From documents to data

How can you use AI to turn disparate documents into structured, sourced and verifiable data?

1960 € per session, excl. VAT

In short

“Extracting data from heterogeneous documents” is a private in-person session of a full day for documentation and research officers building databases from varied documents or procurement staff, administrators and analysts responsible for reconciling files. Groups of up to 12 participants, in French, with exercises on your own work cases.

Why this training?

An amount separated from its currency, or a quantity confused with a packaging unit, can distort a procurement reconciliation. The variety of layouts makes copying and pasting unreliable and conceals contradictions between versions. AI makes fields easier to locate, but may fill in missing data or misread a table. Usable extraction therefore requires an explicit schema, source references and error measurement that is independent of the generated response. Running case study: The fictitious organisation Avelune Équipements must reconcile twelve supplier files containing PDF quotations, scanned delivery notes, pricing tables and corrective emails. The procurement department expects a table of references, quantities, prices, currencies and due dates, with explanations for discrepancies. Workplace application: Reuse the data dictionary on a small, authorised batch of business documents, establish a manually verified reference and compare errors before and after adjusting the instructions. Have exceptions validated by the procurement manager before integration.

Objectives of this training

  • Define the fields, types and normalisation rules for a business table.
  • Classify documents according to their readability and preparation requirements.
  • Extract values with a verifiable location in the sources.
  • Handle missing data, duplicates and contradictions without inventing data.
  • Measure errors and document corrections before delivery.

Who is it for and what are the prerequisites?

Roles and tasks concerned

  • Documentation and research officers building databases from varied documents.
  • Procurement staff, administrators and analysts responsible for reconciling files.

Prior skills

  • Use a generative AI assistant and know how to formulate a structured instruction.
  • Use filters and simple spreadsheet formulae; be familiar with the documents used in your business process.

What you will work on

  1. Defining the data contract

    Objective: Translate procurement reconciliation into verifiable fields. Granularity by order line, types, units, currency and the distinction between missing and illegible data.

  2. Preparing the documents

    Objective: Separate usable sources from documents that need reworking. Inventory, versions, anonymisation, native text and optical character recognition according to the permitted resources.

  3. Extracting with evidence

    Objective: Obtain structured data without filling in the blanks. Instruction, tabular format, supporting excerpts; documents treated as sources rather than instructions.

  4. Reconciling without overwriting discrepancies

    Objective: Consolidate rows from multiple formats. Matching keys, normalisation, duplicates, corrective versions and business decisions.

  5. Measuring quality

    Objective: Distinguish apparent accuracy from reliable extraction. Manual reference: correct values as a proportion of extracted values, omissions as a proportion of expected fields; errors by format.

  6. Delivering an auditable file

    Objective: Enable the procurement department to take over and use the data. Export, data typing, validation statuses, traceability and human review workflow.

What you take away

  • Dictionary of fields and normalisation rules.
  • Version-controlled extraction instruction with output format.
  • Consolidated table with document references and validation statuses.
  • Quality report and register of exceptions requiring a decision.

Method and assessment

  • Short teaching inputs followed by immediate practice on the running case study.
  • Individual work followed by work in pairs, with peer review of outputs.
  • Guided group discussion: what works, what needs correcting and what remains a human decision.
  • Preparation: Provide only fictitious or anonymised documents and check the organisation’s rules on data transmission. Check access to authorised tools and the formats they accept; provide a transcription for illegible scans.

Observable criteria: Each field has a type, a definition and a rule for missing values. ; Each extracted value refers to a document and an identifiable page, cell or section. ; Contradictions are flagged without silently replacing a value. ; Rates of correct values and omissions are calculated against a manual reference. ; The delivered file retains document identifiers and complies with the defined schema..

Frequently asked questions

Can scans be used?

Depending on their readability and the permitted resources; a verified transcription may be necessary.

Are programming skills required?

No, the exercises use structured instructions and a spreadsheet.

What should you do if a value cannot be found?

Leave it missing with an explicit status, without automatic estimation.

Can the review be omitted?

No: critical fields and contradictions require human validation appropriate to the risk.

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