Know and understand importance, characteristics and uses of verification including visual checking and double data entry to reduce data entry errors

15 Proofing (Verification)

Learning Objectives

• Explain why verification (proofing) is essential when handling data.
• Identify the purpose and key characteristics of effective verification (AO3 – analyse/evaluate reliability).
• Describe the software tools and techniques used for proofing, including visual checking, double data entry, validation rules, cross‑checking totals, checksums and audit trails.
• Apply appropriate proofing techniques in documents, spreadsheets, databases and presentations to minimise data‑entry errors.
• Understand the eSafety, data‑protection and health‑&‑safety implications of proofing personal or critical data.

Why Proofing Matters

Data underpins every decision, report and analysis. Errors can cause:

• Incorrect conclusions and poor decisions.
• Loss of credibility for individuals and organisations.
• Financial loss, legal issues or safety hazards (e.g., wrong dosage in a medical record).
• Wasted time correcting mistakes after they have been used.

Proofing is the systematic process of checking data for accuracy before it is stored, processed or published.

Purpose and Characteristics of Verification (AO3)

• Systematic: Follow a documented procedure each time data is entered.
• Independent: Where possible, the checker is not the original data‑entry operator.
• Timely: Errors are identified as soon as possible, ideally before the data is used.
• Documented: Record how verification was carried out and any corrections made.
• Risk‑based: Critical data receives more rigorous proofing.

Proofing and eSafety / Data Protection

When personal or sensitive information is being proofed, learners must also consider:

• Confidentiality: Verify that no unauthorised personal data is exposed; use redaction, encryption or password‑protected files where required.
• Data‑protection compliance: Ensure that proofing actions respect GDPR or local data‑privacy regulations (e.g., do not copy personal data to insecure devices).
• Health & safety impact: Incorrect data in safety‑critical systems (e.g., engineering specifications, medical dosages) can lead to physical harm.

Software Tools for Proofing

Spell‑check & Grammar‑check – Quick Guide

• Activate via the Review tab (Word, PowerPoint) or Tools menu (Google Docs/Sheets).
• Common settings: “Ignore ALL CAPS”, “Add to dictionary”, “Suggest alternatives”.
• Limitations: cannot detect correctly‑spelled but factually wrong data; rely on the user to accept or reject suggestions.

Validation Checks (Automated Field‑Level Validation)

File‑format Considerations

• Proofed documents should be saved in a non‑editable format (PDF/A) for distribution.
• When sharing spreadsheets, consider exporting to CSV for data‑exchange but keep a master Excel/Google‑Sheets file with validation rules intact.
• Databases should be backed up in a format that preserves field constraints (e.g., .accdb for Access, .sql dump for MySQL).

How‑to: Set a Range Validation Rule in Excel

1. Select the cells that will contain the data (e.g., B2:B101).
2. Go to Data → Data Validation.
3. In the “Allow” dropdown choose Whole number (or Decimal).
4. Set “Data” to between and enter the minimum and maximum values (e.g., 0 and 120).
5. Optionally add an Input Message and an Error Alert, then click OK.

How‑to: Set a Field‑type Constraint in Microsoft Access

1. Open the table in Design View.
2. Select the field you want to constrain (e.g., Salary).
3. In the “Data Type” column choose Number and set the “Field Size” (e.g., Long Integer).
4. In the “Validation Rule” box type a condition, e.g., >=0 to prevent negative salaries.
5. Enter a helpful message in “Validation Text” (e.g., “Salary must be a positive number”).
6. Save the table – the rule is now enforced for all data entry.

Proofing Techniques

1. Visual Checking (Manual Review)

Compare the data displayed on screen (or printed) with the original source document.

• Low cost; quick for small data sets.
• Effective when the source is a hard copy or a scanned image.
• Relies on concentration; fatigue can cause missed errors.

2. Double Data Entry

Two separate operators enter the same data independently; the two versions are then compared automatically.

• Detects 95‑99 % of random typing errors.
• Can be supported by software that flags mismatches.
• Requires twice the labour time.

Step‑by‑Step Double Data Entry Process

1. Prepare two identical data‑entry screens or forms.
2. Assign two different operators (Operator A and Operator B).
3. Each operator enters the complete data set independently.
4. Run a comparison routine that flags any mismatched fields.
5. For each mismatch, refer back to the original source document and correct the erroneous entry.
6. Record the final, verified data set and keep a log of the verification actions taken.

3. Other Verification Methods (required by the syllabus)

• Field‑level validation – built‑in checks (range, format, etc.) that give immediate feedback.
• Cross‑checking against source totals – compare the sum of line items with a grand total supplied on the source form.
• Checksum / Audit‑trail verification – automatically generated codes that confirm data integrity during transfer or storage.
• Peer review – a colleague checks a completed dataset before it is finalised.
• Re‑entry of critical fields only – high‑risk items (e.g., financial amounts) are entered twice even if the rest of the record uses single entry.

Comparison of Proofing Techniques

Proofing in the Three Practical Components (Section 15.2)

• Document Production (Word, Google Docs)

  • Spell‑check & grammar‑check.
  • Manual proof‑reading and “Track Changes”.
  • Peer review of content and layout.
  • Save final version as PDF/A to preserve formatting.

• Spreadsheets (Excel, Google Sheets)

  • Data‑validation rules (range, list, date, custom formula).
  • Double entry of critical columns (e.g., totals, financial amounts).
  • Visual inspection of formulas and cross‑checking totals.
  • Export to CSV only after proofing is complete.

• Databases (Access, MySQL, etc.)

  • Field‑type constraints, mandatory fields, check‑digit verification.
  • Audit‑trail logging of inserts/updates.
  • Peer review of query results and reports.
  • Backup in a format that retains constraints (e.g., .accdb, .sql dump).

• Presentations (PowerPoint, Google Slides)

  • Spell‑check and peer review of slide text.
  • Visual check that charts/tables match source data.
  • Export final version as PDF to lock in content.

Practical Example – Double Entry in a Spreadsheet

School exam scores entered by two operators. The comparison highlights a discrepancy that is then corrected.

Tips for Reducing Data‑Entry Errors

• Use clear, well‑structured source documents (tables, labelled fields).
• Train staff on common error types (transposition, omission, duplication).
• Implement field‑level validation (numeric only, date format, mandatory fields).
• Schedule regular breaks to avoid fatigue.
• Maintain an error‑log and analyse trends to improve the proofing process.
• When handling personal data, double‑check that no sensitive information is inadvertently displayed.

Suggested Diagram

Summary

Proofing is a vital quality‑control step in ICT. By understanding why it is needed, applying systematic characteristics, and using the appropriate software tools and techniques (spell‑check, grammar‑check, validation rules, visual checking, double data entry, cross‑checking totals, checksums and audit trails), learners can dramatically reduce data‑entry errors across documents, spreadsheets, databases and presentations. This not only improves the reliability of information for decision‑making (AO3) but also safeguards personal data, complies with eSafety standards, and prevents health‑ and safety risks.