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29 January 2017
⛏️Data Management: Master Data (Definitions)
26 January 2017
⛏️Data Management: Data Governance (Definitions)
"A company-wide framework that determines which decisions must be made and who should make them. This includes the definition of roles, responsibilities, obligations and rights in handling the company’s resource data. In this, data governance pursues the goal of maximizing the value of the data in the company. While data governance determines how decisions should be made, data management makes the actual decisions and implements them." (Boris Otto & Hubert Österle, "Corporate Data Quality", 2015)
20 January 2017
⛏️Data Management: Data Element (Definitions)
"An atomic unit of data; in most cases, a field." (Microsoft Corporation, "Microsoft SQL Server 7.0 Data Warehouse Training Kit", 2000)
"(1) an attribute of an entity; (2) a uniquely named and well-defined category of data that consists of data items and that is included in a record of an activity." (William H Inmon, "Building the Data Warehouse", 2005)
"The most atomic, pure, and simple fact that either describes or identifies an entity. This is also known as an attribute. It can be deployed as a column in a table in a physical structure." (Sharon Allen & Evan Terry, "Beginning Relational Data Modeling" 2nd Ed., 2005)
"The smallest unit of data that is named. The values are stored in a column or a field in a database." (Laura Reeves, "A Manager's Guide to Data Warehousing", 2009)
[data attribute:] "1.An inherent fact, property, or characteristic describing an entity or object; the logical representation of a physical field or relational table column. A given attribute has the same format, interpretation, and domain for all occurrences of an entity. Attributes may contain adjective values (red, round, active, etc.). 2.A unit of data for which the definition, identification, representation, and permissible values are specified by means of a set of characteristics. 3.A representation of a data characteristic variation in the logical or physical data model. A data attribute may or may not be atomic." (DAMA International, "The DAMA Dictionary of Data Management", 2011)
"A single unit of data." (SQL Server 2012 Glossary, "Microsoft", 2012)
"A primitive item of data; one that has a value within the context of study and is not further decomposed." (James Robertson et al, "Complete Systems Analysis: The Workbook, the Textbook, the Answers", 2013)
"A unit of data (fact) that can be uniquely defined and used. Example: last name is a data element that can be defined as the family name of an individual and is distinct from other name-related elements." (Gregory Lampshire, "The Data and Analytics Playbook", 2016)
"A basic unit of information that has a unique meaning and subcategories (data items) of distinct value. Examples of data elements include gender, race, and geographic location." (CNSSI 4009-2015)
⛏️Data Management: Data Literacy (Definitions)
"Understanding what data mean, including how to read charts
appropriately, draw correct conclusions from data and recognize when data are
being used in misleading or inappropriate ways." (Jake R Carlson et al., "Determining Data Information Literacy Needs: A Study of Students and Research Faculty", 2011) [source]
"Data literacy is the ability to collect, manage, evaluate, and apply data, in a critical manner." (Chantel Ridsdale et al, "Strategies and Best Practices for Data Literacy Education", [knowledge synthesis report] 2016) [source]
"The data-literate individual understands, explains, and documents the utility and limitations of data by becoming a critical consumer of data, controlling his/her personal data trail, finding meaning in data, and taking action based on data. The data-literate individual can identify, collect, evaluate, analyze, interpret, present, and protect data." (IBM, Building "Global Interest in Data Literacy: A Dialogue", [workshop report] 2016) [source]
"the ability to understand the principles behind learning from data, carry out basic data analyses, and critique the quality of claims made on the basis of data." (David Spiegelhalter, "The Art of Statistics: Learning from Data", 2019)
"The ability to recognize, evaluate, work with, communicate, and apply data in the context of business priorities and outcomes." (Forrester)
"Data literacy is the ability to derive meaningful information from data, just as literacy in general is the ability to derive information from the written word." (Techtarget) [source]
"Data literacy is the ability to read, work with, analyze and communicate with data, building the skills to ask the right questions of data and machines to make decisions and communicate meaning to others. "(Qlik) [source]
"Data literacy is the ability to read, write and communicate data in context, with an understanding of the data sources and constructs, analytical methods and techniques applied, and the ability to describe the use case application and resulting business value or outcome." (Gartner)
