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.
BT - Information & Security: An International Journal DA - 2020 DO - https://doi.org/10.11610/isij.4711 IS - 2 LA - eng N2 -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.
PY - 2020 SE - 157 SP - 157 EP - 171 T2 - Information & Security: An International Journal TI - Data Science as a Service: The Data Range VL - 47 ER -