Organizations seek to streamline their data and analytics structures in order to meet increasingly demanding business requirements. This can be difficult due to complex and fast-moving data landscapes. DataOps promises a remedy by combining an integrated and process-oriented perspective on data with automation and methods from agile software engineering, like DevOps, to improve quality, speed, and collaboration and promote a culture of continuous improvement. The goal of this on-going research is to elaborate DataOps as a new discipline. For this, it explores the body of knowledge and presents a working definition of DataOps as well as an initial research framework based on an explorative literature review and eight interviews with industry experts.
%0 Journal Article
%1 noauthororeditor
%A Ereth, Julian
%B Proceedings of the Conference „Lernen, Wissen, Daten, Analysen“
%C Mannheim
%D 2018
%K definition seminar
%P 104-112
%T DataOps – Towards a Definition
%U http://ceur-ws.org/Vol-2191/paper13.pdf
%X Organizations seek to streamline their data and analytics structures in order to meet increasingly demanding business requirements. This can be difficult due to complex and fast-moving data landscapes. DataOps promises a remedy by combining an integrated and process-oriented perspective on data with automation and methods from agile software engineering, like DevOps, to improve quality, speed, and collaboration and promote a culture of continuous improvement. The goal of this on-going research is to elaborate DataOps as a new discipline. For this, it explores the body of knowledge and presents a working definition of DataOps as well as an initial research framework based on an explorative literature review and eight interviews with industry experts.
@article{noauthororeditor,
abstract = {Organizations seek to streamline their data and analytics structures in order to meet increasingly demanding business requirements. This can be difficult due to complex and fast-moving data landscapes. DataOps promises a remedy by combining an integrated and process-oriented perspective on data with automation and methods from agile software engineering, like DevOps, to improve quality, speed, and collaboration and promote a culture of continuous improvement. The goal of this on-going research is to elaborate DataOps as a new discipline. For this, it explores the body of knowledge and presents a working definition of DataOps as well as an initial research framework based on an explorative literature review and eight interviews with industry experts.},
added-at = {2023-05-05T15:45:28.000+0200},
address = {Mannheim},
author = {Ereth, Julian},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2c94f644040a5facd3f00014ec211e34b/lucabennardo},
booktitle = {Proceedings of the Conference „Lernen, Wissen, Daten, Analysen“},
interhash = {4c9ff70ac7bce1c060533832292e9497},
intrahash = {c94f644040a5facd3f00014ec211e34b},
keywords = {definition seminar},
pages = {104-112},
timestamp = {2023-05-05T15:46:19.000+0200},
title = {DataOps – Towards a Definition},
url = {http://ceur-ws.org/Vol-2191/paper13.pdf},
year = 2018
}
