Data Governance · All four engines

Governed data is the first link in the decision chain

Every enterprise has a data policy. Almost none can say whether the estate obeys it. Conformance is asserted annually and breached daily, and every breach feeds the AI and the decisions downstream.

Prophesee Data Governance reads the whole corpus against the policy, surfaces every breach with a named owner, and cleans the estate with governed agents, so conformance becomes a measured number with a date on it.

Entity resolutionClassification and mappingChange-to-document conformanceContinuous monitoringAnomaly detectionEvidence and lineage
The shift

Everyone has a data policy. Almost nobody can say whether the estate obeys it.

The policy lives in a document. The data lives in a thousand systems. Between them sits an annual audit that samples a fraction of the estate a year after the breaches happened, while the same ungoverned data feeds every model and every decision the business makes.

11%
Executives who say their organisation’s data quality is sufficient for AI use
Readiness
1 in 4
Executives reporting audit-detected AI errors that reached boards or external audiences
Exposure

Sources: Workiva Executive Benchmark Survey, May 2026 (n=2,272).

Two estates, one policy

Structured and unstructured, read against the same rules

The market sells these as separate products: master data tools for the systems, content governance for the documents. Prophesee reads both estates against one policy set, and every breach lands in the same exception queue with the same named owners.

One policy rule projecting onto two estates: structured records and fields on the left, unstructured documents and tags on the right, with breaches from both routed into one exception queue of named owners
Structured

Master data, records and fields

Residency, retention and ownership rules enforced on the systems of record. The same customer, supplier and site resolved across every spelling and every system, so the policy applies to the entity, not to whichever copy the audit happened to sample.

Unstructured

Documents, content and tags

Classification, retention and residency on the corpus itself. Nexus sees every document the policy covers, so a file on a share drive is as governed as a row in the warehouse, and a breach in either one looks the same to the owner who has to fix it.

Governance as a side effect

The more you use it, the cleaner it gets

Policies live in Nexus admin. Every question asked through Nexus widens what the policy can see; every breach surfaced gets an owner; every approved fix is executed by governed agents and recorded. Data quality stops being an annual programme and becomes a side effect of daily use.

A ring of four arcs running clockwise: Use (people work), See (signal surfaces), Own (owner named), Fix (agent repairs). Centre text: the more you use it, the cleaner it gets.
A new approach

From policy on paper to policy enforced in the corpus

nexus iconNexus
Today · Data catalogue

The catalogue describes schemas, not conformance. Nobody can ask the estate where the policy is breached and get an answer.

An ontology over the whole corpus

Every document, record and field resolved into entities, owners and applicable policies. Ask where PII sits outside the EU, in plain language, and get a cited answer computed inside your own permissions.

pulse iconPulse
Today · Annual data audit

Conformance is checked by sampling, once a year. The estate is breached daily in between, and the audit describes last year’s estate.

The policy, watched continuously

Retention, residency, classification and ownership rules written in plain English and evaluated across the whole corpus, always. Every breach becomes one severity-scored exception routed to the named data owner.

foresight iconForesight
Today · Data quality dashboard

Dashboards count defects after they have already corrupted a decision. There is no forward view of which data breaks what next.

Predict the defect before the decision

Defects are linked forward to the forecasts, alerts and reports they feed. Models rank where drift, duplication and decay will surface next by decision impact, so the clean-up effort follows consequence rather than volume.

horizon iconHorizon
Today · Clean-up project

Remediation runs as heroic one-off projects that relapse by the next quarter. No trajectory, no attribution, no proof anything moved.

Clean-up as a managed campaign

Model the campaign before funding it, track days to conformance as actuals land, and let governed agents execute the approved fixes. Reclassify, tag, quarantine, dedupe, archive. Permissions, budgets and audit are inherited by construction, and every change carries its approval.

From challenge to decision

Turning data governance into decisions

Problem: Entity resolution
What produces it today: Data catalogue

The same customer, supplier and document exist under different spellings in different systems, so no policy can be applied to "the" record.

