Data estate maturity levels for Microsoft Cloud for Sustainability

For organizations with existing sustainability solutions, establishing a sound data strategy for integration with Microsoft Cloud for Sustainability is crucial in driving well-architected efforts. A more integrated solution with Microsoft Cloud for Sustainability allows organizations to streamline their processes, make data-driven decisions, and achieve greater efficiency, all while contributing to a more well-architected solution.

To understand the level of data estate maturity with Microsoft Cloud for Sustainability, there are three data integration levels to consider.

A diagram showing the data estate maturity levels with Microsoft Cloud for industry.

Download a printable PDF of this diagram.

Level 1 Traditional: Isolated data repositories

The isolated data repositories level refers to having separate sustainability systems or databases that store data independently, creating data silos within an organization. It means that data within these repositories isn't easily accessible or shareable with Microsoft Cloud for Sustainability. It results in duplicated data, limited data visibility, and difficulty in making data-driven decisions. The lack of integration with Microsoft Cloud for Sustainability makes it challenging for organizations to gain a comprehensive view of their data, which can affect their ability to make informed decisions and drive business outcomes.

Level 2 Bridge: Combining the data estate

Combining data estate level refers to the data exchange between an organization's legacy systems and the Microsoft Cloud. The data exchange improves data accessibility and quality, and the usage of advanced analytics and other capabilities provided by the Microsoft Cloud. The data exchange process involves:

  • Mapping the data from existing systems to the Microsoft Cloud.
  • Transforming the data to conform to the Microsoft Cloud data model.
  • Loading the data into the Microsoft Cloud.

The data exchange process can also involve real-time data synchronization between existing systems and the Microsoft Cloud, ensuring that data is always up to date and accurate.

Microsoft Cloud provides a range of data integration tools, such as Azure Data Factory, to support the data exchange process. These tools help organizations to manage the data exchange process in an automated and scalable manner, reducing the time and resources required to complete the process.

By exchanging data between existing systems and the Microsoft Cloud, organizations can improve data quality, increase data accessibility, and take advantage of the advanced analytics and other capabilities provided by the Microsoft Cloud. These practices enable organizations to drive better business outcomes and improve their data governance practices.

Level 3 Hub or platform: Centralized data management

Hub: Bringing together the data estate

The data hub level refers to bringing together the data estate to a centralized data management strategy that consolidates all data sources into a single repository, known as the data hub. The hub approach aims to create a single source of truth for an organization's sustainability data, reducing the complexity of the data infrastructure and improving data quality.

The data hub serves as the centralized repository for all sustainability data, which various systems and applications across the organization can access. In addition to improving data quality and accuracy, the hub approach also enables organizations to improve data governance by providing a centralized location for managing and maintaining the data estate. This approach includes defining data standards, managing data access and security, and auditing and monitoring data usage.

Platform: A single, unified view of data and analytics

The platform level refers to bringing together the data estate to centralized data management with a standardized format of Common Data Model. Common Data Model is a data standard that provides a shared data language and structure for business data. Organizations can use Common Data Model to improve data consistency and accuracy, reduce data duplication, and increase data interoperability across different systems and services.

Common Data Model is a flexible and extensible data model that a user can customize to meet the specific needs of individual organizations. It supports both cloud and on-premises deployments and can be used with various platforms, including Dynamics 365, Power Apps, Power Automate, and Power BI, to create connected journeys across data, business processes, and collaboration use cases for both Microsoft and ecosystem partner products.

Next steps