Data management overview
Accurate and healthy sustainability data enables organizations to understand the impact of their ESG initiatives, make informed decisions, engage stakeholders, manage risks, and contribute to a more sustainable future. It aligns environmental and social considerations with business strategies, fostering responsible and resilient practices that benefit both the organization and society at large.
Microsoft Sustainability Manager solution relies importantly on data and its quality. Bad data in terms of accuracy, context, and completeness invariably produce bad insights that hurt an organization’s sustainability goal. Organizations need to understand their data landscape and design an effective strategy with the inbuilt Data tools in Microsoft Sustainability Manager.
Sustainability Manager implementation journey
As organizations look to meet their sustainability goals by configuring and deploying Microsoft Sustainability Manager, it's important to comprehend the data-centric journey. The following illustration describes the typical journey for calculating carbon emissions, water, and waste management.
During the journey of enabling the carbon emission accounting and water and waste management, organizations typically start with the following tasks:
- Review the data landscape
- Collect their data before transforming
- Import data
- Perform calculations and analysis.
Some of the common patterns of the implementation of Microsoft Sustainability Manager are as follows:
The data journey truly begins with setting up organizational and reference data. This crucial step sets up the organization to report, track, and collaborate with stakeholders.
Organizations can accomplish data transformation and import using built-in capabilities such as the Excel templates and Power Query guided experience. In addition, organizations can choose to bring data from other solutions using the catalog of available Partner solution connectors. In cases where complex data transformations are needed, organizations can employ Extract, Transform, and Load (ETL) patterns while accommodating network and security considerations.
After the data import, organizations can perform calculations over carbon, water, or waste activities. The calculated data is presented as insights as Microsoft Sustainability Manager aggregates and presents key indicators. Organizations then take action, monitor progress, export data, and collaborate.
In addition to using the built-in data model and calculation capabilities, organizations could opt to extend the solution to meet their requirements. These requirements could include adding new fields, tables, creating or customizing calculation models, or adding flows.
Cyclical and incremental journey
The implementation journey described in the previous section can repeat for specific categories and operations. We recommend that organizations start small, learn from the process, and then iterate. For example, organizations can start with viable areas such as Scope 1 and Scope 2 emissions before expanding to other carbon and sustainability areas. In addition, starting from the simplest import experiences helps organizations understand the data model, data format, and requirements across all supported data categories.
Types of data
In Microsoft Sustainability Manager, you can categorize data into three broad groups – Configuration, Activity, and Analytical Data. To achieve efficient results during the implementation, it's crucial to understand these groups of data and adhere to the data import sequence to ensure error-free imports, avoiding any issues related to dependencies.
Configuration data: The fundamental configuration organizations must perform before importing the activity data. This configuration includes setting up the organization data manually, setting up reference data to be used across the solution, and calculation libraries for organizations to define their set of calculations. Properly defining these configurations is essential as it ensures smooth execution of the rest of the Sustainability Manager implementation without issues. For example, the organizational units and reporting years configuration enable organizations to establish the operational boundaries for reporting purposes.
Transactional data: The operations and activities data from Carbon emissions, water, and waste management, consolidated within the operational boundaries of organization units and reporting years. As a best practice before importing this data into Sustainability Manager, validate that all the reference data used in activity data is already configured. Doing so eliminates most of the issues that could result in data import errors.
Analytical data: This data feeds all insights and scorecards. The analytical data is stored in a managed data lake and isn't accessible to end users.
Data management considerations
Some of the most important architecture and design decisions during the implementation journey are related to the data management aspects of Microsoft Sustainability Manager.
The following table outlines some of the most important data management topics that organizations need to carefully consider during the implementation of Microsoft Sustainability Manager.
| Data management topic | Considerations |
|---|---|
| Data import | Which data should be ingested first and what is the sequence to import subsequent data types? What should be the considerations to handle data transformation and ingestion of different formats and different volumes of data? What should be the design for continuous refresh of data? Hot to ensure the uniqueness of imported records? How to manage the velocity of the data for scenarios such as IoT? How to securely access remote data sources that are hosted in the cloud or on-premises locations? |
| Data export | What options do organizations have to export their calculated data to other systems for downstream processing? |
| Data security | What considerations should be in place to prevent data from unauthorized access and modification? |
| Data auditing and traceability | What considerations should be in place to ensure accuracy, reliability, and compliance in the measurement, reporting, and management of sustainability data? |
| Custom dimensions | What should be the considerations to associate activity data with operational contextual information such as production line and production type used for manufacturing of goods in facility? |
In the following sections, we explore each of these data management topics with their respective benefits, design considerations, and solution capabilities to configure Microsoft Sustainability Manager with best practices and improved time to value.
Summary
In summary, the following are the core considerations for organizations that want to embark on their sustainability journey using Sustainability Manager:
Organizations should define the data landscape to create an inventory of data sources and systems that are involved.
Organizations should consider using an iterative process to transform and import data, perform calculations, and analyze the reports. The plan should be to start with simpler datasets and data categories and progressively expand the scope and complexity.
Organizations should ensure the configuration is in place before they import the transactional data. This step eliminates most issues that could result from data import errors.