"Data literacy is the ability to read, work with, analyze and communicate with data. It’s a skill that empowers all levels of workers to ask the right questions of data and machines, build knowledge, make decisions, and communicate meaning to others." (Sumo Logic) [source]
"Data
literacy is the skill set of reading, communicating, and deriving meaningful
information from data. Collecting the data is only the first step. The real
value comes from being able to put the information in context and tell a story." (Sisense) [source]
19 January 2017
🚧Project Management: Product Lifecycle (Definitions)
"The period of time, consisting of phases, that begins when a product is conceived and ends when the product is no longer available for use. Since an organization may be producing multiple products for multiple customers, one description of a product life cycle may not be adequate. Therefore, the organization may define a set of approved product life-cycle models. These models are typically found in published literature and are likely to be tailored for use in an organization. A product life cycle could consist of the following phases: (1) concept/vision, (2) feasibility, (3) design/development, (4) production, and (5) phase out." (Sandy Shrum et al, "CMMI®: Guidelines for Process Integration and Product Improvement", 2003)
"The period of time that begins when a product is conceived and ends when the product is no longer available for use. This cycle typically includes phases for concept definition (verifies feasibility), full-scale development (builds and optionally installs the initial version of the system), production (manufactures copies of the first article), transition (transfers the responsibility for product upkeep to another organization), operation and sustainment (repairs and enhances the product), and retirement (removes the product from service). Full-scale development may be divided into subphases to facilitate planning and management such as requirements analysis, design, implementation, integration and test, installation and checkout." (Richard D Stutzke, "Estimating Software-Intensive Systems: Projects, Products, and Processes", 2005)
"A term to describe a product, from its conception to its discontinuance and ultimate market withdrawal." (Steven Haines, "The Product Manager's Desk Reference", 2008)
"a model of the sales and profits of a product category from its introduction until its decline and disappearance from the market; focuses on the appropriate strategies at each stage." (Gina C O'Connor & V K Narayanan, "Encyclopedia of Technology and Innovation Management", 2010)
"A collection of generally sequential, non-overlapping product phases whose name and number are determined by the manufacturing and control needs of the organization. The last product life cycle phase for a product is generally the product's retirement. Generally, a project life cycle is contained within one or more product life cycles." (Cynthia Stackpole, "PMP® Certification All-in-One For Dummies®", 2011)
"The series of phases that represent the evolution of a product, from concept through delivery, growth, maturity, and to retirement." (For Dummies, "PMP Certification All-in-One For Dummies" 2nd Ed., 2013)
18 January 2017
⛏️Data Management: Business Rules (Definitions)
16 January 2017
⛏️Data Management: Data Flow (Definitions)
"The sequence in which data transfer, use, and transformation are performed during the execution of a computer program." (IEEE," IEEE Standard Glossary of Software Engineering Terminology", 1990)
"A component of a SQL Server Integration Services package that controls the flow of data within the package." (Marilyn Miller-White et al, "MCITP Administrator: Microsoft® SQL Server™ 2005 Optimization and Maintenance 70-444", 2007)
"Activities of a business process may exchange data during the execution of the process. The data flow graph of the process connects activities that exchange data and - in some notations - may also represent which input/output parameters of the activities are involved." (Cesare Pautasso, "Compiling Business Process Models into Executable Code", 2009)
"Data dependency and data movement between process steps to ensure that required data is available to a process step at execution time." (Christoph Bussler, "B2B and EAI with Business Process Management", 2009)
[logical data flow:] "A data flow diagram that describes the flow of information in an enterprise without regard to any mechanisms that might be required to support that flow." (David C Hay, "Data Model Patterns: A Metadata Map", 2010)
[physical data flow:] "A data flow diagram that identifies and represents data flows and processes in terms of the mechanisms currently used to carry them out." (David C Hay, "Data Model Patterns: A Metadata Map", 2010)
