⛏️Data Management: Data Scrubbing (Definitions)

"The process of making data consistent, either manually, or automatically using programs." (Microsoft Corporation, "Microsoft SQL Server 7.0 System Administration Training Kit", 1999)

Processing data to remove or repair inconsistencies." (Rod Stephens, "Beginning Database Design Solutions", 2008)

"The process of building a data warehouse out of data coming from multiple online transaction processing (OLTP) systems." (Microsoft, "SQL Server 2012 Glossary", 2012)

"A term that is very similar to data deidentification and is sometimes used improperly as a synonym for data deidentification. Data scrubbing refers to the removal, from data records, of identifying information (i.e., information linking the record to an individual) plus any other information that is considered unwanted. This may include any personal, sensitive, or private information contained in a record, any incriminating or otherwise objectionable language contained in a record, and any information irrelevant to the purpose served by the record." (Jules H Berman, "Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information", 2013)

"The process of removing corrupt, redundant, and inaccurate data in the data governance process. (Robert F Smallwood, Information Governance: Concepts, Strategies, and Best Practices, 2014)

"Data Cleansing (or Data Scrubbing) is the action of identifying and then removing or amending any data within a database that is: incorrect, incomplete, duplicated." (experian) [source]

"Data cleansing, or data scrubbing, is the process of detecting and correcting or removing inaccurate data or records from a database. It may also involve correcting or removing improperly formatted or duplicate data or records. Such data removed in this process is often referred to as 'dirty data'. Data cleansing is an essential task for preserving data quality." (Teradata) [source]

"Data scrubbing, also called data cleansing, is the process of amending or removing data in a database that is incorrect, incomplete, improperly formatted, or duplicated." (Techtarget) [source]

"Part of the process of building a data warehouse out of data coming from multiple online transaction processing (OLTP) systems." (Microsoft Technet)

"The process of filtering, merging, decoding, and translating source data to create validated data for the data warehouse." (Information Management)

⛏️Data Management: Data Steward (Definitions)

"A person with responsibility to improve the accuracy, reliability, and security of an organization’s data; also works with various groups to clearly define and standardize data." (Margaret Y Chu, "Blissful Data ", 2004)

"Critical players in data governance councils. Comfortable with technology and business problems, data stewards seek to speak up for their business units when an organization-wide decision will not work for that business unit. Yet they are not turf protectors, instead seeking solutions that will work across an organization. Data stewards are responsible for communication between the business users and the IT community." (Tony Fisher, "The Data Asset", 2009)

"A business leader and/or subject matter expert designated as accountable for: a) the identification of operational and Business Intelligence data requirements within an assigned subject area, b) the quality of data names, business definitions, data integrity rules, and domain values within an assigned subject area, c) compliance with regulatory requirements and conformance to internal data policies and data standards, d) application of appropriate security controls, e) analyzing and improving data quality, and f) identifying and resolving data related issues. Data stewards are often categorized as executive data stewards, business data stewards, or coordinating data stewards." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

[business data steward:] "A knowledge worker, business leader, and recognized subject matter expert assigned accountability for the data specifications and data quality of specifically assigned business entities, subject areas or databases, but with less responsibility for data governance than a coordinating data steward or an executive data steward." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"The person responsible for maintaining a data element in a metadata registry." (Microsoft, "SQL Server 2012 Glossary, 2012)

"The term stewardship is “the management or care of another person’s property” (NOAD). Data stewards are individuals who are responsible for the care and management of data. This function is carried out in different ways based on the needs of particular organizations." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement ", 2012)

"The person responsible for maintaining a data element in a metadata registry." (Microsoft, SQL Server 2012 Glossary, 2012)

"An individual comfortable with both technology and business problems. Stewards are responsible for communicating between the business users and the IT community." (Jim Davis & Aiman Zeid, "Business Transformation: A Roadmap for Maximizing Organizational Insights", 2014)

"A role in the data governance organization that is responsible for the development of a uniform data model for business objects used across boundaries. The data steward is also often responsible for the development of master data management and ensures compliance with the governance rules." (Boris Otto & Hubert Österle, "Corporate Data Quality", 2015)

