Every UAE data leader we have engaged with has run a data quality assessment at some point — mapping domains, scoring dimensions, identifying the top issues. What is rarely present six months later is the specific evidence that data trust is still holding: the specific reconciliation performed last week against source systems, the specific freshness SLA maintained on the executive dashboard's underlying feeds, the specific consistency check catching the mismatch before it reaches the board pack. Data quality assessed is data quality once measured; data trust is data quality operated as SLA.
The instinct is to buy a data quality platform or run periodic quality campaigns. The instinct treats data trust as a project. What produces sustained data trust is running the operational discipline — reconciliation as monthly cadence with named ownership, data-freshness SLAs enforced per feed class, cross-system consistency monitoring with anomaly surfacing, and executive scorecard cadence showing trust score trajectory. C3 does that work as a 12-month subscription. Data trust as SLA, not data trust as aspiration — and the honest position is that the retainer only makes sense if data trust is treated as an operational commitment, not a periodic exercise. The reconciliation nobody noticed had broken is the one that surfaces at the board meeting.
Six operating streams,
running on monthly cadence.
Six operating streams sequenced across onboarding (M 01), baseline period (M 02-03), and steady state operations (M 04+). Each stream has named cadence, SLA commitment, and Practice Lead accountability.
Data quality SLA enforcement
Data quality dimensions (completeness, accuracy, timeliness, consistency, validity) enforced monthly per feed class. Not one-time scoring — active SLA enforcement with named quality owner per domain. Quality drift surfaced within named cadence.
Reconciliation cadence with named ownership
Monthly reconciliation across source systems and consumption layers with named ownership per reconciliation pair. Reconciliation failures surface within 2 business days with named remediation ownership. The reconciliation nobody noticed had broken is the one that surfaces at the board meeting.
Data-freshness governance
Data-freshness SLAs enforced per feed class (real-time / near-real-time / batch / daily / weekly). Freshness violations surface within named SLA thresholds. This is where most retainers do the load-bearing work — the freshness SLA nobody enforces produces the trust degradation.
Cross-system consistency monitoring
Continuous monitoring of cross-system consistency for entities that appear in multiple systems (customer, product, transaction, employee). Anomaly surfacing with named remediation routing. Not batch reconciliation — continuous consistency governance.
Trust score trajectory review
Quarterly trust score review with executive sponsor and data consumer stakeholders. Trust score trajectory measured against target with named improvement priorities per quarter. Trust score presented as evidence not opinion.
Executive scorecard & review
Monthly executive scorecard (trust score per domain, reconciliation completeness, freshness SLA compliance, consistency anomaly trend) with named target trajectories. Direct monthly review with data leadership and executive sponsor. Board-defensible data trust reporting cadence.
Twelve-month subscription.
Three lifecycle stages.
The retainer runs for 12 months minimum with three lifecycle stages: onboarding (M 01), baseline period (M 02-03), and steady state operations (M 04-12) with the annual review gating renewal. Monthly cadence and SLA commitments are steady from M 02 onward.
Data trust,
run on SLA cadence not periodic campaign.
Every C3 subscription follows a fixed operating model tuned to your data landscape in the first month. Not a data quality platform selection; not a periodic quality campaign. The rhythm that produces sustained trust score, reconciliation discipline, and consistency governance across the 12-month cadence.
From data trust as periodic campaign
to data trust as operational SLA.
A typical pre-engagement state has data quality assessed at some point, reconciliation ad-hoc, freshness SLAs undefined, and BI users routing around trust issues with manual workarounds. The subscription produces the operating cadence under which trust score, reconciliation completeness, and consistency governance sustain measurably.
Reference pattern. Some subscriptions surface that the data platform is stronger than assumed and the leverage sits on operating discipline rather than tooling addition — the honest output is 'the platform is right; the retainer's job is discipline not procurement.' That's a legitimate finding, not a failure to justify tooling upgrades. The alternative is manufacturing quality-tooling findings to sell platform additions the data team doesn't need — which erodes the trust operations advisor role the retainer requires.
A UAE bank,
trust score sustained above 95% across the year.
Representative pattern for a UAE bank with mature data platform investment but experiencing recurrent trust degradation between quarterly quality campaigns. Ranges reflect target outcomes NexITC underwrites in scope for this class of engagement. N=1 — illustrative composite, not a specific client.
Five service elements,
each with monthly SLA cadence.
Every service element has documented SLA commitment, monthly or continuous delivery cadence, and named Practice Lead accountability. Not one-time deliverables — recurring operational outputs.
Data Quality SLA Enforcement per Domain
Quality dimensions (completeness, accuracy, timeliness, consistency, validity) enforced monthly per feed class. SLA: quality score above target per domain monthly; drift escalated within 2 business days.
Monthly Reconciliation Cadence with Named Ownership
Monthly reconciliation across source systems and consumption layers with named ownership per reconciliation pair. SLA: 100% reconciliation completeness monthly; failures escalated within 2 business days.
Data-Freshness Governance per Feed Class
Freshness SLAs enforced per feed class (real-time / near-real-time / batch / daily / weekly). SLA: freshness compliance above 99% per feed class; violations escalated within named thresholds per class.
Continuous Cross-System Consistency Monitoring
Continuous consistency monitoring for entities appearing in multiple systems (customer / product / transaction). SLA: consistency anomalies surfaced within 4 business hours of occurrence.
Executive Scorecard & Quarterly Trust Score Review
Monthly executive scorecard covering trust score per domain, reconciliation completeness, freshness SLA compliance, consistency anomaly trend, and data consumer satisfaction — with named target trajectories per KPI. Delivered with direct monthly review with data leadership and executive sponsor. Integrated with quarterly trust score review where trust trajectory is measured against target with named improvement priorities per quarter. The board-defensible data trust reporting cadence that answers 'can we trust the number on the dashboard?' with specific evidence — and the delivery vehicle that turns 'we ran a data quality assessment last year' into sustained operational reality.
Six outcome metrics,
measured baseline to steady state.
Success is not "the subscription is running." It is measured against six specific outcomes captured at onboarding baseline (M 01) and re-measured monthly with target trajectory through steady state (M 04+).
Honest scoping.
C3 is a fit when specific conditions are met. It is not a fit when other conditions are — and "the platform is right; the retainer's job is discipline not procurement" is a legitimate finding we surface early rather than manufactured up to sell platform additions.
Signs off operating model, SLA commitments, and monthly scorecard reviews. Typically 20-30% time commitment monthly through the retainer with lower steady-state investment after baseline is established.
C3 operates data trust on the platform you have; it does not build the platform. Where platform implementation is incomplete or genuinely absent, [[B4|B4 Data Platform Foundation Sprint™]] delivers the platform foundation before C3 begins.
C3 operates against defined data domains and consumption patterns. Where domain structure is undefined or in-flight redesign, [[A6|A6 Data Trust Sprint™]] baselines the domain landscape before C3 defines the operating scope.
The operating cadence needs time to establish. Shorter commitments produce onboarding costs without steady-state value. Board or executive sponsor commitment to 12-month minimum is a hard prerequisite.
Data quality operations requires named quality owners per domain. Where ownership is centrally-collapsed to a single data team without domain distribution, C3 onboarding includes ownership definition — but sustained operation requires distributed ownership.
That's B4 Data Platform Foundation Sprint™ — fixed-scope build for data platform establishment. C3 operates on the platform you have; B4 builds it.
That's A6 Data Trust Sprint™ — 3-week data trust posture baseline. A6 baselines what needs closing; C3 operates what's in place.
That's C4 DataOpsCommand™ — managed data pipeline reliability retainer. Adjacent domain, different scope. C3 operates data trust (quality/reconciliation/freshness/consistency); C4 operates pipeline reliability (health/incidents/MTTR). Often run in parallel.
That's a separate advisory engagement — governance framework establishment, stewardship model design, or Chief Data Officer support. C3 operates data trust discipline against the governance framework you have; it does not establish the governance framework itself.
Managed retainer.
Monthly cadence. No surprises.
Every Run engagement is scoped as a 12-month minimum subscription with monthly delivery cadence. Retainer structure agreed at kickoff. Scope amendments negotiated through the Practice Lead, not surfaced as invoice surprises.
The five questions data leaders actually ask.
Q_01How is this different from a data quality platform vendor's professional services?
Data quality platform vendors typically deliver implementation services against their own platform — configuration, rules setup, initial deployment.
C3 is the opposite pattern: managed data trust operations discipline against whatever platform you have, with no platform swap and no reseller relationship.
The subscription runs quality SLAs, reconciliation cadence, freshness governance, and consistency monitoring against your existing data platform. Where you already have a data quality platform vendor delivering platform-level features, C3 layers above it to provide operational governance, named ownership discipline, and executive review cadence that platform-level features typically don't cover.
Q_02What KPIs does the subscription actually track?
Q_03How does C3 interact with C4 DataOpsCommand for data-heavy organisations?
Q_04Does C3 require domain-level data ownership across the organisation?
Q_05What comes after C3 or in parallel?
One name.
Six accountabilities.
Specialist consulting means the person who onboards the retainer is the person who owns the cadence — with escalation to CEO on any material issue within 24 hours.
Practice Lead — Cloud/Edge
Named account owner for the duration of the retainer. Present at every monthly review, every quarterly release gate, every difficult conversation. Available for escalation on operational issues within 24 hours.
Including scope amendments and renewal negotiation.
Signs off the monthly performance review and quarterly release.
With executive sponsor.
Authorised to negotiate.
CEO within 24 hours.
Named commitment to SLA thresholds.
What runs before,
beside, and with C3.
Data Trust Sprint™
Prior Assess engagement that baselines data trust posture with reconciliation discipline recommendations. Sequence: A6 (baseline) → C3 (operate) for the full cycle. A6 identifies what needs closing; C3 operates what's in place. Some organisations run A6 annually alongside C3 for re-baselining.
Data Platform Foundation Sprint™
Prior Build engagement that delivers data platform foundation (one domain end-to-end, then scale from evidence). C3 operates data trust on the platform; B4 builds the platform. Sequence: B4 → C3 when platform needs implementation first; C3 directly when platform is in place and operational discipline is the gap.
DataOpsCommand™
Peer Run retainer for data pipeline reliability operations. Adjacent domain (pipeline reliability vs data trust), same operating model discipline. Organisations often run C3 and C4 in parallel — C3 for the data trust question ('can we rely on the data?'), C4 for the pipeline reliability question ('can we rely on the pipelines delivering the data?').
30 minutes.
One data trust question.
Bring the specific data trust question blocking your board conversation — trust score degrading between quality campaigns, reconciliation nobody noticed had broken surfacing at the wrong meeting, freshness SLAs undefined or aspirational, BI users routing around trust issues with manual workarounds, or platform investment complete but trust operations discipline uncertain. C3 is scoped in the clinic — data landscape, ownership structure, sponsor, commitment appetite, prerequisites. If C3 is not the fit (platform needed first, or one-time data trust assessment is the actual need), the clinic surfaces the honest alternative.
- —Data landscape check
- —Domain ownership structure check
- —Trust degradation pattern check
- —Fit assessment against A6, B4, C4