"The fact that data, in the form of a virtual entity class, can be sent from a party, position, external entity, or system process to a party, position, external entity, or system process." (David C Hay, "Data Model Patterns: A Metadata Map", 2010)
"An abstract representation of the sequence and possible changes of the state of data objects, where the state of an object is any of: creation, usage, or destruction [Beizer]." (International Qualifications Board for Business Analysis, "Standard glossary of terms used in Software Engineering", 2011)
"Data flow refers to the movement of data from one purpose to another; also the movement of data through a set of systems, or through a set of transformations within one system; it is a nontechnical description of how data is processed. See also Data Chain." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement ", 2012)
"The movement of data through a group of connected elements that extract, transform, and load data." (Microsoft, "SQL Server 2012 Glossary", 2012)
"A path that carries packets of information of known composition; a roadway for data. Every data flow’s composition is recorded in the data dictionary." (James Robertson et al, "Complete Systems Analysis: The Workbook, the Textbook, the Answers", 2013)
"the path, in information systems or otherwise, through which data move during the active phase of a study." (Meredith Zozus, "The Data Book: Collection and Management of Research Data", 2017)
"The lifecycle movement and storage of data assets along business process networks, including creation and collection from external sources, movement within and between internal business units, and departure through disposal, archiving, or as products or other outputs." (Kevin J Sweeney, "Re-Imagining Data Governance", 2018)
"A graphical model that defines activities that extract data from flat files or relational tables, transform the data, and load it into a data warehouse, data mart, or staging table." (Sybase, "Open Server Server-Library/C Reference Manual", 2019)
"An abstract representation of the sequence and possible changes of the state of data objects, where the state of an object is any of: creation, usage, or destruction." (Software Quality Assurance)
⛏️Data Management: Data Quality Management [DQM] (Definitions)
[Total Data Quality Management:] "An approach that manages data proactively as the outcome of a process, a valuable asset rather than the traditional view of data as an incidental by-product." (Karolyn Kerr, "Improving Data Quality in Health Care", 2009)
"The application of total quality management concepts and practices to improve data and information quality, including setting data quality policies and guidelines, data quality measurement (including data quality auditing and certification), data quality analysis, data cleansing and correction, data quality process improvement, and data quality education." (DAMA International, "The DAMA Dictionary of Data Management", 2011)
"Data Quality Management (DQM) is about employing processes, methods, and technologies to ensure the quality of the data meets specific business requirements." (Mark Allen & Dalton Cervo, "Strategy, Scope, and Approach" [in "Multi-Domain Master Data Management"], 2015)
"DQM is the management of company data in a manner aware of quality. It is a sub-function of data management and analyzes, improves and assures the quality of data in the company. DQM includes all activities, procedures and systems to achieve the data quality required by the business strategy. Among other things, DQM transfers approaches for the management of quality for physical goods to immaterial goods like data." (Boris Otto & Hubert Österle, "Corporate Data Quality", 2015)
"Data quality management (DQM) is a set of practices aimed at improving and maintaining the quality of data across a company’s business units." (altexsoft) [source]
"Data quality management is a set of practices that aim at maintaining a high quality of information. DQM goes all the way from the acquisition of data and the implementation of advanced data processes, to an effective distribution of data. It also requires a managerial oversight of the information you have." (Data Pine) [source]
"Data quality management is a setup process, which is aimed at achieving and maintaining high data quality. Its main stages involve the definition of data quality thresholds and rules, data quality assessment, data quality issues resolution, data monitoring and control." (ScienceSoft) [source]
"Data quality management is the act of ensuring suitable data quality." (Xplenty) [source]
"Data quality management provides a context-specific process for improving the fitness of data that’s used for analysis and decision making. The goal is to create insights into the health of that data using various processes and technologies on increasingly bigger and more complex data sets." (SAS) [source]
