In the context of the mining industry, there are currently concerted efforts to optimize and enhance the activities and processes of mineral extraction and processing. The Digital Transformation plays a crucial role in the integration of new digital technologies. Industry 4.0 serves as a significant driver, based on principles such as modularity, interoperability, virtualization, decentralization, real-time capabilities, and service orientation.

Industry 4.0 encompasses a wide range of Advanced Technologies within its portfolio, including Big Data Analytics, Cloud Computing, Extended Reality (XR), 3D Printing, Internet – of – Things ( IoT ), Industrial Internet- of – Thins ( IIoT ). , Blockchain Technology , Computer Simulation , Digital Twin, among others. As a result of the adoption of one or more of these technologies promoted by industry 4.0, companies in the mining sector face the following challenges:

1. Managing large volumes of data from heterogeneous and/or disparate sources.
2. Addressing data quality problems, possibly due to sensors with low precision, incorrect readings, among other issues.
3. Optimizing the Mine-to-Mill process centralizing and integrating the data generated by various mining areas including drilling, blasting, geology, geotechnics, planning, mining operation, among others, while reducing their complexity in discovery.
4. Achieving efficient scalability in a cost-effective manner, at this point cloud adoption with all its challenges becomes urgent.
5. Within the staff of mining companies, it is essential to have personnel with skills and experience in data science, in addition to evaluating appropriate technologies.

To overcome the aforementioned challenges, this article proposes that organizations,  within their data-driven strategies, develop Data Governance and Architecture Practices to ensure that the right people participate in the determining standards, the use and integration of data between the different business units and/or mining areas, to guarantee that data management practices are aligned with the organization’s business objectives, in addition to allowing risk reduction in various mining operations.

It is known that implementing the practice of data architecture and governance is not an easy task and demands a high level of culture, awareness, and commitment from senior management, for this the following lines of action are recommended:

1. At a strategic level, formalize Data Governance through a program within the organization that is attached to the project office (PMO) and is led by a Chief Data Officer (CDO). This area should work on a data culture plan directed at both the strategic, tactical, and operational levels.

• Start the implementation from less to more, choosing a data-driven business case that provides value to the organization and is not overly complex, in which value can delivered within a maximum period of 6 months, so that the governance and data architecture foundations are established for subsequent modular business cases.

• Adopt one or more frameworks that are aligned with the organization’s strategy and initiatives. This article recommends reviewing and analyzing the following frameworks:

1. DMBOK v.2 of DAMA, for data management.
2. DCAM CMMI, For the maturity model of data management capabilities.
3. TOGAF v.10 Open Group, for enterprise architecture practice and management.
4. SABSA Institute, for security and risk architecture practice at the data architecture level
5. NIST CSF v2.0, to managing cybersecurity in data management.
6. ISO 27001:2022, for establishing the SGSI in the data privacy and security chapter.
7. LGPD, ISO 27701, for the requirements for the implementation, maintenance and continuous improvement of an Information and Privacy Management System.

• Adopt and promote a federated operational governance to enable independence, democratization, self-service, and decentralization of data for better agility in the development of data-driven digital products, placing the data product as the minimum unit of governance to increase the value in the quality and lineage of your data.

Within the MS4M product suite we have Data For Miners (D4M), which is a technological platform with good governance practices that will help mining companies proactively adopt data governance and architecture in their organizations.