Managing

In IBM® Envizi ESG Suite, you can capture data and verify that the data has no gaps or missing data.

Capturing account data
In IBM Envizi ESG Suite, you create accounts to capture utility type data, for example, electricity and natural gas; waste, steam, water, stationary and transport fuels; KPIs, and supplier spend data for your locations.
Adding account data in bulk
In IBM Envizi ESG Suite, you can upload activity data, KPIs, and spend data in bulk by using report templates.
Approving account data
In IBM Envizi ESG Suite, you can use account data approvals to improve the quality of the ESG data that is used for downstream reporting, analysis, and decision making.
Verifying data quality
You can assess the quality of your data to find missing data, overlapping data, and data gaps for your accounts. Several reports and dashboards are available to help you find inaccuracies.
Monthly Data Summary report
The Monthly Data Summary report displays aggregated monthly consumption, energy, emissions, and cost per month, quarter, and year. Data is aggregated irregardless of invoice periods.
Account Style Field Extract
Provides a detailed reference of all account styles available to a client, including their data types, data fields, field mandatory status, help text, and configuration settings.
Managing issues
Use the Issue Tracking component in IBM Envizi ESG Suite to track and manage data capture, data quality, and energy management issues. Issue tracking is a key component of Interval Metering Analytics. You can also use issue tracking to request manual data capture of account records.
Tagging data
Tagging allows you to group data for specific reporting needs and apply varying reporting percentages and effective dates to further control how and when data gets reported.
Adding notes and attachments
It is best practice to capture notes and attachments with your data.
Normalizing data
Activity data is normalized into monthly data so that the data can be analyzed and factors can be easily applied. Review an overview of how data is normalized.