"Data quality management (DQM) refers to a business principle that requires a combination of the right people, processes and technologies all with the common goal of improving the measures of data quality that matter most to an enterprise organization." (BMC) [source]
"Put most simply, data quality management is the process of reviewing and updating your customer data to minimize inaccuracies and eliminate redundancies, such as duplicate customer records and duplicate mailings to the same address." (EDQ) [source]
12 January 2017
⛏️Data Management: Reference Data (Definitions)
"Reference data is focused on defining and distributing collections of common values to support accurate and efficient processing of operational and analytical activities." (Martin Oberhofer et al, "Enterprise Master Data Management", 2008)
"Sets of values or classification schemas referred to by systems, applications, data stores, processes, and reports, as well as by transactional and master records. Examples include lists of valid values, code lists, status codes, flags, product types, charts of accounts, product hierarchy." (Danette McGilvray, "Executing Data Quality Projects", 2008)
"Data that describe the infrastructure of an enterprise. These comprise the 'type' entity classes that provide lists of values for other attributes." (David C Hay, "Data Model Patterns: A Metadata Map", 2010)
"Data characterized by shared read operations and infrequent changes. Examples of reference data include flight schedules and product catalogs. Windows Server AppFabric offers the local cache feature for storing this type of data." (Microsoft, "SQL Server 2012 Glossary", 2012)
"Corporate data that has been defined externally and is uniformly changed across company boundaries, such as country codes, currency codes and geo-data." (Boris Otto & Hubert Österle, "Corporate Data Quality", 2015)
"Reference data is commonly used to link and give additional details to the data. It is the data used to classify, organize, or categorize other data. Reference data can also contain value hierarchies, for example, the relationships between product and geographic hierarchies. It is escorted by the discipline Reference Data Management, which makes sure the reference data is consistent and that different versions are managed and distributed properly." (Piethein Strengholt, "Data Management at Scale", 2020)
⛏️Data Management: Data Lifecycle (Definitions)
"The data life cycle is the set of processes a dataset goes through from its origin through its use(s) to its retirement. Data that moves through multiple systems and multiple uses has a complex life cycle." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement ", 2012)
"The recognition that as data ages, that data takes on different characteristics" (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)
"The development of a record in the company’s IT systems from its creation until its deletion. This process may also be designated as “CRUD”, an acronym for the Create, Read/Retrieve, Update and Delete database operations." (Boris Otto & Hubert Österle, "Corporate Data Quality", 2015)
"The series of stages that data moves though from initiation, to creation, to destruction. Example: the data life cycle of customer data has four distinct phases and lasts approximately eight years." (Gregory Lampshire, "The Data and Analytics Playbook", 2016)
10 January 2017
⛏️Data Management: Metadata (Definitions)
03 January 2017
⛏️Data Management: Transactional Data (Definitions)
"Data about the day-to-day dynamic activities of a company, such as invoices." (Gavin Powell, "Beginning Database Design", 2006)
"Data that describe an internal or external event or transaction that takes place as an organization conducts its business. Examples include sales orders, invoices, purchase orders, shipping documents, passport applications, credit card payments, and insurance claims. Transactional data are typically grouped into transactional records, which include associated master and reference data." (Danette McGilvray, "Executing Data Quality Projects", 2008)
"The set of records of individual business activities or events." (Janice M Roehl-Anderson, "IT Best Practices for Financial Managers", 2010)
"Data related to sales, deliveries, invoices, trouble tickets, claims, and other monetary and non-monetary interactions." (Microsoft, "SQL Server 2012 Glossary", 2012)
"A type of data that gathers information about contracts, deliveries, invoices, payments and so forth and exhibits a high frequency of change. Transaction data provide a key to the activities of the core business objects." (Boris Otto & Hubert Österle, "Corporate Data Quality", 2015)
"Information stored from a time-based instance, like a bank deposit or phone call." (Jason Williamson, "Getting a Big Data Job For Dummies", 2015)
"Master data and reference data with associated time dimension." (Hamid R Arabnia et al, "Application of Big Data for National Security", 2015)
🚧Project Management: Baseline (Definitions)
