Missing data is common in product footprinting. Teams may not know every transport route, production process, energy mix or end-of-life scenario at the start. Without a structured approach, these gaps can block calculations or lead to inconsistent assumptions.

Pickler helps by separating required inputs, important primary data and additional fields. Conservative defaults and secondary data can be used where appropriate, while better primary data can be added later to improve representativeness.

A product can start with material and weight data while missing transport details are handled with defaults. Later, the team can replace defaults with more accurate supplier or logistics data.

Pickler makes missing data visible and manageable so teams can calculate now, improve later and avoid hidden spreadsheet assumptions.