Energy & Utilities · Regulatory Reporting Data

Build Regulatory Reporting Data Your Energy & Utilities Teams Can Trace and Defend

DataConsultant helps energy and utilities organisations connect confirmed reporting requirements to governed source data, controlled transformations, quality rules, reconciliations, lineage, approvals and retained evidence—so reporting teams can operate a repeatable source-to-submission capability instead of reconstructing the answer every cycle.

✓Obligation-to-data traceability

✓Data quality and reconciliations

✓Source-to-submission lineage

✓Approvals, evidence and control monitoring

Request a Regulatory Reporting Data Consultation → Explore Service Scope

Scope depends on the regulated activity, jurisdictions, reporting calendar, data domains, source systems, control environment and authorised interpretation of applicable requirements.

Regulatory Reporting Data Control MapSource-to-submission view

Confirmed RequirementField, measure, frequency, entity and due date

Source DataMeter, grid, generation, billing, market, finance

Mapping & LogicDefinitions, reference data, calculations, adjustments

Quality ChecksCompleteness, validity, timeliness, consistency

ReconciliationControl totals, tolerances, cross-system agreement

SubmissionReview, approval, format, period and sign-off

Evidence & MonitoringLineage, exceptions, approvals, change and retention

Critical data ownershipNamed business and data accountability

Lineage coverageSource, transformation and report-field traceability

Exception statusOpen, investigated, approved and remediated issues

Evidence readinessRetained support for review and assurance

Regulatory + business contextMap only confirmed obligations and authorised interpretations into the data design.

Energy-specific data domainsMeter, generation, grid, market, billing, asset, customer, finance and reference data where relevant.

Evidence-led controlsData rules, reconciliations, lineage, exceptions, approvals and retained evidence.

Sustainable operating capabilityRoles, change control, monitoring, issue management, runbooks and knowledge transfer.

Why Regulatory Reporting Data Becomes a Control Problem in Energy & Utilities

A reporting template is only the last mile. Confidence depends on what happened upstream: which operational and commercial systems produced the data, how values were mapped and transformed, which exceptions were accepted, who approved changes and whether the result can be reconstructed after submission.

Where reporting-data risk enters

Typical failure points to investigate before redesigning the process.

Regulatory
Submission

Fragmented meter, market or operational sources

Conflicting definitions and reference data

Manual spreadsheet adjustments outside governed workflows

Late-arriving data and unmanaged period cut-offs

Unversioned mappings, calculations and transformation logic

Weak control totals, tolerances and reconciliations

Unclear source-to-report lineage and ownership

Approval, exception and evidence gaps

Current State → Target State

Common current state

Report logic lives in individual workbooks or scripts.
Teams disagree on source precedence or definitions.
Quality checks are reactive and performed near deadline.
Reconciliations lack documented tolerances and ownership.
Lineage stops before the submission field.
Exceptions and overrides are difficult to reconstruct.
Change impact is unclear when systems or formats change.

Controlled target state

Confirmed requirements map to owned data elements and rules.
Source precedence and transformations are versioned.
Quality checks run at defined control points.
Reconciliations have owners, tolerances and evidence.
Lineage connects source to report field.
Exceptions follow approved workflows with decisions retained.
Change control identifies affected reports and controls.

Assess Where Reporting-Data Risk Enters Your Submission Process

Share the reporting scope, source landscape, recurring exceptions and control concerns. We can help define a focused evidence-led assessment.

Request a Reporting Data Assessment →

Where Regulatory Reporting Data Sits in the Energy & Utilities Value Chain

Reporting values can traverse operational, metering, commercial and financial processes before they appear in a return. The service follows that path to identify data producers, consumers, transformations, controls and accountable decisions.

Generation & Supply

Output, capacity, fuel, dispatch, availability and operating records.

What operational data is authoritative?

Network & Assets

Transmission, distribution, grid, asset and outage information.

Which network state applies to the period?

Metering & Market

Meter reads, intervals, schedules, market and settlement records.

Which validated quantities feed calculations?

Billing & Customer

Tariffs, accounts, consumption, invoices and adjustments.

How are commercial rules applied?

Finance & Reconciliation

Ledger, revenue, cost, accrual, settlement and management data.

What must reconcile before sign-off?

Regulatory Reporting

Formats, measures, approvals, submissions, evidence and follow-up.

Is the submission traceable and supported?