"The original approved plan for work such as a project. Usually used with a modifier, e.g., cost baseline, schedule baseline, performance measurement baseline." (Margaret Y Chu, "Blissful Data ", 2004)
"An approved plan for a project, plus or minus approved changes. It is compared to actual performance to determine if performance is within acceptable variance thresholds. Generally refers to the current baseline, but may refer to the original or some other baseline. Usually used with a modifier (e.g., cost performance baseline, schedule baseline, performance measurement baseline, technical baseline)." (Project Management Institute, "Practice Standard for Project Estimating", 2010)
"The approved version of a work product that can be changed only through formal change control procedures and is used as a basis for comparison." (For Dummies, "PMP Certification All-in-One For Dummies" 2nd Ed., 2013)
"The original approved plan for a project, including approved changes. It usually includes baseline budget and baseline schedule. It is used as a benchmark for comparison with actual performance. See Project Control." (Peter Oakander et al, "CPM Scheduling for Construction: Best Practices and Guidelines", 2014)
02 January 2017
#️⃣Software Engineering: Programming (Part VII: Documentation - Lessons Learned)
Software Engineering Series
Introduction
“Documentation is a love letter that you write to your future self.”
Damian Conway
For programmers as well for other professionals who write code, documentation might seem a waste of time, an effort few are willing to make. On the other side documenting important facts can save time sometimes and provide a useful base for building own and others’ knowledge. I found sometimes on the hard way what I needed to document. With the hope that others will benefit from my experience, here are my lessons learned:
Lesson #1: Document your worked tasks
“The more transparent the writing, the more visible the poetry.”
Gabriel Garcia Marquez
Personally I like to keep a list with what I worked on a daily basis – typically nothing more than 3-5 words description about the task I worked on, who requested it, and eventually the corresponding project, CR or ticket. I’m doing it because it makes easier to track my work over time, especially when I have to retrieve some piece of information that is somewhere else in detail documented.
Within the same list one can track also the effective time worked on a task, though I find it sometimes difficult, especially when one works on several tasks simultaneously. In theory this can be used to estimate further similar work. One can use also a categorization distinguishing for example between the various types of work: design, development, maintenance, testing, etc. This approach offers finer granularity, especially in estimations, though more work is needed in tracking the time accurately. Therefore track the information that worth tracking, as long there is value in it.
Documenting tasks offers not only easier retrieval and base for accurate estimations, but also visibility into my work, for me as well, if necessary, for others. In addition it can be a useful sensemaking tool (into my work) over time.
Lesson #2: Document your code
“Always code as if the guy who ends up maintaining your code will be
a violent psychopath who knows where you live.”
Damian Conway
There are split opinions over the need to document the code. There are people who advise against it, and probably one of most frequent reasons is rooted in Agile methodology. I have to stress that Agile values “working software over comprehensive documentation”, fact that doesn’t imply the total absence of documentation. There are also other reasons frequently advanced, like “there’s no need to document something that’s already self-explanatory “(like good code should be), “no time for it”, etc. Probably in each statement there is some grain of truth, especially when considering the fact that in software engineering there are so many requirements for documentation (see e.g. ISO/IEC 26513:2009).
Without diving too deep in the subject, document what worth documenting, however this need to be regarded from a broader perspective, as might be other people who need to review, modify and manage your code.
Documenting code doesn’t resume only to the code being part of a “deliverables”, but also to intermediary code written for testing or other activities. Personally I find it useful to save within the same fill all the scripts developed within same day. When some piece of code has a “definitive” character then I save it individually for reuse or faster retrieval, typically with a meaningful name that facilitates file’s retrieval. With the code it helps maybe to provide also some metadata like: a short description and purpose (who and when requested it).
Code versioning can be used as a tool in facilitating the process, though not everything worth versioning.
