YuzeData connects sustainability data to drive adoption and impact

GHG reporting and emissions calculations is challenging at scale, especially if collecting the raw data remains a manual or spreadsheet task.

However, with data schemas that model how each scope’s data should be captured and prepared for emissions calculations, and bespoke connectors for a range of data sources, YuzeData makes it easy to connect to the source systems that hold raw and transactional data, and automate passing that to the right emissions calculation tools. 

Data collection can be automated from a variety of sources such as Utility Data Management, ERPs, Shipment, Supplier, Product Carbon Footprinting and Expense Management Systems.

Collected data can also be cleaned and transformed e.g. with configurable mappings or aggregration calculations, to ensure it fits into the destination platform. Examples of destination systems include ESG, Sustainability, Carbon Accounting, Lifecycle Assessment and Corporate Performance Management tools. 

The result is an automated pipeline running regularly to take the manual effort out of quarterly, monthly or weekly data collection across Scopes 1, 2 and 3. 

With attention turning more to global supply chain due diligence and risk mitigation, companies demand up to date information on suppliers at all tiers and on the materials, components and services they supply. 

Modern supply chain management platforms are meeting these needs at scale with expert monitoring and analytics. However, these rely on onboarding and frequently updating large amounts of accurate ERP data into the platform, and synchronizing key learnings out of the platform to other corporate systems. 

With data schemas and data transformation built for BOM (Bill of Materials) and multi-level parts and supplier data, YuzeData automates this synchronization and update process meaning customers can get to supply chain insights faster. 

Data can be collected from ERPs, PLMs or combined from multiple sources of transactional data. Collected data can be cleaned and transformed to meet the specific data model of the chosen supply chain vendor e.g. converting bulk purchasing data into individual unit data. 

This means less overhead on internal IT teams wrangling data pipelines with ERPs and redoing data mappings should new parts or suppliers be introduced to the business.