"A natural person assigned the responsibility to catalog, define, and monitor changes to critical data. Example: The data steward for finance critical data is Dan." (Gregory Lampshire, "The Data and Analytics Playbook", 2016)

"A person responsible for managing data content, quality, standards, and controls within an organization or function." (Jonathan Ferrar et al, "The Power of People", 2017)

"A data steward is a job role that involves planning, implementing and managing the sourcing, use and maintenance of data assets in an organization. Data stewards enable an organization to take control and govern all the types and forms of data and their associated libraries or repositories." (Richard T Herschel, "Business Intelligence", 2019)

⛏️Data Management: Hashing (Definitions)

"A technique for providing fast access to data based on a key value by determining the physical storage location of that data." (Jan L Harrington, "Relational Database Dessign: Clearly Explained" 2nd Ed., 2002)

"A mathematical technique for assigning a unique number to each record in a file." (S. Sumathi & S. Esakkirajan, "Fundamentals of Relational Database Management Systems", 2007)

"A technique that transforms a key value via an algorithm to a physical storage location to enable quick direct access to data. The algorithm is typically referred to as a randomizer, because the goal of the hashing routine is to spread the key values evenly throughout the physical storage." (Craig S Mullins, "Database Administration", 2012)

"A mathematical technique in which an infinite set of input values is mapped to a finite set of output values, called hash values. Hashing is useful for rapid lookups of data in a hash table." (Oracle, "Database SQL Tuning Guide Glossary", 2013)

"An algorithm converts data values into an address" (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

"The technique used for ordering and accessing elements in a collection in a relatively constant amount of time by manipulating the element’s key to identify the element’s location in the collection" (Nell Dale et al, "Object-Oriented Data Structures Using Java" 4th Ed., 2016)

"The application of an algorithm to a search key to derive a physical storage location." (George Tillmann, "Usage-Driven Database Design: From Logical Data Modeling through Physical Schmea Definition", 2017)

"Hashing is the process of mapping data values to fixed-size hash values (hashes). Common hashing algorithms are Message Digest 5 (MD5) and Secure Hashing Algorithm (SHA). It’s impossible to turn a hash value back into the original data value." (Piethein Strengholt, "Data Management at Scale", 2020)

"A mathematical technique in which an infinite set of input values is mapped to a finite set of output values, called hash values. Hashing is useful for rapid lookups of data in a hash table." (Oracle, "Oracle Database Concepts")

"A process used to convert data into a string of numbers and letters." (AICPA)

"A technique for arranging a set of items, in which a hash function is applied to the key of each item to determine its hash value. The hash value identifies each item's primary position in a hash table, and if this position is already occupied, the item is inserted either in an overflow table or in another available position in the table." (IEEE 610.5-1990)

⛏️Data Management: Hash (Definitions)

"A number (often a 32-bit integer) that is derived from column values using a lossy compression algorithm. DBMSs occasionally use hashing to speed up access, but indexes are a more common mechanism." (Peter Gulutzan & Trudy Pelzer, "SQL Performance Tuning", 2002)

"A set of characters generated by running text data through certain algorithms. Often used to create digital signatures and compare changes in content." (Tom Petrocelli, "Data Protection and Information Lifecycle Management", 2005)

"Hash, a mathematical method for creating a numeric signature based on content; these days, often unique and based on public key encryption technology." (Bo Leuf, "The Semantic Web: Crafting infrastructure for agency", 2006)

[hash code:] "An integer calculated from an object. Identical objects have the same hash code. Generated by a hash method." (Michael Fitzgerald, "Learning Ruby", 2007)

"An unordered collection of data where keys and values are mapped. Compare with array." (Michael Fitzgerald, "Learning Ruby", 2007)

"A cryptographic hash is a fixed-size bit string that is generated by applying a hash function to a block of data. Secure cryptographic hash functions are collision-free, meaning there is a very small possibility of generating the same hash for two different blocks of data. A secure cryptographic hash function should also be one-way, meaning it is infeasible to retrieve the original text from the hash." (Michael Coles & Rodney Landrum, "Expert SQL Server 2008 Encryption", 2008)

"A hash is the result of applying a mathematical function or transformation on data to generate a smaller 'fingerprint' of the data. Generally, the most useful hash functions are one-way collision-free hashes that guarantee a high level of uniqueness in their results." (Michael Coles, "Pro T-SQL 2008 Programmer's Guide", 2008)

"The output of a hash function." (Mark S Merkow & Lakshmikanth Raghavan, "Secure and Resilient Software Development", 2010)

"A number based on the hash value of a string." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"1.Data allocated in an algorithmically randomized fashion in an attempt to evenly distribute data and smooth access patterns. 2.Verb. To calculate a hash key for data." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"A hash is the result of applying a mathematical function or transformation on data to generate a smaller 'fingerprint' of the data. Generally, the most useful hash functions are one-way collision-free hashes that guarantee a high level of uniqueness in their results." (Jay Natarajan et al, "Pro T-SQL 2012 Programmer's Guide" 3rd Ed., 2012)

"An unordered association of key/value pairs, stored such that you can easily use a string key to look up its associated data value. This glossary is like a hash, where the word to be defined is the key and the definition is the value. A hash is also sometimes septisyllabically called an “associative array”, which is a pretty good reason for simply calling it a 'hash' instead." (Jon Orwant et al, "Programming Perl" 4th Ed., 2012)

"In a hash cluster, a unique numeric ID that identifies a bucket. Oracle Database uses a hash function that accepts an infinite number of hash key values as input and sorts them into a finite number of buckets. Each hash value maps to the database block address for the block that stores the rows corresponding to the hash key value (department 10, 20, 30, and so on)." (Oracle, "Database SQL Tuning Guide Glossary", 2013)

"The result of applying a mathematical function or transformation to data to generate a smaller 'fingerprint' of the data. Generally, the most useful hash functions are one-way, collision-free hashes that guarantee a high level of uniqueness in their results." (Miguel Cebollero et al, "Pro T-SQL Programmer’s Guide" 4th Ed., 2015)

[hash code:] "The output of the hash function that is associated with the input object" (Nell Dale et al, "Object-Oriented Data Structures Using Java" 4th Ed., 2016)

"A numerical value produced by a mathematical function, which generates a fixed-length value typically much smaller than the input to the function. The function is many to one, but generally, for all practical purposes, each file or other data block input to a hash function yields a unique hash value." (William Stallings, "Effective Cybersecurity: A Guide to Using Best Practices and Standards", 2018)

"The number generated by a hash function to indicate the position of a given item in a hash table." (IEEE 610.5-1990)

⛏️Data Management: Completeness (Definitions)

"A characteristic of information quality that measures the degree to which there is a value in a field; synonymous with fill rate. Assessed in the data quality dimension of Data Integrity Fundamentals." (Danette McGilvray, "Executing Data Quality Projects", 2008)

"Containing by a composite data all components necessary to full description of the states of a considered object or process." (Juliusz L Kulikowski, "Data Quality Assessment", 2009)

"An inherent quality characteristic that is a measure of the extent to which an attribute has values for all instances of an entity class." (David C Hay, "Data Model Patterns: A Metadata Map", 2010)

"Completeness is a dimension of data quality. As used in the DQAF, completeness implies having all the necessary or appropriate parts; being entire, finished, total. A dataset is complete to the degree that it contains required attributes and a sufficient number of records, and to the degree that attributes are populated in accord with data consumer expectations. For data to be complete, at least three conditions must be met: the dataset must be defined so that it includes all the attributes desired (width); the dataset must contain the desired amount of data (depth); and the attributes must be populated to the extent desired (density). Each of these secondary dimensions of completeness can be measured differently." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement ", 2012)

"Completeness is defined as a measure of the presence of core source data elements that, exclusive of derived fields, must be present in order to complete a given business process." (Rajesh Jugulum, "Competing with High Quality Data", 2014)

"Complete existence of all values or attributes of a record that are necessary." (Boris Otto & Hubert Österle, "Corporate Data Quality", 2015)

"The degree to which all data has been delivered or stored and no values are missing. Examples are empty or missing records." (Piethein Strengholt, "Data Management at Scale", 2020)

"The degree to which elements that should be contained in the model are indeed there." (Panos Alexopoulos, "Semantic Modeling for Data", 2020)

