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Prepare

Source integrity & auditability

Non-destructive transformations and traceable methods keep the underlying research defensible.

Follow a finding back to its evidence.

Non-destructive transformations and traceable methods keep the underlying research defensible. Every merge, relabel, and derived variable remains inspectable rather than disappearing into a working copy.

The method stays close to the result.

Source imports are preserved, calculated columns show their formulas, statistical tests name the method used, and AI-coded themes link back to verbatims. Analysts and reviewers can trace a finding to the evidence behind it—not a black-box output.

Research you can stand behind.

Inspectable evidence helps agencies defend work to clients and in-house teams satisfy internal review. It follows the project from preparation through analysis and into the findings shared in Insight Hubs.

Make review part of the workflow, not a late-stage exercise.

When a result is challenged, the team should not have to reconstruct the path from a slide backwards. AddMaple keeps the working evidence, the changes made to it, and the methods used to analyse it in the same project. That gives another analyst a place to begin and gives the person delivering the research a more credible answer when someone asks, “How did we get here?”

Questions teams ask

What does source integrity mean in AddMaple?

Source integrity means the imported source remains available, transformations stay inspectable, formulas are visible, statistical methods are named, and coded themes can be traced to verbatims.

Can reviewers see how a finding was produced?

Analysts and reviewers can inspect the source, calculated-column formulas, transformations, statistical outputs, and evidence behind AI-coded themes.

Does source integrity only apply during data preparation?

No. It is intended to support review from preparation through analysis and into the findings shared in Insight Hubs.

How it works

Step-by-step guides in the AddMaple help center.

  • Prepare messy survey data

    Make transformations inspectable while preserving the source structure needed to review and reproduce analysis.

Part of AddMaple's feature areas

This capability sits within broader product areas on the features hub.

  • Data preparation & live research data

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