Data Scoping & Extraction

Data preparation frequently dominates an RSE project's timeline. Use these heuristics to scope and execute data tasks efficiently.

Estimating the Effort

Apply Multipliers: Multiply your initial data cleaning estimate by at least 2. Cleaning is non-deterministic; fixing one issue often reveals another.
Scope by Purpose: Only clean what is strictly required for the final output. Drop unnecessary fields immediately.
Fix Parsing First: Never estimate a task on broken parsing. Resolve malformed data or rogue delimiters upfront to eliminate "fake" edge cases.
Timebox Samples: If data is too massive to fix parsing upfront, pull a random sample (e.g. 5%). Timebox the effort required to load it cleanly before providing a full estimate.

Survey Cardinality: Use summaries and value counts to spot structural anomalies early (e.g., a boolean field with 5 distinct values expands scope).
Inspect the Tails: Sort numerical and date fields to manually check absolute minimums and maximums (e.g., negative values, 1970 dates, 999999).
Hunt Defaults: Explicitly flag placeholders like -1, 0, 1900-01-01, or test. They pass basic validation but corrupt downstream aggregations.
Check "Other" Buckets: Manually inspect free-text fields associated with "Other" or "Unknown" categories for complex, multi-value scenarios.

Scope by Field-Level Accuracy: Do not estimate based on raw Character Error Rate (CER). Scope strictly by the required field-level accuracy (e.g., Year).
Pilot-Based Estimation: Run a 50-100 document pilot to calculate actual Human-in-the-Loop (HITL) correction time before estimating the full project.
Quarantine Queues: Explicitly budget engineering time to build exception handling for unparseable documents. Do not assume a 100% straight-through processing rate.
Language & Unicode Support: Verify the OCR engine's native language pack support. Explicitly allocate time for Unicode normalization (e.g., NFKC) before applying extraction.
LLM Post-Processing: Consider LLMs over manual dictionaries for semantic repair of OCR text. Implement quality gates to catch LLM hallucinations (over-correction).
Layout-Aware Cleaning: Survey document layouts first. Complex layouts (e.g., multi-column) require estimating for layout reconstruction, not just text cleaning.