Quantifying dataset comprehensibility — a metric + SaaS to measure effective data processing

Many mid-to-large organizations struggle to know whether their datasets are actually comprehensible to modern analytics and LLM-driven systems, which causes expensive iteration cycles, brittle RAG pipelines, and opaque downstream decision-making. This issue is felt most by data scientists, ML engineers, analytics teams and product owners across roughly 280,000 mid/large organizations globally. You could build a SaaS that produces a standardized, explainable "comprehensibility" metric: a numeric score derived from automated retrieval- and graph-aware probes, LLM-based understanding tests, and domain-specific heuristics, exposed via SDKs, pipeline hooks and a monitoring dashboard. The product would include benchmarking, simulated RAG retrieval and query workloads, APIs to correlate scores with downstream model accuracy and business KPIs, and vertical templates to speed adoption. At an average contract value of $60K, the 280k-account opportunity implies a $16.8B addressable market if you can align with enterprise purchasing patterns. The timing is favorable—LLM-driven analytics require new evaluation metrics, RAG and graph-first retrieval increase actionable information per dataset, and data observability is maturing—so buyers are actively looking for measurable signals that tie data assets to outcomes. To stand out you must deliver reproducible, human-aligned scores that predict downstream value, provide enterprise-grade security and seamless catalog integrations, and be candid about challenges: creating reliable, domain-general proxies for "comprehensibility" and convincing engineering and procurement teams to operationalize a novel metric.