Accelerator · Data Governance & Regulatory

Compliance & Data Lineage

Governed, traceable data before it reaches a regulator

A governed, auditable data foundation for regulatory workloads: every record validated, enriched and traced from source system to report. Built for the data behind subscriber privacy, SIM registration, number portability, regulator returns and the operator's mobile-money business.

See it on live demo data The Data Fabric Framework

What it gives you

Lineage

Source-to-report, field by field

DQ rules

Format, validation, enrichment and correlation checks

Audit

Who changed what, when, in which system

30-45

Day pilot to a production-ready capability

On the demo platform now Loading

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Lineage completeness

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Overall DQ pass rate

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Systems traced

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DQ rules running

Figures come from the demo database behind this site (synthetic data), read live from the platform APIs.

Chapter 4The proof Use case 3 of 6 Value-chain stageRegulatory, privacy and financial control

Prove every number

The pressureSubscriber registration, privacy and new AI rules all ask the same question: where did this number come from?

Why it matters to an operator executiveRegulators and auditors expect operators to trace a reported figure back to source. Built-in lineage turns that from a quarter-end fire drill into a query — and lowers the cost of every submission.

Where it sits in the telecom value chain

Network
Product & pricing
Sales & channels
Customer care
Billing, RA & fraud
Wholesale & enterprise
Regulatory

Highlighted: the stages this use case changes. See how our services map to it →

The story so far Stop fraud in the moment Fix the foundation once

The fraud and revenue-assurance use cases come together in the SENTINEL command centre →

Business lens

From a business outcome to measured value

We start from the KPI an executive owns, not from the technology — then work back to the decisions, data and agents that move it.

Our delivery methodology

1 · Outcome & KPI Regulatory confidence Report preparation time · open audit findings · DQ on regulated data
2 · Value-chain domain Regulatory Privacy, registration, financial and AI controls
3 · Decisions Fit to submit? Which control failed; who remediates
4 · Data Metadata as evidence Catalogue, pipeline lineage, DQ results, access logs, control library
5 · AI & agents Evidence on autopilot Automated lineage, DQ anomaly checks; agents keep evidence packs current
6 · Governance & adoption Owners sign off Policy as code; RBAC and immutable audit trail
7 · Measured value Defensible, faster Fewer findings and less effort; baselined in discovery

How we’d deliver it

Indicative durations · sized with you in discovery

1 2–3 wks

Discover & value case

Main deliverable

Regulatory obligation map and value case

Data & AI Strategy Data Governance & Regulatory
2 2–3 wks

Design

Main deliverable

Control and data-ownership model; critical data elements

Data Governance & Regulatory
3 30–45 days

Build & integrate

Main deliverable

Lineage and DQ on priority reports; evidence agents

Data Engineering & Platform Data Governance & Regulatory
4 2–4 wks

Deploy & adopt

Main deliverable

Data owners and compliance using evidence packs

Data Governance & Regulatory
5 Ongoing

Run & scale

Main deliverable

More reports and regulations; governance operations

Managed Services

Same five phases on every engagement; the pilot (phase 3) runs on your data with your team. How the methodology works →

The problem

Regulators ask where a number came from

Operators report to telecom, privacy and — for mobile money — financial regulators. The data behind those returns is stitched together from BSS, OSS, CRM, wallet and partner systems, often by hand. When a figure is questioned, tracing it back to source takes days, and fixing a quality issue means re-running the whole chain.

This accelerator puts compliance data on the governed Data Fabric: validated and enriched on the way in, traced at every hop, and monitored by data-quality rules with named owners — so reports are reproducible and questions can be answered in minutes.

Subscriber privacyConsent, data-subject requests and breach notification under data-privacy law.
SIM registration & KYCComplete, verified subscriber identity records.
Number portability & lawful requestsAccurate, time-stamped records for porting and lawful-intercept processes.
Regulator returnsQuality-of-service, subscriber and revenue returns that reconcile to source.
Mobile-money reportingAML screening and suspicious-transaction reporting for the wallet business.

How it works

Six stages, every one traced

The same stages you see in the live lineage data below.

01
Ingestion
Records arrive from source systems in their native formats.
02
Normalisation
Formats, codes and identifiers are standardised.
03
Validation
Field-level data-quality rules pass, warn or fail each record.
04
Enrichment
Reference and subscriber context is added and recorded.
05
Compliance check
Regulatory rules and screening run on the enriched record.
06
Output
Report-ready data leaves with its full lineage attached.

Screens · live demo data

Lineage and data quality on the demo platform

Each panel below is drawn from the platform APIs on this site. The lineage sample covers the payment and mobile-money flows in the demo dataset.

Some demo data could not be loaded just now. The panels that loaded are shown; refresh to try again.

Total lineage records

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Fields validated

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Fields enriched

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Lineage completeness

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Lineage by processing stage

Average processing time by stage (ms)

Audit trail by source system

Record formats in the lineage log

Data-quality pass rate by rule type

Data-quality rules

Lowest pass rate first

Rule	Type	Source	Processed	Failed	Pass rate

Agent squad · supervised digital workforce

Agents keep the evidence in order

Agents watch quality, trace lineage and draft the paperwork; data owners and compliance officers approve what goes to a regulator.

Data Quality Agent

Rule monitor

Runs validation rules, spots failing sources and opens issues with the data owner.

Lineage Agent

Tracer

Answers "where did this figure come from?" with the full path from source field to report cell.

Report Preparer

Regulatory returns

Assembles returns and the evidence pack; a compliance officer signs off before submission.

Human sign-off required

Part of the DaasLabs supervised digital workforce: autonomy levels, named owners and a full audit trail. See the digital workforce →

Who uses it

Owners of regulated data

Chief Data Officer

Data governance

Catalogue, ownership and lineage in one place
DQ scorecards by domain
Policy enforcement across systems

Regulatory Affairs & Compliance

Returns & audits

Reproducible regulator returns
Evidence packs on demand
Faster answers to regulator queries

Data Protection Officer

Privacy

Where personal data lives and flows
Consent and access-request support
Breach impact assessment

Demo brief

What the demo data is telling us

A short reading of the panels above, written the way we would brief an operator executive after a first look at their data.

Illustrative demo briefWritten from this page’s synthetic demo data · not client results

What the demo data shows

Lineage is captured at every pipeline stage — ingestion, enrichment, normalisation, compliance check and output — and the few runs that fail at each stage can be traced to their source. Data-quality rules pass at high rates overall; the weakest are the correlation and enrichment rules that join records across systems.

Where we would act first

Tag the critical data elements behind regulatory returns and subscriber-registration reporting.
Attach data-quality rules and a named owner to each element.
Generate an evidence pack per submission: lineage, rule results and sign-offs.

What a pilot would prove

One priority report traced end to end, with owner sign-off and an evidence pack you could hand to an auditor.

Value

What changes for the business

Answers in minutes

Any reported figure can be traced to its source records and the rules applied on the way, without a reconciliation project.

Quality fixed at source

Failing rules point to the system and owner responsible, so issues are fixed once instead of patched in every report.

A foundation for AI

The same governed, traced data feeds revenue assurance, fraud and customer models — and the agents that use them.

Getting started

From pilot to production

Most operators start with one regulatory return and the data domains behind it.

Phase 1

Discovery

Pick the return, map critical data elements, owners and today's controls.

1-2 weeks

Phase 2

Pilot

Connect sources, switch on lineage and DQ rules, and reproduce the return with full traceability.

30-45 days

Phase 3

Scale

Add returns and domains; extend the catalogue and ownership model across BSS and OSS.

Sprints

Phase 4

Run

DataOps and governance operations under agreed SLAs through Managed Services.

Managed service

Start with your hardest return

Tell us which report takes longest to defend. We'll propose a 30-45 day pilot to make it traceable.

Talk to us About the service