Executive Summary

Many midmarket and enterprise engineering, data engineering, and compliance teams—roughly 120,000 organizations that would be targets for enterprise tooling—struggle with undocumented or inconsistent schemas in legacy databases when attempting cloud migrations, BI onboarding, or audits; this creates project delays, hidden costs, and compliance risks. The pain is concrete: multi-month discovery efforts, repeated manual reverse-engineering, and audit cycles that cost teams time and money and often derail modernization initiatives. You could build a programmatic extractor that connects to common legacy sources (RDBMS, mainframe catalogs, ERP stores), normalizes noisy metadata into a versioned data dictionary, and emits detailed table-level reports, lineage maps, and transformation templates, augmented by LLM-generated human summaries and code-snippets for common ETL tasks. The product should produce machine-readable exports (JSON/CSV), PDF/HTML documentation, and change-tracking to support audits and migration playbooks, aiming at an average contract value in the neighborhood of $30K ACV given the enterprise scope. The market dynamics make this attractive now: an estimated $3.6B addressable market, strong tailwinds from legacy modernization and increasing regulatory scrutiny, and an opportunity to leverage recent AI advances to automate previously manual work; market score and revenue potential metrics I’d assign are high (90/100 and 88/100 respectively) because demand is sizable and immediate. Enterprises are actively moving workloads to cloud and BI platforms and will pay for reliable, auditable artifacts that reduce migration risk and shorten project timelines. To stand out you’ll need highly reliable connectors, deterministic validation layers so automated outputs are auditable, and a developer-friendly UX that combines AI summaries with traceable provenance and human-in-the-loop correction workflows. Be honest about challenges: competition is medium, engineering to handle varied legacy systems is nontrivial, and selling into enterprise procurement and security reviews will require proven controls and reference implementations before scale.

Executive Summary

Market Opportunity

Programmatic extraction of legacy DB data-dictionary into detailed table reports targets a $3.6B = 120,000 midmarket+ businesses x $30K ACV (enterprise tooling for legacy modernization/documentation) total addressable market with medium saturation and a year-over-year growth rate of 8-12% (legacy modernization, data governance & observability tooling).

Key trends driving demand: Legacy modernization -- enterprises migrating workloads to cloud/BI need documented schemas and automated exports; Data governance & compliance -- regulatory and audit needs drive demand for versioned data dictionaries and lineage; AI-assisted developer tooling -- LLMs can auto-generate readable docs, mapping and transformation scripts from noisy metadata; Shrinking legacy talent pool -- fewer specialists raises willingness to pay for automation that encodes institutional knowledge.

Key competitors include Progress OpenEdge — built‑in Data Dictionary / Developer Studio, SchemaCrawler (open‑source), DBeaver (Community + Enterprise), Aqua Data Studio (Idera).

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Programmatic extraction of legacy DB data-dictionary into detailed table reports

Executive Summary

Programmatic extraction of legacy DB data-dictionary into detailed table reports

Executive Summary

Market Validation

Market Opportunity

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