Dataplanedataplane

    Master Data

    Master Data You Create, Not Just Store

    A single canonical record for every real-world entity, so duplicates collapse and your systems finally agree on what they're looking at.

    The Idea

    Systems Keep Their Own Records. Your Business Has One Entity

    Every business runs on real-world entities reflected in its data: products, suppliers, locations, customers. The same entity naturally recurs across many systems, acquisitions, and eras, each capturing it under its own name and code, with its own view of the world.

    Master data is the authoritative, shared definition of those entities: one canonical record per real-world thing every system, report, and team can point to. Recognizing those duplicates is a meaning problem, not a string match, so Dataplane proposes the matches and canonical forms, and your domain owners decide.

    Why It Matters

    Canonical Entities Everything Else Can Stand On

    When the same entity lives under five names in five systems, every cross-system question turns into a reconciliation project. A canonical layer, one shared record per real-world thing, turns that scramble into infrastructure everything downstream can build on.

    Systems That Finally Agree

    Every system, report, and team reads the same entity. A supplier is one supplier; spend, demand, and inventory roll up against one record instead of splitting across phantom duplicates.

    History That Survives Renames

    Alias maps keep years of trend, performance, and demand history attached to an entity even as its codes change, so a rename never orphans the past.

    Trustworthy Inputs for AI

    Agents and models reason over canonical entities instead of inflating an already-noisy world with duplicates. Clean entities are the grounding reliable automation needs.

    A Foundation That Compounds

    Canonical records, identifier rules, and value sets land in a model you own and write straight into your ontology, richer with every entity you resolve and never locked in a proprietary hub.

    How It Works

    From Scattered Copies to One Golden Record

    One entity, fragmented across five systems, walked to a single canonical record your team trusts, in four steps.

    Step 1

    The Same Entity, Scattered

    A single real-world entity is recorded separately across systems, acquisitions, and exports. Deterministic tools see distinct entities, and every join, rollup, and forecast built on them inherits the split.

    One entity · four names
    PUMP-4500ERP
    Pump 4500AAcquired Co.
    P4500-REV2MES
    centrifugal-4500Catalog
    Four records. One real pump. The software can't tell.
    Step 2

    Two-Layer Matching

    A lexicographic layer tightens the solution space so the semantic layer can work more effectively. The semantic layer reads attributes, context, and relationships to catch the variants exact rules miss.

    Matching to PUMP-4500
    Pump 4500Alexicographic
    normalized match
    P4500-REV2semantic
    re-coded revision
    centrifugal-4500semantic
    described by attributes
    The semantic layer catches what exact rules miss
    Step 3

    Your Domain Owners Confirm

    Dataplane proposes the duplicates and a canonical form; domain owners who know the data confirm or correct it. Nothing merges silently, and every decision is recorded.

    Proposed merge · awaiting confirmation
    Canonical recordPUMP-4500
    4 variants → 1 entity
    ConfirmEdit
    The people who know the catalog have the final say.
    Step 4

    One Golden Record, History Intact

    The canonical record applies everywhere, with every legacy identifier mapped to it so years of history survive the rename. Identifier rules, alias maps, and value sets write straight into your ontology. Master data work is ontology work under another budget line.

    PUMP-4500 canonical
    Pump 4500AP4500-REV2centrifugal-4500
    3 aliases mapped · history preserved
    OntologyData QualityEnrichmentAgents
    Writes straight into the model everything else reads from

    Outcomes

    Tie Every Match to a Downstream Win

    +0%

    agent accuracy reasoning over canonical entities instead of duplicates

    0

    golden record each system, report, and agent can finally agree on

    0%

    of merges confirmed by a domain owner before they take effect

    Give Every System One Entity to Agree On

    See how Dataplane turns scattered records into one source of truth: duplicates found across your systems, a canonical record proposed for your domain owners to confirm, and the result written straight into your semantic model.

    Explore related capabilities: Data Ontology, Data Quality, and Data Enrichment.