Modernizing Legacy Data with
AI-Powered Extraction & Digitization
Challenge
A global public health organization relied on more than 15 years of survey data stored in PDFs, Word files, and scanned forms. Many documents contained complex tables, coded response options, and handwritten response counts. Because everything had been captured in different formats over the years, analysts struggled with slow manual extraction, inconsistent transcription, and datasets that could not be compared across time. As a result, valuable insights about health trends remained locked in documents instead of informing policy and program decisions.
DDD’s Solution
Digital Divide Data deployed its AI-powered data extraction system to automatically interpret and structure the organization’s legacy files. The platform read question text, answer options, and both handwritten and printed response counts, even when formatting varied widely. All extracted information was standardized into a uniform JSON schema, ready for database ingestion, dashboarding, and statistical analysis. What previously required hours of manual effort per form was now completed in minutes with significantly higher accuracy and consistency.
Impact
The organization achieved 80–90% faster processing, enabling them to digitize their entire historical archive for the first time. Data accuracy improved dramatically, eliminating common transcription errors and producing a unified dataset suitable for trend analysis across multiple years. With their legacy data finally accessible and clean, the team unlocked new insights on community health patterns, strengthened evidence-based decision-making, and modernized their research workflows, all while reducing operational costs.