"The degree of data representing all properties and instances of the real-world context." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"Data is considered 'complete' when it fulfills expectations of comprehensiveness." (Precisely) [source]

"The degree to which all required measures are known. Values may be designated as “missing” in order not to have empty cells, or missing values may be replaced with default or interpolated values. In the case of default or interpolated values, these must be flagged as such to distinguish them from actual measurements or observations. Missing, default, or interpolated values do not imply that the dataset has been made complete." (CODATA)

⛏️Data Management: Availability (Definitions)

"Corresponds to the information that should be available when necessary and in the appropriate format." (José M Gaivéo, "Security of ICTs Supporting Healthcare Activities", 2013)

"A property by which the data is available all the time during the business hours. In cloud computing domain, the data availability by the cloud service provider holds a crucial importance." (Sumit Jaiswal et al, "Security Challenges in Cloud Computing", 2015) 

"Availability: the ability of the data user to access the data at the desired point in time." (Boris Otto & Hubert Österle, "Corporate Data Quality", 2015)

"It is one of the main aspects of the information security. It means data should be available to its legitimate user all the time whenever it is requested by them. To guarantee availability data is replicated at various nodes in the network. Data must be reliably available." (Omkar Badve et al, "Reviewing the Security Features in Contemporary Security Policies and Models for Multiple Platforms", 2016)

"Timely, reliable access to data and information services for authorized users." (Maurice Dawson et al, "Battlefield Cyberspace: Exploitation of Hyperconnectivity and Internet of Things", 2017)

"A set of principles and metrics that assures the reliability and constant access to data for the authorized individuals or groups." (Gordana Gardašević et al, "Cybersecurity of Industrial Internet of Things", 2020)

"Ensuring the conditions necessary for easy retrieval and use of information and system resources, whenever necessary, with strict conditions of confidentiality and integrity." (Alina Stanciu et al, "Cyberaccounting for the Leaders of the Future", 2020)

"The state when data are in the place needed by the user, at the time the user needs them, and in the form needed by the user." (CODATA)

"The state that exists when data can be accessed or a requested service provided within an acceptable period of time." (NISTIR 4734)

"Timely, reliable access to information by authorized entities." (NIST SP 800-57 Part 1)

⛏️Data Management: Data Products (Definitions)

"In the case of data mesh, a data product is an architectural quantum. It is the smallest unit of architecture that can be independently deployed and managed." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"A data product is a data asset that should be trusted, reusable, and accessible. The data product is developed, owned, and managed by a domain, and each domain needs to make sure that its data products are accessible to other domains and their data consumers." (Marthe Mengen, 2024) [source] 

"A data product is a self-contained, independently deployable unit of data that delivers business value." (James Serra, "Deciphering Data Architectures", 2024)

"A collection of optimized data or data-related assets that are packaged for reuse and distribution with controlled access. Data products contain data as well as models, dashboards, and other computational asset types. Unlike data assets in governance catalogs, data products are managed as products with multiple purposes to provide business value." (IBM)

"A data product, in general terms, is any tool or application that processes data and generates results. […] Data products have one primary objective: to manage, organize and make sense of the vast amount of data that organizations collect and generate. It’s the users’ job to put the insights to use that they gain from these data products, take actions and make better decisions based on these insights." (Sisense) [source]

"A data product is a product built around data, containing everything required to complete a specific task or objective using that underlying data." (Opendatasoft)

"A data product is digital information that can be purchased." (Techtarget) [source]

"A key concept in data mesh architecture, Data Products are independent units of data managed by a specific domain team. They are responsible for defining, publishing, and maintaining their data assets while ensuring high-quality data that meets the needs of its consumers." (DataHub)

[Data product specification:] "Detailed description of a data set or data set series together with additional information that will enable it to be created, supplied to and used by another party" (ISO 19131)

"Data set or data set series that conforms to a data product specification" (ISO 19131)

⛏️Data Management: Accessibility (Definitions)

"Capable of being reached, capable of being used or seen." (Martin J Eppler, "Managing Information Quality" 2nd Ed., 2006)

"The degree to which data can be obtained and used." (Danette McGilvray, "Executing Data Quality Projects", 2008)

