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    Why Trusted Data Beats More Data

    An interactive explainer on how enterprise information becomes a governed, explainable, AI-ready asset, and why that work belongs before ingestion rather than after it.

    Watch the Explainer

    Microsoft builds the platform. Zantaz prepares the data the platform depends on. Everything below makes that idea concrete, from the first principle to the platforms it feeds.

    Start Here

    Five ideas, in order, that explain why Trusted Data Collections exist.

    Advance one idea at a time. Each step adds the supporting point and the proof behind it.

    1. 01

      The platforms are ready. The data is not.

      Organizations have invested in Microsoft Fabric, Snowflake, Databricks, and generative AI. The value of those platforms depends entirely on the trustworthiness of the information entering them.

      Up to 60 percent of an unstructured estate is redundant, obsolete, or trivial.

    1 of 5

    Metadata Provenance Through the Modern Data Stack

    Provenance is established at the source and carried all the way to the outcome.

    Information where it already lives, across the Microsoft estate and beyond.

    • Microsoft 365 and SharePoint
    • Exchange and OneDrive
    • Windows File Shares
    • Cloud storage and collaboration platforms
    • Legacy archives

    What Gets Preserved

    The same document, with and without the context that makes it trustworthy.

    Q3-Regional-Contract-Final.docx

    Everything the file system alone can tell you

    • File name
    • File type and size
    • Created and modified dates
    • Folder path

    Without preserved context, no one can answer whether this document is current, properly owned, appropriately retained, or safe for an AI application.

    Value by Role

    The same capability matters differently to every role.

    Choose a role to see the question it starts with and the outcomes it changes.

    When Copilot answers a question today, can anyone show which records supported the answer?

    AI performs best on relevant, well-contextualized information. Collections give copilots, agents, and retrieval applications curated content instead of the entire uncontrolled estate.

    • Higher relevance in retrieved information
    • Fewer ungrounded or contextually inaccurate responses
    • AI access limited to approved content
    • Answers traceable back to supporting records

    Where It Lands

    Trusted Data Collections make the platform the enterprise already owns more productive.

    Microsoft Fabric

    Collections are projected into OneLake with source identifiers, classifications, ownership, security context, and collection history. Purview extends visibility by cataloging Fabric assets and recording supported downstream lineage.

    • More useful and better-governed Lakehouse data
    • Stronger context for analytics and AI
    • Less low-value data entering Fabric
    • Improved traceability between source and business output
    • A stronger foundation for Purview data products
    • Better Copilot grounding and end-to-end lineage

    Collection Builder

    Every collection starts with a business purpose, not a folder structure.

    Choose a purpose to see what is included, how it is classified, what is excluded, and where it is approved for use.

    Sources Read In Place

    • SharePoint account sites
    • Exchange correspondence
    • Departmental file shares

    Classifications Applied

    • Customer or account
    • Owner and territory
    • Record type
    • Sensitivity

    Excluded as ROT or Out of Scope

    • Duplicate proposal drafts
    • Superseded pricing files
    • Expired opportunity notes

    Approved Downstream Uses

    • Copilot grounding for account teams
    • Analytics and reporting
    • Enterprise search

    The Questions Enterprises Ask First

    Direct answers to the six questions that come up in every evaluation.

    A catalog describes what an organization has. A Trusted Data Collection prepares information for approved use. Content is discovered, classified, enriched, and organized into a governed dataset with its provenance intact, so it can be activated rather than only inventoried.

    Governance belongs before ingestion, not after it.

    Modern platforms can process, analyze, and activate data, but they cannot determine whether a source document is relevant, properly owned, appropriately retained, accurately classified, or safe for a particular AI use case. Trusted Data Collections answer those questions first, turning data preparation from a one-time migration task into a repeatable foundation for AI, analytics, governance, compliance, and legal defensibility.

    Trusted Data Collections OverviewMirror or Migrate