Map Your Operational Data to GreenSphere Indicators

Most stuck points in onboarding come from a single question: where does my data go? This guide is the mental model. It's not a workflow — there's no "click here" sequence. It's the map between what your business has (utility bills, fuel receipts, payroll, waste invoices) and what GreenSphere expects.

The two-layer data model

GreenSphere has two data layers that work together:

• Raw data — operational activity values: kWh of electricity, litres of fuel, tonnes of waste, m³ of water, headcount, business travel kilometres. This is what your bills, receipts, meters, and HR systems produce.
• Indicators — KPIs and disclosure metrics: total Scope 1 emissions, energy intensity per square metre, water withdrawal in stressed areas, ratio of capital spend to revenue. This is what gets tracked on your dashboard and exported in reports.

Raw data feeds indicators. Indicators are what gets reported.

GreenSphere ships with 600+ pre-set indicators and 200+ pre-set raw data types, covering the standard operational categories most SMEs need. You can use these as-is, or extend with custom raw data types and custom indicators (see below).

How indicators relate to raw data

Each indicator on your dashboard or in a report is one of three shapes:

• One raw data point — the indicator equals a single raw data type (e.g., total electricity consumption from grid electricity).
• Many raw data points combined in a formula — the indicator computes from multiple inputs (e.g., emissions intensity = total emissions ÷ revenue; ratio of capital spend to revenue = capital goods spend ÷ revenue).
• No raw data points — value entered directly — the indicator is logged manually as a Calculated Metric (e.g., supplier-provided figures, externally audited values, or KPIs computed outside the platform). See Log your first data point for how.

Every indicator in the platform also links directly to a report field. When you log raw data that feeds an indicator, that indicator updates automatically; when you generate a GRI or IFRS report, the indicator value populates the linked report field.

See what feeds what — the drill-down

Both the Raw Data and KPIs pages have a drill-down view that makes the data lineage visible. Click into any single raw data type or indicator and the right-hand panel shows the connections.

On the KPIs page, click into any indicator (e.g., Ratio of Capital Spend to Revenue). You'll see:

• Trend over time — value by period.
• Peak Value, Average, Entries summary tiles.
• Data Lineage (right-hand panel) — the list of raw data types feeding this indicator. For Ratio of Capital Spend to Revenue, the lineage shows Capital Goods – Spend and Revenue Metric.
• Audit Trail — recent activity on this indicator.
• Indicator Entries — every period's value, with source labelled Calculated (computed from raw data) or Manual (entered directly).

On the Raw Data page, click into any raw data type. The mirror view shows which indicators consume it — so you can trace upward from operational data to the KPIs and report fields it powers.

Common operational inputs and where they go

The 80/20 set — what most SMEs actually have, and where each piece of data maps in the platform:

This is not exhaustive — the platform has 200+ pre-set raw data types — but it covers the inputs most SMEs work with first.

What if my data doesn't fit cleanly?

Real operational data is often messy. The platform is built around three honest cases:

• Aggregated bills. A single electricity bill that covers two sites with one meter — log against the parent organisation, or split proportionally and log against each site separately. Use audit notes to record the allocation method.
• Multi-site shared meters. Same logic: pick an allocation method (floor area, occupancy, headcount), document it in audit notes, and apply it consistently.
• Missing data for a period. Skip the period rather than estimating with no basis. If you must estimate, document the method in audit notes — assurance providers expect to see this.

Three SME profiles and their typical inputs

Hospitality SME (e.g., hotel group). Electricity (kWh) for each property, gas for hot water and kitchens, water (m³), waste invoices by stream, refrigerant top-ups, food and beverage purchasing spend, occupied room-nights, headcount by site.

Manufacturing SME. Grid electricity, stationary combustion (boilers, furnaces), mobile combustion (fleet), process emissions where applicable, water withdrawal and discharge, waste by hazardous and non-hazardous, raw materials purchased (Scope 3 Cat 1), production output, headcount.

Professional services / software firm. Grid electricity for the office, business travel (flights, hotel nights, taxis), employee commuting, IT equipment purchases (Scope 3 Cat 2), procurement spend on services, revenue, headcount.

Across all three: revenue, headcount, and floor area are common intensity denominators that show up in many indicators.

Adding custom raw data types

If a raw data type you need isn't in the pre-set 200+, create a custom one.

Open the Raw Data page and click Create Data Type. The Create Data Type page has three steps:

1. Data Type Identity. Name the data type (e.g., "Refrigerant — R410A"), add an optional description.
2. Unit & Classification. Pick a standard unit from the list, or click + Custom Unit to define one specific to your reporting needs. Optional category tags (Environmental, Energy, Financial, Social, Water, Waste, Other) help with filtering on the Raw Data dashboard. Set a default reporting frequency if relevant.
3. (Optional) Add First Entry. Skip or add a first value now.

The Live Preview panel on the right shows what the data type will look like before you save. Click Create Data Type to commit.

Wiring a custom raw data type into an indicator

Creating a raw data type doesn't automatically connect it to any indicator. Two ways to wire it in:

Option A — Create a new custom indicator. Open the KPIs page and click + Create KPI. The Create Indicator page lets you:

• Set indicator identity (name, description, optional category and reporting framework).
• Choose Data Entry Method — Formula (Calculated) or Manual Entry.
• For Formula, click any raw data type in the Available Raw Data Types panel on the right to insert it into your formula. Standard arithmetic operators (+, −, ×, ÷, parentheses, ^) are available above the formula box. Units are inferred automatically; you can override them.
• The Live Preview shows the formula and inferred unit before save.

Click Create Indicator to commit. The new indicator now consumes your custom raw data type.

Option B — Duplicate and edit an existing indicator. From the KPIs page, find an indicator close to what you want, open it, duplicate it, then edit the formula to include your new raw data type. This is faster than building from scratch when an existing indicator has the right shape but needs one input swapped.

After either option, log a value against your custom raw data type (see Log your first data point) and the indicator calculation runs immediately.

What this guide deliberately doesn't cover

• Scope 3 methodology depth — see the reporting guide Calculating Scope 3 emissions: the 15 categories explained.
• Materiality assessment for choosing which indicators to track — see the reporting guide Building a materiality assessment from scratch.
• The actual calculation chain (factor lookup, scope assignment, aggregation) — see the platform guide How emissions calculations work.

What's next

You've got the model. The next step is the workflow that uses it most: generating your first report, where the indicators you've populated flow into the disclosure fields.

Open Generate your first GRI report or Generate your first IFRS S1/S2 report depending on which framework your workspace was configured with.