Dataplanedataplane

    Data Ontology

    A Semantic Model of Your Data

    A living model of what your business means, so teams can find the right data, trust its definitions, and ground every system in one source of truth.

    The Idea

    Your Data Describes Tables. Your Ontology Describes Your Business.

    Every organization runs on concepts that are reflected in its data: products, locations, suppliers, customers, calendars. At large enterprises, those concepts are scattered across systems, spreadsheets and teams, each with its own view of the world.

    An ontology is the explicit, machine-readable version of that meaning: a semantic model that names the entities you care about, defines their relationships, captures the rules that identify them, and maps every raw column back to what it actually represents. It is the shared definition of your business that humans and software can both rely on.

    Why It Matters

    An Explicit Model Your Whole Organization Can Build On

    When the meaning of your business lives in tribal knowledge, every question becomes a negotiation. Serializing it as a model that is explicit, shared, and machine-readable turns that meaning into infrastructure that compounds across your organization.

    One Shared Definition

    Every team and every system reads the same meaning. A supplier is one supplier, a product hierarchy is the product hierarchy, with no more reconciling whose version is right.

    Answers You Can Trust

    When the rules that identify and relate your entities are explicit, the numbers downstream are defensible. You can point to exactly why a result means what it says.

    Ready for AI & Automation

    Agents and automations act on what your business actually means, not on raw tables they have to guess at. An explicit model is the grounding they need to be reliable.

    An Asset That Compounds

    The model is yours: versioned, exportable, and richer with every project. Improvements accrue in one place instead of fragmenting across tools.

    How It Works

    Dataplane Drafts the Model. Your Team Signs Off

    From scattered sources to a living semantic model in four steps.

    Step 1

    Connect Your Sources

    Point Dataplane at your warehouses, databases, and document stores. No upfront modeling, no blank editor. Just connect what you already have.

    Warehouse
    Snowflake
    ERP Tables
    SAP
    Docs & CSVs
    SharePoint
    Spreadsheets
    Excel
    Step 2

    Dataplane Proposes a Model

    It profiles structure and content and drafts a first-pass ontology: the entities, relationships, and identifier rules it sees in your data.

    Draft ontology
    sold throughoccurred oncontainsreturn ofis ais ais aWebChannelStoreChannelCatalogChannelChannelCalendarDateOrderLineItemReturn
    Step 3

    Your Team Validates

    Confirm an entity, merge aliases, correct a rule. Each decision teaches the model what your business actually means.

    Validated by your team
    Channel
    3 subtypes confirmed
    Order
    2 relationships mapped
    LineItem
    contained in Order
    Return
    returnOf LineItem
    Step 4

    It Grounds Everything

    Quality, enrichment, lineage, and agentic systems all read from the same definitions, and write improvements back into the model.

    Ontology
    QualityEnrichmentLineageAgents

    Outcomes

    Measurable From Day One

    0%

    reduction in effort spent reconciling data definitions

    0×

    faster integration of new systems on shared data model

    +0%

    improvement in agent accuracy using ontology semantic layer

    Give Your Business a Model It Can Build On

    Start with the model as your first project, or let it accrue underneath your first cleaning and transformation work. Either way, you walk away with a model of your business that compounds. See how the ontology powers agentic systems once it all connects.

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