#315: Programvare for å lede fremtidens bedrifter
Astrid Thommesen Sæbø
Fra SAP Norway
#285: Salgs-og markedsføringsteknologi
#184: Digitalisering for en bedre verden
Fra Høgskolen i Molde
#075: Fire V’er
Fra DNV GL
#069: Big Data og personvern
#54: Store data — store muligheter
Fra DNV GL
#52: Anvendelse av data
#51: «Virtuelle maskiner»
Fra eSmart Systems
#50: Big Data — en kilde til innovasjon
Fra Acando Norge
#49: Mennesket i grensesnittet mot teknologi
#48: Ansvarlig bruk av stordata
Fra Norsk Regnesentral
#47: Hva er CRISP-metoden?
#46: Fysikk + statistikk = diagnostistikk
Fra NTNU, TEKNA
#45: Digitale tvillinger
Fra Kongsberg Digital
#44: Big Data og geometrisk modellering
Fra SINTEF Digital
#43: Slik kan Big Data predikere fremtiden
Fra Bane NOR
#42: Data som beskriver den industrielle virkeligheten
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.»