Open AccessGood practices for the adoption of DataOps in the software industry
Author(s) -
Manuel Rodrı́guez,
Luiz Jonatã Pires de Araújo,
Manuel Mazzara
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1694/1/012032
Subject(s) - devops , process (computing) , data science , computer science , value (mathematics) , software , best practice , knowledge management , process management , business , management , machine learning , economics , programming language , operating system
The increasing adoption of DevOps, the growing availability of data concerning data development processes gives rise to the need for a systematic process for collecting, processing and using data into companies. Enterprises are making significant investments in data science applications while still struggling to realize the value of this effort. Data science is emerging as a fast-growing practice within enterprises. Several tools and platforms are being continuously introduced that support data science models while managing large data sets used to train data science models. Such a scenario lead to the emergence of DataOps. This paper summarises some of the good practices in the DataOps from the literature, offering guidelines intended to approach an organizational shift towards better data-driven decision making. This study presents a picture of the definition, the steps for adopting and challenges of the adoption of DataOps.