BIG.DATA - LØRN.TECH
Tema: BIGDATA

#581: Norge skal bli datadrevne
Kristin Rotevatn Nyberg
Fra Visma bWise
Tema: BIGDATA

#574: Industriell software som eksportvare?
Anna Olsson
Fra Cognite
Tema: BIGDATA

#494 – Vi skal digitalisere snøen!
Monica Vaksdal
Fra Think Outside
Tema: BIGDATA

#422 – Big Data and ‘Social Physics’
Johannes Bjelland
Fra Telenor
Tema: BIGDATA

#413 – Democratization of data
Liv Elise Saune Tøftum
Fra Telenor Norway
Tema: BIGDATA

#394 – Kartteknologi og digital tvilling
Geir Hansen
Fra Geodata
Tema: BIGDATA

#380 – Jordskjelv på månen – jorden og mars
Volker Oye
Fra NORSAR
Tema: BIGDATA

#315 – Programvare for å lede fremtidens bedrifter
Astrid Thommesen Sæbø
Fra SAP Norway
Tema: BIGDATA

#307 – Internasjonalt programvareselskap fra Stavanger
Tor Inge Vasshus
Fra Corporater
Tema: BIGDATA

#285: Salgs-og markedsføringsteknologi
Stig Hammer
Fra MarkedsPartner
Tema: BIGDATA

#184 – Digitalisering for en bedre verden
Bjørn Jæger
Fra Høgskolen i Molde
Tema: BIGDATA

#75 – Fire V’er
Jørgen Kadal
Fra DNV GL
Tema: BIGDATA

#69 – Big Data og personvern
Catharina Nes
Fra Datatilsynet
Tema: BIGDATA

#54 – Store data — store muligheter
Elisabeth Tørstad
Fra DNV GL
Tema: BIGDATA

#52 – Anvendelse av data
Ellie Dobson
Fra Arundo
Tema: BIGDATA

#51 – «Virtuelle maskiner»
Davide Roverso
Fra eSmart Systems
Tema: BIGDATA

#49 – Mennesket i grensesnittet mot teknologi
Heidi Moseng
Fra Skala
Tema: BIGDATA

#50 – Big Data — en kilde til innovasjon
Arturo Amador
Fra Acando Norge
Tema: BIGDATA

#48 – Ansvarlig bruk av stordata
Anders Løland
Fra Norsk Regnesentral
Tema: BIGDATA

#46 – Fysikk + statistikk = diagnostistikk
Lise Randeberg
Fra NTNU, TEKNA
Tema: BIGDATA

#47 – Hva er CRISP-metoden?
Simen Sommerfeldt
Fra Bouvet
Tema: BIGDATA

#45 – Digitale tvillinger
Michael Link
Fra Kongsberg Digital
Tema: BIGDATA

#43 – Slik kan Big Data predikere fremtiden
Sverre Kjenne
Fra Bane NOR
Tema: BIGDATA

#44 – Big Data og geometrisk modellering
Heidi Dahl
Fra SINTEF Digital
Tema: BIGDATA

#42 – Data som beskriver den industrielle virkeligheten
Geir Engdahl
Fra Cognite

https://en.wikipedia.org/wiki/Big_data
Big data is a term used to refer to data sets that are too large or complex for traditional data-processing application software to adequately deal with. Data with many cases (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Big data was originally associated with three key concepts: volume, variety, and velocity. Other concepts later attributed with big data are veracity (i.e., how much noise is in the data) and value.
Current usage of the term “big data” tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. “There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem.” Analysis of data sets can find new correlations to “spot business trends, prevent diseases, combat crime and so on.” Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, fintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, biology and environmental research.
Data sets grow rapidly- in part because they are increasingly gathered by cheap and numerous information- sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks. The world’s technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s; as of 2012, every day 2.5 exabytes (2.5×1018) of data are generated. Based on an IDC report prediction, the global data volume will grow exponentially from 4.4 zettabytes to 44 zettabytes between 2013 and 2020. By 2025, IDC predicts there will be 163 zettabytes of data. One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.
Relational database management systems, desktop statistics and software packages used to visualize data often have difficulty handling big data. The work may require “massively parallel software running on tens, hundreds, or even thousands of servers”. What qualifies as being “big data” varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. “For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration.”

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