In all industries, we have seen how DevOps and DataOps have been widely adopted as methodologies to improve the quality and reduce time-to-market for software engineering and data engineering initiatives, respectively. However, the debate on MLOps often focuses exclusively on tools, overlooking a critical aspect of the success of Machine Learning (ML) investment – enabling individuals to achieve their goals. This implies designing the appropriate structure for your organization.

Furthermore, it is essential to consider the unique aspects of machine learning, and how the general principles of software development may not always be applicable to these projects.

Building a data-driven organization comes with numerous barriers and challenges related to change management, habit modification, and providing an organization with role models and practical assistance in data management and utilization. It involves a cultural shift and a change in the way things are done.

At Bluetab we believe in the vision of comprehensive data governance, an approach that combines global principles and policies for the most relevant aspects, and decentralized and federated management by data domains.

In this article we focus on the role of the Central Office (Data Management Office), as well as the infrastructure or data platform and data domains, in a federated environment. In addition, we tell you our recommendations for the transition towards adding value through data.

From Bluetab we think that the importance of governing the entire life cycle of the data has grown. We believe that a new approach is necessary, to evolve Towards a Data Government 2.0 that takes into account: