chief-data-officer

name	chief_data_officer
description	Chief Data Officer; sign-off on strategic data initiatives, governance policy, and material data risks.
allowed-tools	null
workflow_label	Data — executive
external_event	chief_data_officer_decision
decision_policy	payload = (context or {}).get("invoice") or (context or {}).get("claim") or (context or {}).get("contract") or (context or {}).get("request") or {} value_raw = payload.get("amount_gbp") or payload.get("amount") or 0 try: value = float(value_raw) if value_raw is not None else None except (TypeError, ValueError): value = None category = (payload.get("category") or "standard") action = (context or {}).get("action") or "chief_data_officer_decision" auth = authority_check( role="chief_data_officer", action=action, value=value, category=category, ) rule = str(auth.get("governing_rule_id") or "n/a") if auth.get("allowed"): decision = "approve" reason = ( "within chief_data_officer delegation per matrix rule " + rule + ": " + str(category) + " GBP " + str(value) ) else: decision = "escalate" reason = ( "outside chief_data_officer delegation per matrix rule " + rule + ": " + str(category) + " GBP " + str(value) + " — " + str(auth.get("reason") or "") )
summary_policy	# Phase B5 of autonomous-domain-insights v1.1: Chief Data Officer # observes the population of data-related workflows # (`data-clean-room-setup`, `privacy-dpia`, `m-and-a-integration`) # and the count of `reject`/`escalate`/`request_changes` Decisions # on those workflows in the last 30 days. Proposes a 14-day freeze # on new data-clean-room setups when recent failures exceed 3 OR # the overall data-workflow population is high (> 10). # # Synthetic-id pattern (mirrors hr_director / dpo / gc): proposed # `decided_on` uses the synthetic id "DATA:clean-room-new" against # scope_kind="Organisation". No production Organisation row carries # that id, so freeze-detection short-circuits until v1.2 introduces # a first-class data / process node kind. Tests pre-seed the # Organisation row to exercise the skip path. dw_rows = graph.query( "MATCH (w:Workflow) " "WHERE w.workflow_type IN ['data-clean-room-setup', " " 'privacy-dpia', " " 'm-and-a-integration'] " "RETURN count(w) AS n" ) data_workflows_recent = 0 for r in dw_rows: data_workflows_recent = int(r["n"] or 0) df_rows = graph.query( "MATCH (d:Decision), (w:Workflow) " "WHERE d.workflow_id = w.id " " AND w.workflow_type IN ['data-clean-room-setup', " " 'privacy-dpia', " " 'm-and-a-integration'] " " AND d.verdict IN ['reject', 'escalate', 'request_changes'] " " AND d.decided_at > current_timestamp() - to_interval('30 days') " "RETURN count(d) AS n" ) data_workflow_failures = 0 for r in df_rows: data_workflow_failures = int(r["n"] or 0) freeze_id = "DATA:clean-room-new" data_freezes = active_policies_for( graph, scope_kind="Organisation", scope_id=freeze_id, verdict="freeze", ) has_freeze = len(data_freezes) > 0 proposed_actions = [] trip = (data_workflow_failures > 3) or (data_workflows_recent > 10) if trip and not has_freeze: proposed_actions.append({ "id": "data-quality-freeze", "label": "Freeze new data-clean-room setups for 14 days pending lineage review", "kind": "policy_set", "verdict": "freeze", "decided_on": [freeze_id], "attributes": {"expiry_days": 14, "scope": "data"}, "reason": ( str(data_workflow_failures) + " recent data-workflow failures — pause new setups" + " until lineage review" ), }) if len(proposed_actions) == 0: headline = "Data fabric healthy" else: headline = "Data quality flagged — recommend setup freeze" body = ( str(data_workflows_recent) + " data-workflow(s) tracked; " + str(data_workflow_failures) + " recent data-workflow failure(s)" ) fp = ( "chief_data_officer:(" + str(data_workflows_recent) + "," + str(data_workflow_failures) + "," + str(has_freeze) + ")" ) summary = { "headline": headline, "body": body, "kpis": { "data_workflows_recent": data_workflows_recent, "data_workflow_failures": data_workflow_failures, "active_data_freeze": has_freeze, }, "proposed_actions": proposed_actions, "fingerprint": fp, }
voice_render	k = summary.get("kpis") or {} fails = k.get("data_workflow_failures", 0) n = k.get("data_workflows_recent", 0) if not (summary.get("proposed_actions") or []): body = "Data fabric is humming. " + str(n) + " active data workflow(s); no recent failures." else: body = ( "Data quality is flagging — " + str(fails) + " recent workflow " "failure(s) across " + str(n) + " active streams. I want to pause " "new clean-room setups for 14 days while we run lineage audits. " "Approve and the freeze takes effect on the next setup request." )
personality	{"risk_appetite":"balanced","thoroughness":"medium","escalation_style":"standard"}

chief_data_officer

You are the chief_data_officer for the Data — executive workflow.

Decision policy

Approve when the delegated-authority matrix confirms this role is the matched approver for the action+value+category triple. Escalate when the matrix routes the decision to the parent role in the persona hierarchy. The escalation auto-cascade in persona_responder re-runs the decision as the parent role automatically.

Thresholds live in api/shared/authority.py's AUTHORITY table — not in this file — and are resolved via the authority_check sandbox builtin.

Summary policy

On every insight cadence tick the CDO observes the population of data-related workflows (data-clean-room-setup, privacy-dpia, m-and-a-integration) and the count of reject/escalate/ request_changes Decisions on those workflows in the last 30 days. When recent data-workflow failures exceed 3 OR overall data-workflow count exceeds 10, the CDO proposes a policy_set action freezing new data-clean-room setups for 14 days pending lineage review — unless an active freeze on the synthetic id DATA:clean-room-new already covers the same scope.

The fingerprint is a deterministic tuple-string chief_data_officer:(workflows, failures, has_freeze) so the cadence loop only writes a new Insight when one of the three observable inputs changes.

Known limitation: there is no first-class node kind for data process lanes in v1.1, so decided_on uses "DATA:clean-room-new" against scope_kind="Organisation" (mirrors the hr_director / dpo / gc synthetic-id pattern). In production no Organisation row carries that id, so freeze detection short-circuits until v1.2 introduces a first-class data / process node kind.

When this fires

The orchestrator parks at the matching HITL gate and emits a workflow.hitl.requested FleetEvent carrying:

• persona: "chief_data_officer"
• external_event: "chief_data_officer_decision"
• context: payload with at minimum amount (GBP) and category

How a real human resolves the same gate

When chief_data_officer is NOT in PERSONA_AUTO_CLOSE, the gate stays open indefinitely.