Dataplanedataplane

    Data Healing

    Fix Data Issues In-Line and Learn From Every One

    Dataplane corrects erroneous rows automatically or with guidance on how to resolve. Every fix becomes a durable rule and feedback to upstream teams.

    The Idea

    An Alert Isn't a Fix. It's a Backlog.

    Most data tools stop at detection. They raise a flag, file a ticket, and hand the problem to someone else. The error is now visible but downstream teams still have to drive decisions while everyone waits on an upstream fix that may take weeks. So teams improvise: a spreadsheet patch here, a one-off script there, all of it unrepeatable, undocumented, and gone by next cycle.

    Dataplane closes the loop. Users describe how an error should be resolved in plain language, and Dataplane corrects the rows in place and logs exactly what changed and why. Downstream teams keep moving instead of stalling on a fix. And because every correction is captured, upstream teams get clear feedback on what to fix at the source, so the root cause gets addressed instead of patched again next cycle.

    Why It Matters

    Catching an Error Isn't the Same as Fixing It

    The decision gets made either way. A planner still places the order, the model still trains, the report still ships on whatever number is in front of them. An error you flag but never fix lands in that decision just like one you never caught. Healing is what changes the outcome, and every fix you make tells central teams where the real problems are.

    Fixed, Not Just Flagged

    A flag doesn't change the number anyone acts on. The bad value still flows into the forecast, the dashboard, the order. Only a correction does, closing the gap between knowing and acting.

    Aimed by Attribution

    Healing starts from a precise diagnosis. Attribution names the exact rows at fault, so corrections land on the real problem instead of the first row that happened to fail a check.

    Prioritize by Impact, Not Volume

    Upstream teams usually fix whoever escalates loudest. Every captured correction shows central teams which root causes cost the most, so when resources are scarce, the fixes that matter win out over the ones that shout.

    Auditable by Default

    Every correction records what changed, the rule behind it, and who approved it. The original value is preserved, so you can prove how a number was healed, and roll it back if needed.

    How It Works

    From Flagged to Healed in One Loop

    Follow a single failure all the way through: weeks of missing sales history that would throw off a demand forecast, from the rule that catches it to the metadata that remembers it.

    Step 1

    Failures Identified by Data Quality Rules

    Healing starts where detection ends. Your data quality rules flag the values that break, and attribution pins each failure to the exact row at fault, so every fix has a precise target instead of a vague quality report.

    sales_history · weekly2 failures
    A-1042 · W21318
    A-1042 · W22null
    A-1042 · W23305
    B-2200 · W221,204
    B-2200 · W23null
    B-2200 · W241,156
    Rule: sales_units required for every active SKU-week
    Step 2

    Describe the Heal, If You Want a Say

    Dataplane proposes a fix for every failure on its own. When the right correction depends on context, describe the logic in plain language and it heals to your intent instead of a generic default, no scripting required.

    Edit Rule
    Impute each missing value from the adjacent week. If the gap is a new-product launch, use 0 instead.

    Dataplane proposes a default for every failure. Refine it in plain language only when you want a say.

    Step 3

    Healed in Place

    The value at fault is repaired where it lives, so consuming teams get clean data without waiting on an upstream backfill.

    Healed Report2 rows updated
    sku · weekwasnow
    A-1042 · W22null312*
    B-2200 · W23null1,180*
    *imputed from adjacent weeks
    Step 4

    Logged and Reversible

    Every heal produces a report: what changed, the method behind it, and the rule that triggered it. The original value is retained, so you can show exactly how each number was produced and roll it back at any time.

    Healing reportreversible
    A-1042 · W22null → 312
    method: mean of W21, W23 · rule: sales_units required
    B-2200 · W23null → 1,180
    method: mean of W22, W24 · rule: sales_units required
    Original values preserved · every heal can be rolled back
    Step 5

    Capture the Metadata

    The run is recorded as structured metadata: which rules failed, how many rows were healed, and by what method. That record feeds monitoring and the ontology, so recurring failures stay visible to upstream teams and the next cycle heals smarter.

    Run metadata2 healed
    rulefailedhealed
    sales_units present22
    units ≥ 000
    Captured to the ontology · recurring failures surface for upstream teams

    Outcomes

    Resolution You Can Stand Behind

    0%

    faster time to resolution on recurring errors

    0×

    greater data quality resolution throughput

    0%

    reduction in overall data quality effort

    Stop Flagging Errors. Start Healing Them.

    See how Dataplane closes the gap between detection and resolution: failures attributed to the row at fault, healed to your guidance, and logged so every correction is reversible and audit-ready.

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