Data Management Factsheet

The existence of data silos are a key reason for the slow pace of emission measurement in large enterprises

Data is often stored and managed in isolated systems. This leads to information being inaccessible and difficult to amalgamate effectively. In the context of carbon accounting, these silos can manifest as segregated databases, disconnected software applications — ERPs, CRMs, HCM apps, each with distinct teams managing disparate data points. This fragmentation not only complicates the process of collecting and validating data, but also obstructs the creation of a comprehensive and accurate mapping of a company’s footprint.

Data quality and consistency

Integrating data from various sources often exposes inconsistencies, errors, and gaps in data quality. Differing units of measurement, data formats, and accuracy levels can lead to inaccurate emissions calculations.

Data accessibility

Some departments or data sources might not readily share their data due to privacy concerns, proprietary systems, or a lack of standardised data sharing protocols.

Technical compatibility

Integrating data from different systems, databases, and software applications can be technically complex. Ensuring that these systems are compatible and can communicate effectively is not a given.

Data volume and complexity

Carbon accounting requires large volumes of data from a wide range of sources. Managing and processing this data efficiently can be daunting, especially with Scope 3 as the complexity of supply chains increases.

Taking a value chain view

To identify all relevant data sources, often dispersed across various systems and processes within the organisation, so that hotspots don't stay hidden in isolated datasets.

Data governance

To enable efficiency and the appropriate controls for the secure sharing of data for carbon accounting and external reporting.

Appropriate technologies

To build the data infrastructure needed for systems to communicate effectively and for data to be pulled together.

What specific types of data are required for comprehensive carbon accounting?

One of the key steps for any organisation in calculating carbon emissions is conducting a value chain mapping exercise, to identify all relevant data requirements, which could extend beyond a organisation boundaries.

This data typically includes information on energy consumption, transportation and logistics, process, and production activities, purchased goods and services, water usage and wastewater management, waste and recycling, land use and land-use change, employee and customer activities, carbon credits, and leased assets. Given that this data is often dispersed across various systems and processes within the organisation, collecting it in a timely manner can be a challenge.

How does Terrascope facilitate the efficient collection, ingestion, and transformation of this data?

The Terrascope platform offers robust capabilities to ingest and process activity data, whether it’s complex Excel sheets, PDFs, templates, or via direct connections to systems of record like SAP, Salesforce, and others. This flexibility ensures that organisations can more efficiently gather, manage, and analyse the diverse data needed for carbon accounting, on a annual, quarterly or monthly basis.

How does Terrascope facilitate the integration of different systems?

Terrascope is equipped with a cloud-based integration platform designed to streamline the design, deployment, and management of your integration processes for carbon accounting.

Key features include:

Cloud-Based Platform: A platform that operates entirely in the cloud.

Platform Agility: The ability to integrate applications, whether they reside in the cloud or on-premises, ensuring flexibility for both current and future integration needs.

User-Friendly: Designed with non-integration specialists in mind, the platform reduces development effort and accelerates time-to-value.

Flexible Deployment: Options that address data security and privacy concerns while offering adaptable deployment solutions.

Cost Efficiency: Lower operational support costs by minimising the need for servers and other infrastructure.

Extensive Connectivity: The platform connects to hundreds of enterprise endpoints through a wide range of connectors, including applications like SAP, Workday, Anaplan, databases like MySQL, Oracle, Spanner, and cloud storage solutions like BigQuery, Snowflake, and HDFS.

Does Terrascope provide APIs for integration?

Yes, Terrascope offers APIs that allow customers to directly send their activity data to the platform. Additionally, APIs are available to extract calculated data from the Terrascope platform, facilitating seamless integration with third-party tools. This ensures smooth data flow and enhances the overall flexibility and interoperability of the platform within your existing systems.

How frequently will data be collected, and how does Terrascope ensure it remains up-to-date?

Terrascope supports data collection at various frequencies based on data availability. For example, if data is sourced from a system of record (SOR), it can be processed on a daily, weekly, or monthly basis. Additionally, if certain data is only available once or twice a year, it can still be accommodated within the platform. Once the data is collected, Terrascope’s data pipelines automatically process and transform the source data, calculating carbon emissions and making reports readily available after ingestion. This ensures that your carbon data is always up-to-date and accessible.

How does Terrascope handle data in different languages?

By default, the Terrascope platform supports data in English and Japanese. However, it is designed to be scalable and can accommodate any language supported by the Unicode character encoding standard. This ensures that the platform can process and display text in a wide range of languages and scripts, providing flexibility for global and multilingual data management.

How does Terrascope handle complex, unstructured PDFs and Excel files?

The Terrascope platform leverages AI and automation to enable unstructured data extraction to process complex PDFs and Excel files. This reduces the need for manual data processing by efficiently extracting and structuring data from diverse and intricate formats.