Engine: nexus icon Nexus
The applications that replace it: Corpus OntologyGovernance Chat
Problem: Continuous monitoring
What produces it today: Annual data audit

Conformance is sampled once a year, so breaches live in the estate for months before anything notices.

Engine: pulse icon Pulse
The applications that replace it: Data Policy RulesBreach Alerts
Problem: Change-to-document conformance
What produces it today: Policy document

The policy changes, and nobody can say which repositories, retention schedules and classifications the change touches.

Engine: nexus icon Nexuspulse icon Pulse
The applications that replace it: Corpus OntologyRemediation Tracker
Problem: Anomaly detection
What produces it today: Data quality dashboard

Defect counts describe the past. The defects that matter are the ones about to corrupt a live decision.

Engine: foresight icon Foresight
The applications that replace it: Data Risk ForecastDecision Impact
Problem: Evidence and lineage
What produces it today: Clean-up project

Remediation is a spreadsheet of tickets nobody closes, and no one can prove what was fixed, by whom, on whose approval.

Engine: horizon icon Horizonnexus icon Nexus
The applications that replace it: Conformance LedgerAgentic Clean-Up
The applications

12 AI applications that could be relevant

A sample of what becomes possible on the decision layer, not a fixed list: each application draws on the same data foundation and audit trail, and new ones are configured on the engines, not built from scratch.

Nexus
Corpus Ontology

Every document, record and field in the estate resolved into entities, owners and the policies that apply to them.

Nexus
Governance Chat

Ask where customer PII sits outside approved systems and get a cited answer, scoped to your clearance.

Nexus
Agentic Clean-Up

Approved remediations executed by governed agents that inherit permissions, budgets and audit by construction.

Pulse
Data Policy Rules

Write retention, residency, classification and ownership rules in plain English and have the corpus watched continuously.

Pulse
Breach Alerts

Every policy breach becomes one severity-scored exception, routed to the named data owner.

Pulse
Remediation Tracker

Every breach carries its fix status; unresolved exceptions escalate instead of expiring.

Foresight
Data Risk Forecast

Where drift, duplication and decay will surface next, ranked by the decisions they would corrupt.

Foresight
Breach Likelihood

The repositories and flows most likely to yield the next policy breach, with the drivers behind each.

Foresight
Decision Impact

Data defects linked forward to the forecasts, alerts and reports they feed, so quality effort follows consequence.

Horizon
Clean-Up What-If

Model a remediation campaign before funding it: coverage, effort and time to conformance.

Horizon
Days To Conformance

The date the estate reaches policy conformance at the current fix rate, updated as actuals land.

Horizon
Conformance Ledger

Which remediation moved the conformance number, on the record, with attribution.

How work changes

A day in a governed data estate

Today: "Where is our PII?" is a quarterly project across four teams.

With Prophesee:
07:40nexus icon Nexus
The corpus, answerable

She asks where customer PII sits outside approved systems. A cited answer, computed inside her clearance, in seconds.

Today: Breaches surface in the annual audit, a year after they happened.

With Prophesee:
09:15pulse icon Pulse
A breach with an owner

A retention rule she wrote in plain English fires on a share drive. Scored, routed to the system owner, clock running.

Today: Clean-ups are funded on argument and relapse by the next quarter.

With Prophesee:
11:30horizon icon Horizon
Fund the clean-up that moves the number

Two campaigns modelled against days to conformance. She funds the one that shortens it.

Today: Remediation is a ticket queue nobody closes.

With Prophesee:
16:00nexus icon Nexus
The fix that leaves a trail

The approved actions execute agentically across the corpus. Retag, quarantine, archive. Every change lands on the audit trail with her approval attached.

Conformance stopped being a claim. It became a number with a date on it.

One estate, two questions

Govern the estate. Then ask it anything.

You are here

Data Governance

Govern the estate: every record and document read against the policy, every breach owned and fixed.

The other half

Enterprise Search

Ask the estate: cited answers computed inside your permissions, from the same governed corpus.

Explore Enterprise Search →

Enforce the data policy, not publish it

We agree the metric and the baseline in week one, and measure the result on your data.