High-Volume Academic Data Processing, Transcription, & Standardization

Document Sorting

As instructed by the client, we logged into Salesforce and downloaded the documents on a daily basis. At times, the client also sent additional files via email, which we uploaded to Salesforce for record-keeping before proceeding. Any illegible or incomplete documents got flagged right away. The client was notified so they could request better copies instead of us wasting time trying to process unclear files.

The other files were immediately sorted as –

• High Priority - Complex Documents, Edge Cases - Assigned to the most experienced team members with knowledge of different university systems and grading scales.
• Standard Priority - Common Formats - Handled by the core team using established templates and grading references.

Human-in-the-Loop Data Extraction

We used FineReader OCR as a first-pass data extraction tool to collect data from images and scanned PDFs (from universities worldwide). In cases where the tool struggled with poorly scanned documents, handwritten text, and overlapping watermarks, our operators performed field-by-field document transcription from the source image/PDF, with specific protocols for –

• Ambiguous Characters – Distinguishing "0" from "O," "1" from "I" in alphanumeric codes.
• Faded or Obscured Grades – Zooming in on source files, cross-referencing with semester totals or GPA calculations to infer missing values.
• Handwritten Annotations – Cross-referencing against the full document with manual transcription where needed.
• Documents with <85% OCR Confidence Score - Bypass OCR entirely and move directly to pure manual document data entry with contextual interpretation by subject matter experts.

Field Mapping and Data Standardization

Once data was extracted from the source documents, we applied a systematic data standardization process to ensure consistency across diverse transcript formats. Each extracted field was mapped to the client's predefined Salesforce schema, regardless of how it appeared on the original document, through a master template covering 50+ common fields (student details, course codes, grades, credit hours, etc.). This included:

• Standardizing date formats (DD/MM/YYYY vs. MM/DD/YYYY vs. Month-Year variations)
• Normalizing course codes across different institutional conventions
• Converting credit systems (semester hours, ECTS credits, quarter units) to a unified format
• Structuring multi-line address fields into discrete components
• Organizing data hierarchically by academic term

Reference Library Creation

To address the grading scale problem, we created and maintained a reference guide. When we encountered a grading system that had not been processed before, the operator escalated it to a subject matter expert, who researched the institution's official grading scale, documented it in the library, and defined the process for handling it. This reference layer became critical for maintaining consistency across operators and reducing decision-making delays during data standardization.

• Documented grading systems by country, university, and time period.
• Kept track of degree nomenclature changes, university mergers, and program rebranding.
• Noted hybrid grading cases across different departments in the same institution (e.g., "First-year numeric, final-year classification-based") for operator awareness.

Flexible Team Capacity

To handle sudden volume spikes without missing the 24-hour deadline, we organized our team into two groups that could scale dynamically based on workload.

• Core Team - To handle daily document volume and update the reference library.
• Backup Team - Cross-trained on this project and involved as needed to handle 2-3X higher document volume.

We also tracked volume patterns by institution type and season (for example, French university transcripts typically surge in June-August as students complete their academic year and apply for fall admissions to U.S. universities) to anticipate demand spikes and position resources proactively.

Edge Case Management

For the 15–20% of transcripts that defied standard templates, we built a two-tier escalation process. This prevented edge cases from derailing delivery timelines while ensuring that the outcomes stayed accurate.

• Tier 1 (Operator Level Processing): Operators had authority to handle common variations (single transfer credit, grade replacement for one course, minor formatting deviations). These were documented in processing notes but followed modified templates.
• Tier 2 (Lead Review): Complex cases—such as dual degrees from two countries, multiple school transfers, students who stopped and restarted, and credentials from unaccredited institutions—were sent to the team lead for review. The leads collaborated with the operators to determine the correct format, checked with the client where necessary, consulted the client when interpretation was required, and created notes for handling similar cases in the future.

Data Validation & Salesforce Data Entry

Every completed dataset underwent a dual data validation and quality check process (performed by the operator and then by the QA lead) before Salesforce data entry was initiated. Error rates were tracked weekly, and any persistent issues were addressed by retraining the operator or refining the template. We also held weekly stakeholder reviews to discuss -

• Any expected change in the incoming document volume.
• Any template adjustments required by the client.
• Edge cases that were processed and how they were handled.
• Any recurring data quality issues flagged by the client's team.