Normalizing data

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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.

Overview

In IBM® Envizi ESG Suite, each account is associated with an account style that in turn is associated with a data type. Some of the fields in an account style are linked to specific measures that have been defined for the data type. A measure is a numeric field that can be normalized and aggregated. Most data types have a primary measure, and one secondary measure to track cost. Account styles usually include the main measure fields and, potentially, other secondary fields. Factors, such as emission factors, are applied only to the primary measure. Secondary fields in account styles that are not associated with a measure are not normalized.

Account records that are added or ingested can span multiple months. The system then normalizes numeric measures into monthly blocks. Field values are adjusted proportionally based on the number of days in each month. The process by which data is normalized depends on whether the measures are accumulative or instantaneous:
Accumulative Measures
Represents data that accumulates over time. An example is energy consumption.
Instantaneous Measures
Represents a point-in-time value that does not sum. Instead, it remains constant unless it is updated. An example is the floor space of a building.

Example: Accumulative data

The following is an example of the normalization process for an accumulative measure:

A site manager has an electricity invoice that includes usage, maximum demand, and cost that covers a period from 20 December 2024 to 14 February 2025. The invoice includes the following data points:
  • Kilowatt hours: 10,000 kWh
  • Cost: $300 USD
The system uses the following steps to create monthly values:
  1. Determines the total number of days in the invoice: The invoice covers 57 days, which are broken down as:
    • 12 days in December
    • 31 days in January
    • 14 days in February
  2. Determines the pro-rata quantity for each measure for each month. For example, the monthly value for December is calculated as 12/57 * 10,000 = 2,105.2632.
  3. Displays each measure as a separate row for each month in the Monthly Data grid.
The Monthly Data grid displays the data in Monthly data grid rows for accumulative data.
Table 1. Monthly Data grid rows for accumulative data
Month Amount Unit Days of data Measure Measure type
February 2025 2,456 kWh 14 Kilowatt hours (KWh) Primary
January 2025 5,439 kWh 31 Kilowatt hours (KWh) Primary
December 2024 2,105 kWh 12 Kilowatt hours (KWh) Primary
February 2025 74 USD 14 Total cost Secondary
January 2025 163 USD 31 Total cost Secondary
December 2024 63 USD 12 Total cost Secondary

Example - Instantaneous data

The following is an example of the normalization process for an instantaneous measure:

A site manager identifies from the building plans that the floor space of the building is 24,000 sqft. A record is added for floor space for the site from 20 December 2024 to 14 February 2025. The record is normalized into monthly values in the Monthly Data grid. A single row is displayed for each month in the Monthly Data grid in Table 2.
Table 2. Monthly Data grid rows for instantaneous data
Month Amount Unit Days of data Measure Measure type
February 2025 24,000 sqft 14 Quantity Primary
January 2025 24,000 sqft 31 Quantity Primary
December 2024 24,000 sqft 12 Quantity Primary
Note:
  • Account style fields that are not associated with a measure are not normalized. The fields are available for reporting in raw data reports such as the Raw Data Extract Report, the Account Setup and Data Load Report, and the Records by Tag Type Report.
  • Social metrics are represented by a special type of account style and are not monthly normalized. For more information about how social metrics are processed, see Social metrics account styles.