<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Peter Lenk</style></author><author><style face="normal" font="default" size="100%">Michael Street</style></author><author><style face="normal" font="default" size="100%">Ivana Ilic Mestric</style></author><author><style face="normal" font="default" size="100%">Arvid Kok</style></author><author><style face="normal" font="default" size="100%">Giavid Valiyev</style></author><author><style face="normal" font="default" size="100%">Philippe Le Cerf</style></author><author><style face="normal" font="default" size="100%">Barbara Lorincz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Data Science as a Service: The Data Range</style></title><secondary-title><style face="normal" font="default" size="100%">Information &amp; Security: An International Journal</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">artificial intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">big data</style></keyword><keyword><style  face="normal" font="default" size="100%">charge back</style></keyword><keyword><style  face="normal" font="default" size="100%">data engineer</style></keyword><keyword><style  face="normal" font="default" size="100%">data engineering</style></keyword><keyword><style  face="normal" font="default" size="100%">data range</style></keyword><keyword><style  face="normal" font="default" size="100%">data science</style></keyword><keyword><style  face="normal" font="default" size="100%">data science as a service</style></keyword><keyword><style  face="normal" font="default" size="100%">machine learning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">47</style></volume><pages><style face="normal" font="default" size="100%">157-171</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">As with many new disciplines, in many organisations data science is being embraced in a piecemeal way with many parts of organisations creating special purpose environments designed to answer specific problems, fragmenting the overall capacity and knowledge base. Often vendors selling proprietary approaches, potentially creating lock-in, fuel these isolated solutions. This article’s main contribution is a ‘Data Science as a Service (DSaaS)’ model, where common elements required to conduct data science are abstracted and gathered into a logical layered, service-based architecture. This way, each element of the organisation can utilise the services it needs to progress its work, use specific solutions or share common tool sets, share results in a ‘model zoo,’ share data sets, share best practices and benefit from common, robust high-performance infrastructure and tools. With such an approach, it is possible to cluster data science skill sets and provide critical mass where needed. The proposed approach also facilitates a charge-back business model, where data science services are costed and charged to internal organisational elements or external customers in a measured, pay-as-you go way.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><section><style face="normal" font="default" size="100%">157</style></section></record></records></xml>