37 lines
6.4 KiB
Markdown
37 lines
6.4 KiB
Markdown
---
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title: "Big data"
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chunk: 5/10
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source: "https://en.wikipedia.org/wiki/Big_data"
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category: "reference"
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tags: "science, encyclopedia"
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date_saved: "2026-05-05T09:53:36.506913+00:00"
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instance: "kb-cron"
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---
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=== Healthcare ===
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Big data analytics has been used in healthcare in providing personalized medicine and prescriptive analytics, clinical risk intervention and predictive analytics, waste and care variability reduction, automated external and internal reporting of patient data, standardized medical terms and patient registries. Some areas of improvement are more aspirational than actually implemented. The level of data generated within healthcare systems is not trivial. With the added adoption of mHealth, eHealth and wearable technologies the volume of data will continue to increase. This includes electronic health record data, imaging data, patient generated data, sensor data, and other forms of difficult to process data. There is now an even greater need for such environments to pay greater attention to data and information quality. "Big data very often means 'dirty data' and the fraction of data inaccuracies increases with data volume growth." Human inspection at the big data scale is impossible and there is a desperate need in health service for intelligent tools for accuracy and believability control and handling of information missed. While extensive information in healthcare is now electronic, it fits under the big data umbrella as most is unstructured and difficult to use. The use of big data in healthcare has raised significant ethical challenges ranging from risks for individual rights, privacy and autonomy, to transparency and trust.
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Big data in health research is particularly promising in terms of exploratory biomedical research, as data-driven analysis can move forward more quickly than hypothesis-driven research. Then, trends seen in data analysis can be tested in traditional, hypothesis-driven follow up biological research and eventually clinical research.
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A related application sub-area, that heavily relies on big data, within the healthcare field is that of computer-aided diagnosis in medicine. For instance, for epilepsy monitoring it is customary to create 5 to 10 GB of data daily. Similarly, a single uncompressed image of breast tomosynthesis averages 450 MB of data. These are just a few of the many examples where computer-aided diagnosis uses big data. For this reason, big data has been recognized as one of the seven key challenges that computer-aided diagnosis systems need to overcome in order to reach the next level of performance.
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=== Education ===
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A McKinsey Global Institute study found a shortage of 1.5 million highly trained data professionals and managers and a number of universities including University of Tennessee and UC Berkeley, have created masters programs to meet this demand. Private boot camps have also developed programs to meet that demand, including paid programs like The Data Incubator or General Assembly. In the specific field of marketing, one of the problems stressed by Wedel and Kannan is that marketing has several sub domains (e.g., advertising, promotions,
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product development, branding) that all use different types of data.
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=== Media ===
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To understand how the media uses big data, it is first necessary to provide some context into the mechanism used for media process. It has been suggested by Nick Couldry and Joseph Turow that practitioners in media and advertising approach big data as many actionable points of information about millions of individuals. The industry appears to be moving away from the traditional approach of using specific media environments such as newspapers, magazines, or television shows and instead taps into consumers with technologies that reach targeted people at optimal times in optimal locations. The ultimate aim is to serve or convey, a message or content that is (statistically speaking) in line with the consumer's mindset. For example, publishing environments are increasingly tailoring messages (advertisements) and content (articles) to appeal to consumers that have been exclusively gleaned through various data-mining activities.
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Targeting of consumers (for advertising by marketers)
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Data capture
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Data journalism: publishers and journalists use big data tools to provide unique and innovative insights and infographics.
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Channel 4, the British public-service television broadcaster, is a leader in the field of big data and data analysis.
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=== Insurance ===
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Health insurance providers are collecting data on social "determinants of health" such as food and TV consumption, marital status, clothing size, and purchasing habits, from which they make predictions on health costs, in order to spot health issues in their clients. It is controversial whether these predictions are currently being used for pricing.
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=== Internet of things (IoT) ===
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Big data and the IoT work in conjunction. Data extracted from IoT devices provides a mapping of device inter-connectivity. Such mappings have been used by the media industry, companies, and governments to more accurately target their audience and increase media efficiency. The IoT is also increasingly adopted as a means of gathering sensory data, and this sensory data has been used in medical, manufacturing and transportation contexts.
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Kevin Ashton, the digital innovation expert who is credited with coining the term, defines the Internet of things in this quote: "If we had computers that knew everything there was to know about things—using data they gathered without any help from us—we would be able to track and count everything, and greatly reduce waste, loss, and cost. We would know when things needed replacing, repairing, or recalling, and whether they were fresh or past their best."
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=== Information technology ===
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Especially since 2015, big data has come to prominence within business operations as a tool to help employees work more efficiently and streamline the collection and distribution of information technology (IT). The use of big data to resolve IT and data collection issues within an enterprise is called IT operations analytics (ITOA). By applying big data principles into the concepts of machine intelligence and deep computing, IT departments can predict potential issues and prevent them. ITOA businesses offer platforms for systems management that bring data silos together and generate insights from the whole of the system rather than from isolated pockets of data. |