The capture, management, and reporting of environmental, social and governance (ESG) data is a topic of global relevance. IBM Envizi is making this process more accessible with this latest release of Envizi in seven languages. Customers can now view the IBM Envizi ESG Suite interface in English, French, German, Italian, Spanish, Brazilian-Portuguese, and Japanese. 

Translated functionality includes key areas of the IBM Envizi user interface including home pages, summary pages, popular dashboards and PowerReports, menus, buttons, browse grids, search and navigation, forms, dialog boxes, pop-ups, preview panels, and key emails. This functionality will be particularly valuable to organizations operating across multiple geographies where different stakeholders can now view the interface in their own language.

Sustainability has a global impact, and the need to capture, manage, and report on the ESG metrics and data associated with these issues is a global challenge. After all, 41% of executives in a recent study cite inadequate data as a key roadblock in their progress towards delivering sustainable outcomes. Providing the IBM Envizi ESG Suite in seven languages is another step towards smoothing the way for organizations to manage and measure their sustainability impact.

Imagine you are an organization with operations in multiple countries: the capability to personalize the IBM Envizi interface into the local language opens opportunities to make it easier for teams in-country to navigate the platform in their native language. This makes the data entry directly into Envizi simpler, and the single source of truth for your ESG data.

This latest release is just one of the ways IBM Envizi is working to ease the process of ESG data management and reporting. It follows IBM’s recent announcement that it can help organizations accelerate the capture, calculation, and analysis of Scope 3 greenhouse gas (GHG) emissions through the integration of natural language processing (NLP) into IBM Envizi. The NLP capabilities, including text classification, can help categorize spend data drawn from financial or ERP systems.