Data export
The purpose of data export in Microsoft Sustainability Manager is to enable the extraction and transfer of sustainability-related data to external systems or tools. It allows organizations to utilize the collected data for purposes beyond the Sustainability Manager. Some key purposes of data export in sustainability solutions include:
Collaboration and stakeholder engagement: Organizations can share exported sustainability data with internal and external stakeholders that include investors, customers, employees, and partners. This shared data provides stakeholders with access to relevant sustainability information and helps foster engagement, build trust, and promote transparency.
Reporting and compliance: Data export enables organizations to generate customized sustainability reports, comply with regulatory requirements, and provide transparent disclosure to stakeholders. Organizations can use exported data for external reporting frameworks such as Global Reporting Initiative (GRI), Carbon Disclosure Project (CDP), or Corporate Sustainability Reporting Directive (CSRD).
Data analysis and insights: Organizations can analyze exported data using advanced analytics tools and methodologies. By exporting calculated sustainability data, organizations can gain valuable insights into their environmental and social performance, identify trends, patterns, and opportunities for improvement, and make data-driven decisions.
Integration with other systems: Data export facilitates integration with other enterprise systems or third-party applications. You can combine sustainability data with financial, operational, or supply chain data to gain a holistic view of an organization's performance and identify interdependencies.
Long-term data preservation: Data export allows organizations to create data backups or archives for long-term preservation. Data backups ensure data continuity and accessibility for future references, audits, or historical analysis.
Challenges
Challenges of data export in sustainability solutions include:
- Ensuring data quality and consistency.
- Addressing data privacy and security concerns.
- Achieving standardization and interoperability.
- Managing large data volumes and processing time.
- Maintaining data governance and compliance.
- Ensuring proper data interpretation and contextualization.
Methods
There are six recommended methods to export data from Microsoft Sustainability Manager. The following table outlines these methods along with their respective design considerations.
| Method | Description | Limitations | Use when |
|---|---|---|---|
| Azure Synapse Link for Dataverse | Dataverse supports exporting data to Azure Synapse Workspace using Azure Data Lake Storage Gen2, enabling data storage and analysis at a larger scale. | A limitation to consider with this option is that tables that don't have change tracking enabled aren't supported for export. Example tables: Attachments and Calendars. |
Useful for data analytics and custom reporting beyond Sustainability Manager reports and dashboards. |
| Azure Data Factory or Synapse pipelines | Building downstream data pipelines from Sustainability manager to other systems. | Azure Data Factory has specific limits, and the users receive an error message when these limits are exceeded. Specific limits include the maximum number of pipelines, datasets, activities, triggered pipelines, and concurrent runs. For more information, go to Data factory service limits. |
Useful for building export pipelines of calculated data from Sustainability Manager to other systems and destinations. |
| Manual | Manual data export of selected entity to CSV, XML, or Excel files. | Specifically for Excel export, you can export only 10,000 records exported at a time. For more information, go to Export limits. |
Useful when data needs to be shared with individuals who aren't users of Sustainability Manager and for offline manipulation of Excel and CSV data. |
| Export to Power BI | Connect Dataverse data to Power BI for creating interactive visualizations and reports. | Requires Power BI Pro or Premium license. Microsoft Dataverse connector can't read elastic tables. Common data service (Legacy) connector is on the deprecation path, and it isn't recommended. |
When you need to build rich visualizations and in-depth analytical reports using Dataverse data. |
| API access | Programmatically access data in Dataverse using APIs (Example: OData) and export data in a customized format. | Microsoft Power Platform has limits on the number of API requests that you can make in a 24-hour period. These limits protect the platform from excessive load. For more information, go to Service protection API limits (Microsoft Dataverse) |
When you need to automate data retrieval and integration with external systems or build custom applications that interact with Dataverse data programmatically. |
| Export using Power Automate | Utilize Power Automate to automate data export processes to various destinations. | There are specific limits assigned based on the license and the scenario is transactional specific and isn't intended for bulk export. For more information, go to Limits and configuration - Power Automate |
You need to automate repetitive data export tasks without extensive coding efforts. Examples of repetitive data export tasks include sending data via email, saving specific data to SharePoint based on a scenario, or saving data to OneDrive. |