"The opportunity to find, as well as the ease and convenience associated with locating, information. Often, this is related to the physical location of the individual seeking the information and the physical location of the information in a book or journal." (Jimmie L Joseph & David P Cook, "Medical Ethical and Policy Issues Arising from RIA", 2008)

"An inherent quality characteristic that is a measure of the ability to access data when it is required." (David C Hay, "Data Model Patterns: A Metadata Map", 2010)

"The ability to readily obtain data when needed." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Accessibility refers to the difficulty level for users to obtain data. Accessibility is closely linked with data openness, the higher the data openness degree, the more data types obtained, and the higher the degree of accessibility." (Li Cai & Yangyong Zhu, "The Challenges of Data Quality and Data Quality Assessment in the Big Data Era", 2015) [source]

"It is the state of each user to have access to any information at any time." (ihsan Eken & Basak Gezmen, "Accessibility for Everyone in Health Communication Mobile Application Usage", 2020)

"Data accessibility measures the extent to which government data are provided in open and re-usable formats, with their associated metadata." (OECD)

⛏️Data Management: Data Virtualization (Definitions)

"The concept of letting data stay 'where it lives; and developing a hardware and software architecture that exposes the data to various business processes and organizations. The goal of virtualization is to shield developers and users from the complexity of the underlying data structures." (Jill Dyché & Evan Levy, "Customer Data Integration: Reaching a Single Version of the Truth", 2006)

"The ability to easily select and combine data fragments from many different locations dynamically and in any way into a single data structure while also maintaining its semantic accuracy." (Michael M David & Lee Fesperman, "Advanced SQL Dynamic Data Modeling and Hierarchical Processing", 2013)

"The process of retrieving and manipulating data without requiring details of how the data formatted or where the data is located" (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

"A data integration process used to gain more insights.  Usually it involves databases, applications, file systems, websites, big data techniques, and so on." (Jason Williamson, "Getting a Big Data Job For Dummies", 2015)

"Data virtualization is an approach that allows an application to retrieve and manipulate data without requiring technical details about the data, such as how it is formatted at source or where it is physically located, and can provide a single customer view (or single view of any other entity) of the overall data. Some database vendors provide a database (virtual) query layer, which is also called a data virtualization layer. This layer abstracts the database and optimizes the data for better read performance. Another reason to abstract is to intercept queries for better security. An example is Amazon Athena." (Piethein Strengholt, "Data Management at Scale", 2020)

"A data integration process in order to gain more insights. Usually it involves databases, applications, file systems, websites, big data techniques, etc.)." (Analytics Insight)

 "The integration and transformation of data in real time or near real time from disparate data sources in multicloud and hybrid cloud, to support business intelligence, reporting, analytics, and other workloads." (Forrester)

⛏️Data Management: Data Lineage (Definitions)

 "A mechanism for recording information to determine the source of any piece of data, and the transformations applied to that data using Data Transformation Services (DTS). Data lineage can be tracked at the package and row levels of a table and provides a complete audit trail for information stored in a data warehouse. Data lineage is available only for packages stored in Microsoft Repository." (Microsoft Corporation, "SQL Server 7.0 System Administration Training Kit", 1999)

"This information is used by Data Transformation Services (DTS) when it works in conjunction with Meta Data Services. This information records the history of package execution and data transformations for each piece of data." (Anthony Sequeira & Brian Alderman, "The SQL Server 2000 Book", 2003)

"This is also called data provenance. It deals with the origin of data; it is all about documenting where data is, how it has been derived, and how it flows so you can manage and secure it appropriately as it is further processed by applications." (Martin Oberhofer et al, "Enterprise Master Data Management", 2008)

"This provides the functionality to determine where data comes from, how it is transformed, and where it is going. Data lineage metadata traces the lifecycle of information between systems, including the operations that are performed on the data." (Martin Oberhofer et al, "The Art of Enterprise Information Architecture", 2010)

"Data lineage refers to a set of identifiable points that can be used to understand details of data movement and transformation (e.g., transactional source field names, file names, data processing job names, programming rules, target table fields). Lineage describes the movement of data through systems from its origin or provenance to its use in a particular application. Lineage is related to both the data chain and the information life cycle. Most people concerned with the lineage of data want to understand two aspects of it: the data’s origin and the ways in which the data has changed since it was originally created. Change can take place within one system or between systems." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement ", 2012)

