Big data in the context of Data visualization

A buzzword is a word or phrase, new or already existing, that becomes popular for a period of time. Buzzwords often derive from technical terms yet often have much of the original technical meaning removed through fashionable use, being simply used to impress others. Some buzzwords retain their true technical meaning when used in the correct contexts, for example artificial intelligence.Buzzwords often originate in jargon, acronyms, or neologisms. Examples of overworked business buzzwords include synergy, vertical, dynamic, cyber and strategy.

It has been stated that businesses could not operate without buzzwords, as they are the shorthands or internal shortcuts that make perfect sense to people informed of the context. However, a useful buzzword can become co-opted into general popular speech and lose its usefulness. According to management professor Robert Kreitner, "Buzzwords are the literary equivalent of Gresham's law. They will drive out good ideas."Buzzwords, or buzz-phrases such as "all on the same page", can also be seen in business as a way to make people feel like there is a mutual understanding. As most workplaces use a specialized jargon, which could be argued is another form of buzzwords, it allows quicker communication. Indeed, many new hires feel more like "part of the team" the quicker they learn the buzzwords of their new workplace. Buzzwords permeate people's working lives so much that many do not realize that they are using them. The vice president of CSC Index, Rich DeVane, notes that buzzwords describe not only a trend, but also what can be considered a "ticket of entry" with regard to being considered as a successful organization – "What people find tiresome is each consulting firm's attempt to put a different spin on it. That's what gives bad information."

Pattern recognition is the task of assigning a class to an observation based on patterns extracted from data. While similar, pattern recognition (PR) is not to be confused with pattern machines (PM) which may possess PR capabilities but their primary function is to distinguish and create emergent patterns. PR has applications in statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning. Pattern recognition has its origins in statistics and engineering; some modern approaches to pattern recognition include the use of machine learning, due to the increased availability of big data and a new abundance of processing power.

Pattern recognition systems are commonly trained from labeled "training" data. When no labeled data are available, other algorithms can be used to discover previously unknown patterns. KDD and data mining have a larger focus on unsupervised methods and stronger connection to business use. Pattern recognition focuses more on the signal and also takes acquisition and signal processing into consideration. It originated in engineering, and the term is popular in the context of computer vision: a leading computer vision conference is named Conference on Computer Vision and Pattern Recognition.

Analytics is the systematic computational analysis of data or statistics. It is used for the discovery, interpretation, and communication of meaningful patterns in data, which also falls under and directly relates to the umbrella term, data science. Analytics also entails applying data patterns toward effective decision-making. It can be valuable in areas rich with recorded information; analytics relies on the simultaneous application of statistics, computer programming, and operations research to quantify performance.

Organizations may apply analytics to business data to describe, predict, and improve business performance. Specifically, areas within analytics include descriptive analytics, diagnostic analytics, predictive analytics, prescriptive analytics, and cognitive analytics. Analytics may apply to a variety of fields such as marketing, management, finance, online systems, information security, and software services. Since analytics can require extensive computation (see big data), the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics. According to International Data Corporation, global spending on big data and business analytics (BDA) solutions is estimated to reach $215.7 billion in 2021. As per Gartner, the overall analytic platforms software market grew by $25.5 billion in 2020.

Business intelligence (BI) consists of strategies, methodologies, and technologies used by enterprises for data analysis and management of business information to inform business strategies and business operations. Common functions of BI technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, and prescriptive analytics.

BI tools can handle large amounts of structured and sometimes unstructured data to help organizations identify, develop, and otherwise create new strategic business opportunities. They aim to allow for the easy interpretation of these big data. Identifying new opportunities and implementing an effective strategy based on insights is assumed to potentially provide businesses with a competitive market advantage and long-term stability, and help them take strategic decisions.

Conduit OFC and sink OFC is an empirical quantitative method of classifying corporate tax havens, offshore financial centres (OFCs) and tax havens.

Traditional methods for identifying tax havens analyse tax and legal structures for base erosion and profit shifting (BEPS) tools. However, this approach follows a purely quantitative approach, ignoring any taxation or legal concepts, to instead follow a big data analysis of the ownership chains of 98 million global companies. The technique gives both a method of classification and a method of understanding the relative scale – but not absolute scale – of havens/OFCs.

