The Problem We Solve

Why spreadsheets, wikis, and enterprise tools all fall short as data catalogs.

The core problem: no single source of truth for data

As organizations grow, data spreads across dozens of databases, warehouses, dashboards, and tools. Without a central catalog, teams face a predictable set of problems: analysts waste 60–80% of their time searching for and preparing data instead of analyzing it; conflicting KPI definitions produce reports that contradict each other; new team members need weeks to understand what data exists; and compliance reviews become painful because no one knows who has access to what.

Why not Excel?

• No single source of truth: every team has its own version of the spreadsheet, and they diverge quickly.
• No database connectivity: metadata must be manually copied — there is no auto-discovery of tables or columns.
• No lineage or relationships: you can't represent how data flows between assets in a spreadsheet.
• No search or filter: finding a specific table across a 500-row spreadsheet is manual and error-prone.
• No access control: anyone with the file can overwrite anything.

Why not Confluence or Notion?

• No structured data model: wikis are free-form documents. A data catalog needs typed fields (asset type, owner, domain, sensitivity, data quality score).
• No database connectivity: same problem as Excel — no auto-discovery, so metadata goes stale immediately.
• No lineage visualization: there is no way to draw or navigate data flow graphs in a wiki.
• No ERD diagrams: foreign-key relationships between database tables cannot be modeled.
• No data quality tracking: there is no automated completeness metric to tell you what needs documentation.

Why not enterprise tools (Collibra, Alation)?

• Months of implementation: enterprise data catalogs typically take 3–12 months to deploy, requiring dedicated consultants and a lengthy onboarding process.
• Six-figure pricing: annual contracts start at $50K and quickly reach $200K+ for mid-sized deployments.
• Built for large organizations: complex governance workflows, approval chains, and policy engines add overhead that most small teams never need.
• Requires dedicated staff: someone needs to own the tool full-time. Most small data teams don't have that person.