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Data collection in Scaler: Methods & data types

Data collection in Scaler: Methods & data types

An overview of the four ways data enters Scaler and the types of data collected at company, portfolio, asset, and meter level.

Last updated on December 23, 2025

Purpose of this article

This article explains how data gets into Scaler and what types of data Scaler collects, providing a clear, high-level map before diving into detailed how-to guides.

How data gets into Scaler

Scaler supports four complementary data entry methods. Most organisations use a combination of these depending on data type, scale, and workflow.

  1. Manual entry directly in the platform
  1. Bulk upload via the Scaler spreadsheet template
  1. Data Requests to external stakeholders (e.g. property managers, tenants)
  1. Automated data input via integrations (APIs and bill scraping)

Broadly:

  • Methods 1–3 are manual input routes (you or your stakeholders enter or upload data).
  • Method 4 is automated, but always requires a one-off manual setup, especially for meters and assets.

Overview: manual vs automated routes

Method
Type of input
Typical use case
Manual entry in Scaler
Manual
Small portfolios, quick corrections, one-off updates
Bulk upload via Scaler spreadsheet
Manual (bulk)
Larger portfolios, initial onboarding, big changes
Data Requests
Manual (by others)
Collecting data (annually) from property managers, tenants, operators
Automated integrations (APIs / bills)
Automated (after setup)
Ongoing meter/consumption data and some utility details

All of these methods feed into the same underlying data model in Scaler, so you can mix and match them across assets, meters and other data types.

1. Manual data entry directly in the Scaler platform

You can always add or edit data directly in the Scaler interface:

  • Create and edit assets, spaces and meters
  • Enter consumption, cost and other ESG data record by record
  • Fix small issues or fill in one-off gaps

When this is useful

  • You only need to add a few new items (e.g. one new meter, one missing year of data).
  • You’re correcting data or testing how something looks in the platform.
  • You want to quickly prototype or review changes before scaling them up.

This is a fully manual method: you type or paste data directly into Scaler fields.

2. Bulk upload via the Scaler spreadsheet template

For larger changes, you can use the Scaler Spreadsheet template to upload data in bulk. This lets you:

  • Add or update many assets, meters or records at once
  • Prepare data offline in Excel

Typical steps:

  1. Download the relevant Scaler Spreadsheet template with the relevant fields.
  1. Populate it with your data (assets, meters, consumption, etc.).
  1. Upload the completed file back into Scaler.
  1. Review and confirm any validation messages.

This is still a manual method (you or your team manage the spreadsheet), but it is more efficient than record-by-record entry for larger volumes.

3. Data requests to stakeholders

If key data sits with external stakeholders (e.g. property managers, tenants, facility managers), you can use Data Requests:

  • Scaler generates structured portal for the data you need.
  • You give customized access, by asset and field, to your stakeholders, who fill in the required fields.
  • Once submitted, the responses are mapped back into your portfolio after being reviewed and accepted.

Key points

  • The collection is manual for the recipient (they type in or upload data).
  • Scaler helps structure and validate what they provide.
  • Allows users with certain access rights to validate data before it is added to your database and communicate with recipients for any checks or changes.
  • This is ideal when you don’t own the source data but need it in a consistent format.

Data Requests are therefore a manual route, but they distribute the work and help ensure that what comes back is usable.

4. Automated data input (APIs and bill scraping)

For ongoing operational data, especially energy, water and waste consumption, Scaler supports automated data input. There are two main mechanisms:

  1. Third-party API integrations
      • Scaler connects to utilities, energy management platforms or other data providers.
      • Once connected and configured, consumption and related data can flow into Scaler automatically on a regular basis.
      • e.g. ENERGY STAR Portfolio Manager
  1. Bill scraping and automated data extraction
      • Scaler uses tools to extract data directly from utility bills.
      • The relevant fields (consumption, cost, dates, etc.) are captured and imported into your portfolio.

These routes are automated for the ongoing data flow, but they do require a manual setup phase.

