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.
Description
Good practices for the adoption of DataOps in the software industry - IOPscience
%0 Journal Article
%1 Rodriguez_2020
%A Rodriguez, Manuel
%A de Araújo, Luiz Jonatã Pires
%A Mazzara, Manuel
%D 2020
%I IOP Publishing
%J Journal of Physics: Conference Series
%K definition seminar
%N 1
%R 10.1088/1742-6596/1694/1/012032
%T Good practices for the adoption of DataOps in the software industry
%U https://dx.doi.org/10.1088/1742-6596/1694/1/012032
%V 1694
%X 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.
@article{Rodriguez_2020,
abstract = {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.},
added-at = {2023-05-05T16:09:12.000+0200},
author = {Rodriguez, Manuel and de Araújo, Luiz Jonatã Pires and Mazzara, Manuel},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2a023632a3c305e8db6d43036155421a4/lucabennardo},
description = {Good practices for the adoption of DataOps in the software industry - IOPscience},
doi = {10.1088/1742-6596/1694/1/012032},
interhash = {a885607e5cb1ac18b87c0af7895db089},
intrahash = {a023632a3c305e8db6d43036155421a4},
journal = {Journal of Physics: Conference Series},
keywords = {definition seminar},
month = {12},
number = 1,
publisher = {IOP Publishing},
timestamp = {2023-05-05T16:09:12.000+0200},
title = {Good practices for the adoption of DataOps in the software industry},
url = {https://dx.doi.org/10.1088/1742-6596/1694/1/012032},
volume = 1694,
year = 2020
}