⛏️Data Management: Data Mesh (Definitions)

"Data Mesh is a sociotechnical approach to share, access and manage analytical data in complex and large-scale environments - within or across organizations." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)

"A data mesh is an architectural concept in data engineering that gives business domains (divisions/departments) within a large organization ownership of the data they produce. The centralized data management team then becomes the organization’s data governance team." (Margaret Rouse, 2023) [source]

"Data Mesh is a design concept based on federated data and business domains. It applies product management thinking to data management with the outcome being Data Products. It’s technology agnostic and calls for a domain-centric organization with federated Data Governance." (Sonia Mezzetta, "Principles of Data Fabric", 2023)

"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)

"A data mesh is a federated data architecture that emphasizes decentralizing data across business functions or domains such as marketing, sales, human resources, and more. It facilitates organizing and managing data in a logical way to facilitate the more targeted and efficient use and governance of the data across organizations." (Arshad Ali & Bradley Schacht, "Learn Microsoft Fabric", 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)

"A data mesh is a decentralized data architecture that organizes data by a specific business domain - for example, marketing, sales, customer service and more - to provide more ownership to the producers of a given data set." (IBM) [source]

"A data mesh is a new approach to designing data architectures. It takes a decentralized approach to data storage and management, having individual business domains retain ownership over their datasets rather than flowing all of an organization’s data into a centrally owned data lake." (Alteryx) [source]

"A Data Mesh is a solution architecture for the specific goal of building business-focused data products without preference or specification of the technology involved." (Gartner)

"A data mesh is an architectural framework that solves advanced data security challenges through distributed, decentralized ownership." (AWS) [source]

"Data mesh defines a platform architecture based on a decentralized network. The data mesh distributes data ownership and allows domain-specific teams to manage data independently." (TIBCO) [source]

"Data mesh refers to a data architecture where data is owned and managed by the teams that use it. A data mesh decentralizes data ownership to business domains–such as finance, marketing, and sales–and provides them a self-serve data platform and federated computational governance." (Qlik) [source]

⛏️Data Management: Quality (Just the Quotes)

"Quality is never an accident; it is always the result of intelligent effort." (John Ruskin, "Seven Lamps of Architecture", 1849)

"It is most important that top management be quality-minded. In the absence of sincere manifestation of interest at the top, little will happen below." (Joseph M Juran, "Management of Inspection and Quality Control", 1945)

"Data are of high quality if they are fit for their intended use in operations, decision-making, and planning." (Joseph M Juran, 1964)

"The management of a system has to deal with the generation of the plans for the system, i. e., consideration of all of the things we have discussed, the overall goals, the environment, the utilization of resources and the components. The management sets the component goals, allocates the resources, and controls the system performance." (C West Churchman, "The Systems Approach", 1968)

"When a product is manufactured by workers who find their work meaningful, it will inevitably be a product of high quality." (Pehr G Gyllenhammar, "Management", 1976)

"Quality management is a systematic way of guaranteeing that organized activities happen the way they are planned." (Philip B Crosby, "Quality Is Free: The Art of Making Quality Certain", 1977)

"The problem of quality management is not what people don't know about it. The problem is what they think they do know." (Philip B Crosby, "Quality Is Free: The Art of Making Quality Certain", 1977)

"Uncontrolled variation is the enemy of quality." (W Edwards Deming, 1980)

"Almost all quality improvement comes via simplification of design, manufacturing, layout, processes and procedures." (Tom Peters, "Thriving on Chaos", 1987)

"Quality is a matter of faith. You set your standards, and you have to stick by them no matter what. That's easy when you've got plenty of product on hand, but it's another thing when the freezer is empty and you've got a truck at the door waiting for the next shipment to come off the production line. That's when you really earn your reputation for quality." (Ben Cohen, Inc. Magazine, 1987)

"Quality is very simple. So simple, in fact, that it is difficult for people to understand." (Roger Hale, "Quest for Quality", 1987)

"[...] running numbers on a computer [is] easier than trying to judge quality." (Esther Dyson, Forbes, 1987)

