Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementA lot of organizations implemented DevOps processes with success. This allowed different
areas like development, operations, security and quality work together. This cooperation, and
processes associated to the work with these areas are producing excellent results. The
organizations are developing many applications that support operation and are producing a
lot of data. This data has a significant value for organizations because must be used in analysis,
reporting and more recently data science projects to support decisions.
It is time to take decisions supported in data and for this is necessary to transform
organizations in a data-driven organizations and for this we need processes to deal with this
data across all teams.
This dissertation follows a design science research approach to apply multiple analytical
methods and perspectives to create an artifact. The type of evidence within this methodology
is a systematic literature review, with the goal to attain insights into the current state-of-the art research of DataOps implementation. Additionally, proven best practices from the industry
are examined in depth to further strengthen the credibility. Thereby, the systematic literature
review shall be used to pinpoint, analyze, and comprehend the obtainable empirical studies
and research questions. This methodology supports the main goal of this dissertation, to
develop and propose evidence-based practice guidelines for the DataOps implementation
that can be followed by organizations
