Dataplanedataplane

    Data Quality

    Trusted Data, Everywhere It's Consumed

    Define expectations in natural language. Dataplane creates domain-aware checks that run wherever your teams consume data.

    The Idea

    Data Quality Shouldn't Require a Data Team

    Validating data has always been an engineering job, the work of someone who can read the schema, write the checks, and maintain them as things change. That bottleneck is why so many quality problems go unaddressed: the people who know what "right" looks like aren't the people who can encode it.

    Dataplane removes the handoff. It profiles your data and surfaces what to look for automatically, so no one starts from a blank rulebook. Anyone can define a rule in plain language, with no SQL and no pipeline access. And every rule you accept becomes part of the data fabric itself, traveling with the data wherever it's consumed instead of sitting in a script someone has to remember to run.

    Why It Matters

    The People Who Use the Data Know What Good Looks Like

    Consumers, like planners, analysts, and operators, are the ones who feel it when a number is wrong. They know what the data is supposed to say. Put the power to define quality in their hands, and the rules finally reflect how the business actually works, leading to better decisions and better outcomes.

    Catch the Silent Errors

    Domain-aware rules find the costly mistakes that pass every generic check: the stockout read as demand, the drifted unit, the decimal slip in a cost feed.

    Name the Row at Fault

    Attribution pins each failure to the exact rows that caused it, so fixes land where they belong instead of in a vague quality report nobody can act on.

    Heal It in Place

    Corrections are applied where the error actually lives, documented, repeatable, and reversible, so downstream teams get clean data without waiting on anyone upstream.

    Move a Metric You Report

    Tie clean data to a number you already track, like forecast accuracy, fill rate, or margin, and trace the gain back to the specific interventions that earned it.

    How It Works

    Bring Your Data. Pick Your Metric. Watch It Move.

    One messy feed, walked all the way to a forecast you can trust, in five steps.

    Step 1

    Connect Your Data

    Point Dataplane at any source: warehouses, databases, apps, or a flat file. No pipeline rebuild. It reads what's there and immediately understands the entities inside: products, regions, costs, demand.

    ZDEMAND_WKMaterial Demand
    skuregionunitscost
    A1042West318$4.10
    A-1042East0$4.10
    B-2200West96$41.00
    B-2200East1,440$4.10
    Valid numbers. Four silent errors hiding inside.
    Step 2

    Review Proposed Rules

    Instead of authoring checks from scratch, you review domain-aware rules grounded in your data's meaning. Accept the good ones with a click.

    Proposed rules · accept or reject
    sku must match the material master✓
    units = 0 may be a stockout, not demand✓
    cost within 3× vendor price band✓
    region in known setrejected
    Step 3

    Identify Data Quality Issues

    Attribution pins each failure to the exact rows that caused it. No vague quality report. Every issue is traced to the specific record at fault and the reason it broke, so you know precisely what to fix and why.

    4 errors · pinned to the row
    A1042 · West — no match in material master → maps to A-1042
    A-1042 · East — units 0 → stockout, not zero demand
    B-2200 · West — cost $41.00 → decimal slip
    B-2200 · East — units 1,440 → cases counted as eaches
    Step 4

    Heal It in Place

    Dataplane heals each issue automatically, and takes your guidance whenever you want a say in how. Corrections land where the error actually lives, documented, repeatable, and reversible, before anything reaches the forecast.

    Healed in place · auditable
    sku · regionwasnow
    A1042 · WestA1042A-1042
    A-1042 · East0291*
    B-2200 · West$41.00$4.10
    B-2200 · East1,440120
    *imputed from stockout-adjusted history · every change logged & reversible
    Step 5

    Watch the Metric Move

    Clean inputs show up where it counts. The business metric you set at kickoff, whether forecast accuracy, fill rate, or margin, moves, and every gain traces back to the specific issues you healed.

    Forecast accuracy (MAPE)
    31%
    error, down from 31%
    Before
    After

    Outcomes

    Quality You Can Put a Number On

    0×

    more data quality coverage than hand-written checks

    0%

    less time spent finding, correcting, and re-checking data

    0×

    cheaper to fix an error at entry than after it reaches a decision

    See the Errors You're Paying For, Then Watch Them Disappear

    Bring a dataset and a metric you care about. We'll show you where the silent errors are hiding, attribute them to the rows at fault, and heal them in place, with the work auditable end to end.

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