As with all new terminology, a whole range of definitions has been made to describe Big Data (for a great analysis on a number of those, we refer to Gil Press’ blog at Forbes). In 2001 the Meta Group already distinguished Big Data using the 3 V’s: Volume, Variety and Velocity. Today those have been expanded up to seven V’s, including Viscosity, Virality, Veracity and Value:
- Volume – relates to the greater size of the data set and mainly the processing ability of this data. Data generation as well as processing have been growing exponentially; research has shown that 90% of the world’s data has been generated in the past 2 years.
- Variety – refers to the large variety of data that is being generated today. This includes many ‘new’ forms of data from social, machine-to-machine communication, and mobile apparatus (Internet of Things), most of which traditional databases cannot yet process and analyse.
- Velocity – relates to the greater speed at which data is generated (often real-time), as well as the temporary value of the data.
- Viscosity – refers to the inertia when navigating through a data collection. For example due to the variety of data sources, the velocity of data flows and the complexity of the required processing.
- Virality – measures the speed at which data can spread through a network. i-have-data-cro
- Veracity – relates to the quality and origin of the data to determine whether it is trustworthy, conflicting or impure.
- Value – refers to the value that could be extracted from certain data and how Big Data techniques could increase this value.
So in short, as Gartner defines: “Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.”
in: http://www.bigdata-alliance.org/what-is-big-data
http://www.bigdata-alliance.org/people/prin":
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She was a member of the Dutch Cyber Security Council (advisory body of the Dutch cabinet on cybersecurity). Her present research topics include legal, regulatory and ethical aspects of data science, legal analytics, privacy and identity management, commodification & propertisation of data, identity theft/fraud and on-line identity management.
http://www.bigdata-alliance.org/people/rinnooy-kan
http://www.bigdata-alliance.org/big-data-in-het-hoger-onderwijs-vloek-of-zegen
http://www.bigdata-alliance.org/big-data-threat-to-privacy-of-individuals:
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What you should regulate is decisions based on the outcomes of analytics: can you discriminate based on health, lifestyle, wealth or genetics? That is a debate we need to have.
https://nl.wikipedia.org/wiki/Stichting_Lezen_&_Schrijven
http://www.forbes.com/sites/howardbaldwin/2015/01/05/what-should-data-scientists-know/#2715e4857a0b6d2e1bf637e8.
http://www.ad.nl/ad/nl/1012/Nederland/article/detail/3762026/2014/10/04/Vriendjespolitiek-op-ministerie-van-OCW.dhtml
About Business Ethics, Verantwoord Maatschappelijk Ondernemen, Corporate Social Responsibility, Responsibilities.
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