Lesson #3: Document all issues as well the steps used for troubleshooting and fixing
“It’s not an adventure until something goes wrong.”
Yvon Chouinard
Independently of the types of errors occurring while developing or troubleshooting code, one of the common characteristics is that the errors can have a recurring character. Therefore I found it useful to document all the errors I got in terms of screenshots, ways to fix them (including workarounds) and, sometimes also the steps followed in order to troubleshoot the problem.
Considering that the issues are rooted in programming fallacies or undocumented issues, there is almost always something to learn from own as well from others’ errors. In fact, that was the reasons why I started the “SQL Troubles” blog – as a way to document some of the issues I met, to provide others some help, and why not, to get some feedback.
Lesson #4: Document software installations and changes in configurations
At least for me this lesson is rooted in the fact that years back quite often release candidate as well final software was not that easy to install, having to deal with various installation errors rooted in OS or components incompatibilities, invalid/not set permissions, or unexpected presumptions made by the vendor (e.g. default settings). Over the years installation became smoother, though such issues are still occurring. Documenting the installation in terms of screenshots with the setup settings allows repeating the steps later. It can also provide a base for further troubleshooting when the configuration within the software changed or as evidence when something goes wrong.
Talking about changes occurring in the environment, not often I found myself troubleshooting something that stopped working, following to discover that something changed in the environment. It’s useful to document the changes occurring in an environment, importance stressed also in “Configuration Management” section of ITIL® (Information Technology Infrastructure Library).
Lesson #5: Document your processes
“Verba volant, scripta manent.” Latin proverb
"Spoken words fly away, written words remain."
In process-oriented organizations one has the expectation that the processes are documented. One can find that it’s not always the case, some organization relying on the common or individual knowledge about the various processes. Or it might happen that the processes aren’t always documented to the level of detail needed. What one can do is to document the processes from his perspective, to the level of detail needed.
Lesson #6: Document your presumptions
“Presumption first blinds a man, then sets him a running.”
Benjamin Franklin
Probably this is more a Project Management related topic, though I find it useful also when coding: define upfront your presumptions/expectations – where should libraries lie, the type and format of content, files’ structure, output, and so on. Even if a piece of software is expected to be a black-box with input and outputs, at least the input, output and expectations about the environment need to be specified upfront.
Lesson #7: Document your learning sources
“Intelligence is not the ability to store information, but to know where to find it.”
Albert Einstein
Computer specialists are heavily dependent on internet to keep up with the advances in the field, best practices, methodologies, techniques, myths, and other knowledge. Even if one learns something, over time the degree of retention varies, and it can decrease significantly if it wasn’t used for a long time. Nowadays with a quick search on internet one can find (almost) everything, though the content available varies in quality and coverage, and it might be difficult to find the same piece of information. Therefore, independently of the type of source used for learning, I found it useful to document also the information sources.
Lesson #8: Document the known as well the unknown
“A genius without a roadmap will get lost in any country but an average person
with a roadmap will find their way to any destination.”
Brian Tracy
Over the years I found it useful to map and structure the learned content for further review, sometimes considering only key information about the subject like definitions, applicability, limitations, or best practices, while other times I provided also a level of depth that allow me and others to memorize and understand the topic. As part of the process I attempted to keep the copyright attributions, just in case I need to refer to the source later. Together with what I learned I considered also the subjects that I still have to learn and review for further understanding. This provides a good way to map what I known as well what isn’t know. One can use for this a rich text editor or knowledge mapping tools like mind mapping or concept mapping.
Conclusion
Documentation doesn’t resume only to pieces of code or software but also to knowledge one acquires, its sources, what it takes to troubleshoot the various types of issues, and the work performed on a daily basis. Documenting all these areas of focus should be done based on the principle: “document everything that worth documenting”.