"The [quality control] issue has more to do with people and motivation and less to do with capital and equipment than one would think. It involves a cultural change." (Michael Beer, The Washington Post, 1987)

"Cutting costs without improvements in quality is futile." (W Edwards Deming, Forbes, 1988)

"Quality planning consists of developing the products and processes required to meet customer's needs." (Joseph M Juran, "Juran on planning for quality", 1988)

"Quality means meeting customers' (agreed) requirements, formal and informal, at lowest cost, first time every time." (Robert L Flood, "Beyond TQM", 1993)

"Many quality failures arise because a customer uses the product in a manner different from that intended by the supplier." (Joseph M Juran, "The quality planning process", 1999)

"Quality goals that affect product salability should be based primarily on meeting or exceeding market quality. Because the market and the competition undoubtedly will be changing while the quality planning project is under way, goals should be set so as to meet or beat the competition estimated to be prevailing when the project is completed." (Joseph M Juran, "The quality planning process", 1999)

"'Quality' means freedom from deficiencies - freedom from errors that require doing work over again (rework) or that result in field failures, customer dissatisfaction, customer claims, and so on." (Joseph M Juran, "How to think about quality", 1999)

"‘Quality’ means those features of products which meet customer needs and thereby provide customer satisfaction." (Joseph M Juran, "How to think about quality", 1999)

"The anatomy of 'quality assurance' is very similar to that of quality control. Each evaluates actual quality. Each compares actual quality with the quality goal. Each stimulates corrective action as needed. What differs is the prime purpose to be served. Under quality control, the prime purpose is to serve those who are directly responsible for conducting operations - to help them regulate current operations. Under quality assurance, the prime purpose is to serve those who are not directly responsible for conducting operations but who have a need to know - to be informed as to the state of affairs and, hopefully, to be assured that all is well." (Joseph M Juran, "How to think about quality", 1999)

"To attain quality, it is well to begin by establishing the 'vision' for the organization, along with policies and goals. Conversion of goals into results (making quality happen) is then done through managerial processes - sequences of activities that produce the intended results." (Joseph M Juran, "How to think about quality", 1999)

"Our culture, obsessed with numbers, has given us the idea that what we can measure is more important than what we can't measure. Think about that for a minute. It means that we make quantity more important than quality." (Donella Meadows, "Thinking in Systems: A Primer", 2008)

"A model is a representation in that it (or its properties) is chosen to stand for some other entity (or its properties), known as the target system. A model is a tool in that it is used in the service of particular goals or purposes; typically these purposes involve answering some limited range of questions about the target system." (Wendy S Parker, "Confirmation and Adequacy-for-Purpose in Climate Modelling", Proceedings of the Aristotelian Society, Supplementary Volumes, Vol. 83, 2009)

⛏️Data Management: Data Structure (Definitions)

"A logical relationship among data elements that is designed to support specific data manipulation functions (trees, lists, and tables)." (William H Inmon, "Building the Data Warehouse", 2005)

"Data stored in a computer in a way that (usually) allows efficient retrieval of the data. Arrays and hashes are examples of data structures." (Michael Fitzgerald, "Learning Ruby", 2007)

"A data structure in computer science is a way of storing data to be used efficiently." (Sahar Shabanah, "Computer Games for Algorithm Learning", 2011)

"Data structure is a general term referring to how data is organized. In modeling, it refers more specifically to the model itself. Tables are referred to as 'structures'." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement ", 2012)

[probabilistic *] "A data structure which exploits randomness to boost its efficiency, for example skip lists and Bloom filters. In the case of Bloom filters, the results of certain operations may be incorrect with a small probability." (Wei-Chih Huang & William J Knottenbelt, "Low-Overhead Development of Scalable Resource-Efficient Software Systems", 2014)

"A collection of methods for storing and organizing sets of data in order to facilitate access to them. More formally data structures are concise implementations of abstract data types, where an abstract data type is a set of objects together with a collection of operations on the elements of the set." (Ioannis Kouris et al, "Indexing and Compressing Text", 2015)

"A representation of the logical relationship between elements of data." (Adam Gordon, "Official (ISC)2 Guide to the CISSP CBK" 4th Ed., 2015)

