Glossary

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Glossary / Evaluation and implementation guide

Data Discovery

Data discovery is the process of finding and understanding what data assets exist and which one is authoritative for a given question.

An analyst inspects tables, samples rows, traces where a metric is computed and asks owners how it is maintained.

It differs from cataloging, which indexes assets with metadata at scale; discovery is the human investigation that cataloging supports.

Without it, marketing and sales each report pipeline numbers from different sources and meetings turn into debates about whose figure is right.

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A practical example

Example: asked for Q3 sourced pipeline, an analyst finds three candidate tables — a CRM report, a warehouse mart and a BI extract — samples each, discovers the warehouse mart excludes closed-lost, and documents it as the source of truth.

What to evaluate before investing

  • Can analysts sample and profile tables directly, or must they request extracts from a data team first?
  • Is there a way to see lineage — which upstream feeds and transformations produced a table — before trusting its numbers?
  • Are table owners and update frequencies visible, so stale or orphaned datasets are identifiable?

Limitations and tradeoffs

Discovery depends on tribal knowledge when documentation is thin; findings live in one analyst's head and leave with them.

Pair discovery with lightweight documentation of the chosen source of truth so the next person does not repeat the investigation.

Plan your next step with MeshLine

Connect this decision to your automation, organic marketing and customer lifecycle management. In a MeshLine demo, discuss your existing tools, the scope you need and how to measure the result.

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