled and untracked access to source data may produce duplicate copies of data and impose pressure on storage systems." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"Data warehousing, as we are aware, is the traditional approach of consolidating data from multiple source systems and combining into one store that would serve as the source for analytical and business intelligence reporting. The concept of data warehousing resolved the problems of data heterogeneity and low-level integration. In terms of objectives, a data lake is no different from a data warehouse. Both are primary advocates of terms like 'single source of truth' and 'central data repository'." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)

"At first, we threw all of this data into a pit called the 'data lake'. But we soon discovered that merely throwing data into a pit was a pointless exercise. To be useful - to be analyzed - data needed to (1) be related to each other and (2) have its analytical infrastructure carefully arranged and made available to the end user. Unless we meet these two conditions, the data lake turns into a swamp, and swamps start to smell after a while. [...] In a data swamp, data just sits there are no one uses it. In the data swamp, data just rots over time." (Bill Inmon et al, "Building the Data Lakehouse", 2021)

"Data lake architecture suffers from complexity and deterioration. It creates complex and unwieldy pipelines of batch or streaming jobs operated by a central team of hyper-specialized data engineers. It deteriorates over time. Its unmanaged datasets, which are often untrusted and inaccessible, provide little value. The data lineage and dependencies are obscured and hard to track." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"When it comes to data lakes, some things usually stay constant: the storage and processing patterns. Change could come in any of the following ways: Adding new components and processing or consumption patterns to respond to new requirements. […] Optimizing existing architecture for better cost or performance" (Rukmani Gopalan, "The Cloud Data Lake: A Guide to Building Robust Cloud Data Architecture", 2022)

"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. In most cases, it’s easy to turn a data lake into a Delta Lake; all you need to do is specify, when you are storing data to your data lake, that you want to save it in Delta Lake format (as opposed to other formats, like CSV or JSON)." (James Serra, "Deciphering Data Architectures", 2024)