"Is a schematic organization of data and relationship to express a reality of interest, usually represented in a diagrammatic form." (Maria T Artese  Isabella Gagliardi, "UNESCO Intangible Cultural Heritage Management on the Web", 2015)

"The implementation of a composite data field in an abstract data type" (Nell Dale & John Lewis, "Computer Science Illuminated" 6th Ed., 2015)

"A way of organizing data so that it can be efficiently accessed and updated." (Vasileios Zois et al, "Querying of Time Series for Big Data Analytics", 2016)

"A particular way of storing information, allowing to a high level approach on the software implementation." (Katia Tannous & Fillipe de Souza Silva, "Particle Shape Analysis Using Digital Image Processing", 2018)

"It is a particular way of organizing data in a computer so that they can be used efficiently." (Edgar C Franco et al, "Implementation of an Intelligent Model Based on Machine Learning in the Application of Macro-Ergonomic Methods...", 2019)

"Way information is represented and stored." (Shalin Hai-Jew, "Methods for Analyzing and Leveraging Online Learning Data", 2019)

"A physical or logical relationship among a collection of data elements." (IEEE 610.5-1990)

⛏️Data Management: Data Sharing (Definitions)

"The ability to share individual pieces of data transparently from a database across different applications." (Microsoft Corporation, "SQL Server 7.0 System Administration Training Kit", 1999)

"Exchange of data and/or meta-data in a situation involving the use of open, freely available data formats, where process patterns are known and standard, and where not limited by privacy and confidentiality regulations." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Data sharing involves one entity sending data to another entity, usually with the understanding that the other entity will store and use the data. This process may involve free or purchased data, and it may be done willingly, or in compliance with regulations, laws, or court orders." (Jules H Berman, "Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information", 2013)

"The ability of subsystems or application programs to access data directly and to change it while maintaining data integrity." (Sybase, "Open Server Server-Library/C Reference Manual", 2019)

"The ability of two or more DB2 subsystems to directly access and change a single set of data." (BMC)

⛏️Data Management: Information Overload (Definitions)

"A state in which information can no longer be internalized productively by the individual due to time constraints or the large volume of received information." (Martin J Eppler, "Managing Information Quality" 2nd Ed., 2006)

"Phenomena related to the inability to absorb and manage effectively large amounts of information, creating inefficiencies, stress, and frustration. It has been exacerbated by advances in the generation, storage, and electronic communication of information." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"A situation where relevant information becomes buried in a mass of irrelevant information" (Josep C Morales, "Information Disasters in Networked Organizations", 2008)

"A situation where individuals have access to so much information that it becomes impossible for them to function effectively, sometimes leading to where nothing gets done and the user gives the impression of being a rabbit caught in the glare of car headlights." Alan Pritchard, "Information-Rich Learning Concepts", 2009)

"is the situation when the information processing requirements exceed the information processing capacities." (Jeroen ter Heerdt & Tanya Bondarouk, "Information Overload in the New World of Work: Qualitative Study into the Reasons", 2009)

"Refers to an excess amount of information, making it difficult for individuals to effectively absorb and use information; increases the likelihood of poor decisions." (Leslie G Eldenburg & Susan K Wolcott, "Cost Management" 2nd Ed., 2011)

"The inability to cope with or process ever-growing amounts of data into our lives." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"The state where the rate or amount of input to a system or person outstrips the capacity or speed of processing that input successfully." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"The state in which a huge influx of information interferes with understanding an issue, making good decisions, and performance on the job." (Carol A. Brown, "Economic Impact of Information and Communication Technology in Higher Education", 2014)

"The difficulty a person can have understanding an issue and making decisions that can be caused by the presence of too much information." (Li Chen, "Mobile Technostress", Encyclopedia of Mobile Phone Behavior, 2015)

"Occurs when excess of information suffocates businesses and causes employees to suffer mental anguish and physical illness. Information overload causes high levels of stress that can result in health problems and the breakdown of individuals’ personal relationships." (Sérgio Maravilhas & Sérgio R G Oliveira, "Entrepreneurship and Innovation: The Search for the Business Idea", 2018)

"A set of subjective and objective difficulties, mainly originating in the amount and complexity of information available and people’s inability to handle such situations." (Tibor Koltay, "Information Overload", 2021)