Vehicle location data is the big data collection of vehicle locations, including automatic vehicle location data, a core feature of any vehicle tracking system. This usually includes times and often photographs as well, a practice known as video telematics. The process of collecting this data from remote assets via telemetry is a core component of telematics, often managed by a telematic control unit. Its application in the commercial sector forms the basis of fleet digitalization and is central to any fleet telematics system.

Common methods of data collection include automatic number plate recognition from cameras, such as a Dashcam, and radio-frequency identification (RFID) from transponders. In commercial contexts, a dedicated GPS tracking unit is often used for this purpose, forming part of a wider tracking system. Databases of this information are maintained by both government and private entities. For businesses, this data is essential for fleet management tasks like Track and trace, enabling vehicle repossession, and consumer profiling through methods like Driver scoring. Government databases have been subjected to legal orders for location data, and access may be granted in both criminal and civil cases.

The term digital citizen is used with different meanings. According to the definition provided by Karen Mossberger, one of the authors of Digital Citizenship: The Internet, Society, and Participation, digital citizens are "those who use the internet regularly and effectively." In this sense, a digital citizen is a person using information technology (IT) in order to engage in society, politics, and government.

More recent elaborations of the concept define digital citizenship as the self-enactment of people’s role in society through the use of digital technologies, stressing the empowering and democratizing characteristics of the citizenship idea. These theories aim at taking into account the ever increasing datafication of contemporary societies (as can be symbolically linked to the Snowden leaks), which radically called into question the meaning of “being (digital) citizens in a datafied society”, also referred to as the “algorithmic society”, which is characterised by the increasing datafication of social life and the pervasive presence of surveillance practices – see surveillance and surveillance capitalism, the use of artificial intelligence, and Big Data.

E-Science or eScience is computationally intensive science that is carried out in highly distributed network environments, or science that uses immense data sets that require grid computing. The term sometimes includes technologies that enable distributed collaboration, such as the Access Grid. The term was created by John Taylor, the Director General of the United Kingdom's Office of Science and Technology in 1999 and was used to describe a large funding initiative starting in November 2000. E-science has been more broadly interpreted since then as "the application of computer technology to the undertaking of modern scientific investigation", including the preparation, experimentation, data collection, results dissemination, and long-term storage and accessibility of all materials generated through the scientific process. These may include data modeling and analysis, electronic/digitized laboratory notebooks, raw and fitted data sets, manuscript production and draft versions, pre-prints, and print and/or electronic publications." In 2014, IEEE eScience Conference Series condensed the definition to "eScience promotes innovation in collaborative, computationally- or data-intensive research across all disciplines, throughout the research lifecycle" in one of the working definitions used by the organizers. E-science encompasses "what is often referred to as big data [which] has revolutionized science... [such as] the Large Hadron Collider (LHC) at CERN... [that] generates around 780 terabytes per year... highly data intensive modern fields of science...that generate large amounts of E-science data include: computational biology, bioinformatics, genomics" and the human digital footprint for the social sciences.

Turing Award winner Jim Gray imagined "data-intensive science" or "e-science" as a "fourth paradigm" of science (empirical, theoretical, computational and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge.

In computer science, uncertain data is data that contains noise that makes it deviate from the correct, intended or original values. In the age of big data, uncertainty or data veracity is one of the defining characteristics of data. Data is constantly growing in volume, variety, velocity and uncertainty (1/veracity). Uncertain data is found in abundance today on the web, in sensor networks, within enterprises both in their structured and unstructured sources. For example, there may be uncertainty regarding the address of a customer in an enterprise dataset, or the temperature readings captured by a sensor due to aging of the sensor. In 2012 IBM called out managing uncertain data at scale in its global technology outlook report that presents a comprehensive analysis looking three to ten years into the future seeking to identify significant, disruptive technologies that will change the world. In order to make confident business decisions based on real-world data, analyses must necessarily account for many different kinds of uncertainty present in very large amounts of data. Analyses based on uncertain data will have an effect on the quality of subsequent decisions, so the degree and types of inaccuracies in this uncertain data cannot be ignored.

Uncertain data is found in the area of sensor networks; text where noisy text is found in abundance on social media, web and within enterprises where the structured and unstructured data may be old, outdated, or plain incorrect; in modeling where the mathematical model may only be an approximation of the actual process. When representing such data in a database, an appropriate uncertain database model needs to be selected.