Important: manual setup for automated data

Even when using integrations, there is always a manual step at the beginning, especially around meters:

  • Meters must exist in Scaler before data can be mapped to them.

      • You (or your team) will:

    • Create meters directly in the platform or
    • Create meters via bulk upload using the Scaler spreadsheet.
  • During integration setup, each external data source (e.g. utility account, meter ID) must be mapped to the correct meter in Scaler.

Once this is done:

  • Consumption and related data are synced automatically according to the integration’s schedule.

In short:

  • Manual routes (portal, spreadsheet, data requests) are used for initial setup, one-off changes and data you can’t automate.
  • Automated routes (APIs, bill scraping) handle the ongoing flow of data once everything is configured.

How these methods work together

You don’t have to choose just one method. In practice, portfolios often use a combination:

  • Initial onboarding
    • Use bulk upload to create assets and meters.
    • Use data requests to collect missing information from property managers.
  • Ongoing operations
    • Set up API integrations or bill scraping for consumption data where possible.
    • Use manual entry for small corrections, exceptions or one-off updates.
  • Filling gaps
    • When an integration does not cover a site or period, use the spreadsheet or data requests to backfill, by setting up a manual meter for that backfill.

What data Scaler collects

Scaler primarily collects data at the asset and portfolio levels, and then aggregates and calculates results at the company level for analysis and reporting.

Company-level data (limited)

Scaler does not broadly collect raw company-level data. Instead, it aggregates asset and portfolio data to the company level.

The main exception is the Company Performance Dashboard, where companies can record their full Scope 1, 2, and 3 emissions totals.

Navigation path:

Data Collection Portal → Company → Performance dashboard

In this view:

  • Scaler automatically aggregates and calculates real estate operational emissions (Scopes 1 & 2, and limited Scope 3)
  • Clients can manually record all 15 Scope 3 categories, including emissions outside of real estate or outside Scaler coverage

For each scope and category, companies can:

  • Select a baseline year
  • Enter historical (retrospective) years
  • Track change over time
  • Define targets:
    • Short-term (within 5 years)
    • Mid-term (2031–2040)
    • Long-term (2041–2045)
  • Attach supporting documentation and contextual notes

This allows Scaler to function both as:

  • A calculation engine for real-estate emissions, and
  • A system of record for full corporate GHG reporting, even when Scaler does not cover 100% of assets or scopes

Portfolio-level data

Portfolio-level data defines shared configuration and reporting context, including:

  • Portfolio settings (fiscal year, units)
  • Emission factors
  • Targets (single year and pathways) & benchmarks
  • Governance indicators

This data determines how asset-level data is interpreted and reported.

Asset-level data

Asset-level data describes the physical, operational, and contextual characteristics of each asset, including:

  • Asset details and characteristics
  • Floor areas
  • Reporting characteristics
  • Sustainability characteristics
  • Certifications and ratings
  • Risk assessments & building efficiency measures
  • Retrofit/improvement planning
  • Physical climate risk
  • Social data
  • Financial data
  • Development data
  • Custom fields (where configured)

This data provides the core structure and inputs for analytics, emissions calculations, and reporting.

Meters & consumption data

Meters define how performance is measured, while consumption records provide the underlying data.

This includes:

  • Energy, water, and waste meters
  • Physical and calculated meters
  • Installations, which capture refrigerant (F-gas) leakage and translate it into GHG emissions
  • Consumption values and costs
  • Supporting evidence and documentation

This is the primary input for performance analytics and emissions calculations.

How data types and entry methods work together

In practice:

  • Asset and portfolio data is often entered manually
  • Consumption data may come from spreadsheets or automations
  • Missing or updated data may be collected via data requests

Scaler is designed to support flexible, mixed workflows while maintaining structure, validation, and auditability.

Where to go next

  • Asset setup basics
  • Meters & consumption overview
  • Using the Scaler spreadsheet
  • How the Data Request tool works
  • Automations & integrations overview
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