⛏️Data Management: Information (Definitions)
"Information is data that increases the knowledge of the person who consumes it. Information is distinguished from data in that data may or may not be meaningful whereas information is always meaningful. For example, the numeric portion of an address is data, but it is not information." (Microsoft Corporation, "Microsoft SQL Server 7.0 Data Warehouse Training Kit", 2000)
"Usable, processed data, typically output from a computer program." (Greg Perry, "Sams Teach Yourself Beginning Programming in 24 Hours" 2nd Ed., 2001)
"Data that has been processed in such a way that it can increase the knowledge of the person who receives it." (Sharon Allen & Evan Terry, "Beginning Relational Data Modeling" 2nd Ed., 2005)
"data that human beings assimilate and evaluate to solve a problem or make a decision." (William H Inmon, "Building the Data Warehouse", 2005)
"Information is data with context. It can be externally validated and is independent of applications." (Tom Petrocelli, "Data Protection and Information Lifecycle Management", 2005)
"Information can be defined as all inputs that people process to gain understanding." (Martin J Eppler, "Managing Information Quality" 2nd Ed., 2006)
"Sets of data presented in a context. Information about a business and its environment." (Steve Williams & Nancy Williams, "The Profit Impact of Business Intelligence", 2007)
"1.Generally, understanding concerning any objects such as facts, events, things, processes, or ideas, including concepts that, within a certain context and timeframe, have a particular meaning." (DAMA International, "The DAMA Dictionary of Data Management", 2011)
"Data that have been organized so they have meaning and value to the recipient." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed, 2011)
"Refers to all or part of a raw data item, which, on examination, turns out to be of interest. Such interest can be justified by means of explicit criteria. Also denotes an observation conducted in the field." (Humbert Lesca & Nicolas Lesca, "Weak Signals for Strategic Intelligence: Anticipation Tool for Managers", 2011)
"The result of processing raw data to reveal its meaning. Information consists of transformed data and facilitates decision making." (Carlos Coronel et al, "Database Systems: Design, Implementation, and Management 9th Ed", 2011)
"Data with additional context in the form of metadata, including definition and relationships between data and possibly other information. Data in context with metadata makes information." (Craig S Mullins, "Database Administration", 2012)
"In the context of this book, a collection of descriptors derived from observation, measurement, calculation, inference, or imagination in a form that can be shared with or communicated to others, or both. The format can be tangible or intangible or some combination of both." (Kenneth A Shaw, "Integrated Management of Processes and Information", 2013)
"An organised and formatted collection of data" (David Sutton, "Information Risk Management: A practitioner’s guide", 2014)
"Any communication on or representation of facts or data in all forms (textual, graphical, audiovisual, digital)." (Gilbert Raymond & Philippe Desfray, "Modeling Enterprise Architecture with TOGAF", 2014)
"Data that has been organized or processed in a useful manner" (Nell Dale & John Lewis, "Computer Science Illuminated" 6th Ed., 2015)
"One view understands information to be content- and purpose- specific knowledge, which is exchanged during human communication. Another takes the view of a purely informational processing perspective, according to which data is the building blocks for information. Accordingly, data is processed into information." (Boris Otto & Hubert Österle, "Corporate Data Quality", 2015)
"Data that has been processed to create meaning. Information is intended to expand the knowledge of the person who receives it. Information is the output of decision support systems and information systems." (Ciara Heavin & Daniel J Power, "Decision Support, Analytics, and Business Intelligence 3rd Ed.", 2017)
"Organized or structured data, processed for a specific purpose to make it meaningful, valuable, and useful in specific contexts." (Project Management Institute, "A Guide to the Project Management Body of Knowledge (PMBOK® Guide)", 2017)
"A structured collection of data presented in a form that people can understand and process. Information is converted into knowledge when it is contextualised with the rest of a person’s knowledge and world model." (Open Data Handbook)
About Me
- Adrian
- Koeln, NRW, Germany
- IT Professional with more than 24 years experience in IT in the area of full life-cycle of Web/Desktop/Database Applications Development, Software Engineering, Consultancy, Data Management, Data Quality, Data Migrations, Reporting, ERP implementations & support, Team/Project/IT Management, etc.