To succeed in complex environments and handle the innovation, development and support,
organizations have to find ways to support and regulate the autonomy of teams according to …

Datafication processes, the ongoing strive for making organizations data-driven, have in
recent years entailed data-focused software projects and more interdisciplinary teamwork …

Organizations must constantly evolve and adapt to meet customer demands in an ever-
changing business environment. The most notable differences have been in the way fintech …

In recent years, the push to make organizations data-driven has led to data-focused software
projects, both in the private and public sectors. The strive for increasing data-driven …

Context: Organizations opt for continuous delivery of incremental updates to deal with
uncertainty and minimize waste. However, applying continuous engineering (CSE) practices …

Data science is an emerging discipline with a particular research focus on improving the
available techniques for data analysis. While the number of data science projects is growing …

Machine learning operations (MLOps) is an emerging and complex subject area involving
experts from several fields and backgrounds. Its main purpose is to enable a more …

Intelligence (AI), Machine Learning (ML) and Data Science (DS) projects. Although Agile
approaches are increasingly popular in software development, managing and executing …

Data scientists are new knowledge workers representing the knowledge economy era.
Knowledge workers perform unstandardized works that solve ambiguity-intensive problems …

The global economy, driven by both confidential and communal areas, dynamically depends
on the successful finishing of ventures, with trillions of dollars contributed every year. Even …