Generation & capacityPlant output, availability, fuel and operating status

Network & gridFlow, outage, constraint and system records

Reporting Data ProductControlled measures, dimensions, definitions and evidence

Meter & intervalReads, intervals, events, VEE and device context

Market & settlementSchedules, positions, transactions and adjustments

Customer & accountPremise, service, account and customer attributes

Billing & tariffTariffs, consumption, invoices, credits and effective dates

Asset & referenceAsset, location, hierarchy, unit, code and calendar data

FinanceLedger, revenue, cost, balance and accrual data

Metadata & evidenceLineage, rules, approvals, exceptions and control records

What the Regulatory Reporting Data Service Covers

DataConsultant focuses on the data, architecture, quality, lineage, controls and operating model behind confirmed reporting requirements. We do not replace the client’s authorised regulatory interpretation; we turn that interpretation into a practical data and control design.

Obligation-to-data inventoryMap report fields, measures, periods and entities to data needs.

Critical data elementsIdentify report-critical fields, definitions, owners and dependencies.

Source inventory & precedenceDetermine authoritative sources, cut-offs, interfaces and fallback logic.

Reporting data modelDefine measures, dimensions, identifiers and report-ready structures.

Transformations & mappingsDocument calculations, adjustments, reference mappings and versions.

Data-quality rulesDesign completeness, validity, timeliness and reasonability checks.

Reconciliations & tolerancesDefine control totals, cross-system checks and variance escalation.

Lineage & metadataConnect report output to transformations, curated data and sources.

Exception managementClassify defects, route ownership, approve overrides and remediate.

Evidence & approvalsRetain control outcomes, exceptions, sign-offs and changes.

Control monitoringTrack control performance, exception ageing and issue trends.

Operating model & transferClarify roles, forums, runbooks, training and ownership.

Regulatory Reporting Data Framework

A control chain that connects authorised requirements to data and evidence without treating the report template as the architecture.

Confirmed Reporting RequirementReport, field, measure, frequency, entity, period

→

Data RequirementDefinition, criticality, grain, dimensions, source, owner

Source & LineageSystem, interface, dataset, reference data, dependency

→

Transformation & MappingCalculations, aggregations, adjustments, effective dates

Validation & ReconciliationRules, control totals, tolerances, variance, exceptions

→

Approval & SubmissionReview, sign-off, format, evidence and confirmation

Ownership + metadata + security + change control + remediation + monitoring across the lifecycle

Map Your Reporting Requirement to the Data, Controls and Evidence Behind It

Use a focused workshop to identify the critical fields, systems, reconciliations, lineage and owners that determine whether a submission can be reproduced.

Discuss Your Reporting Scope →

Reporting Control Readiness, Data Quality and Regulatory Context

The assessment looks for evidence that each reporting-data requirement can be traced to an accountable source, tested, reconciled, approved and monitored. Findings are prioritised by reporting risk and business impact rather than by a generic maturity score.

Readiness dimension	Evidence we look for	Typical risk if weak	Treatment
Requirement definition	Approved report inventory, field definitions, entity, frequency, cut-off and owner	Different interpretations enter the same return	Evidence-led review
Source ownership	Authoritative source, precedence, owner, interface and period availability	Late or conflicting values cannot be resolved predictably	Evidence-led review
Transformation control	Versioned calculations, mappings, adjustments, effective dates and change approvals	Reported values shift without a reproducible explanation	Evidence-led review
Data quality	Critical elements, rules, thresholds, exceptions, remediation and owners	Defects surface near submission or are accepted without traceable decisions	Evidence-led review
Reconciliation	Control totals, tolerance logic, comparisons, variance investigation and sign-off	Material differences remain unresolved or repeatedly reworked	Evidence-led review
Lineage & metadata	Business and technical lineage from source through transformation to report field	Impact analysis and assurance require manual reconstruction	Evidence-led review
Evidence & approvals	Control execution, exception decisions, approvals and submission confirmation	Teams cannot demonstrate how the submitted number was produced	Evidence-led review
Change management	Impact assessment across source, mapping, rule, report and control changes	System or regulatory changes create hidden reporting breaks	Evidence-led review

From Data Element to Monitored Control

Critical Data ElementReport field or upstream value that matters

Business RuleDefinition, logic, range, relationship or cut-off

Control ObjectiveFailure the control should detect or prevent

Validation / ReconciliationTest, total, tolerance or cross-system comparison

Exception & DecisionOwner, investigation, override, approval, remediation

Monitoring & EvidenceOutcome, ageing, trend, sign-off and record

Representative Reporting-Data Scenarios

Statistics & returnsGeneration, transmission, distribution, trading, capacity, fuel or financial formats where applicableMap measures to controlled sources, rules and evidence

Grid / scheduling contextOperational, schedule, reserve, network or compliance data for entities in scopeEstablish authoritative data, period logic and reconciliation

Metering-linked returnsValidated meter, interval, device, customer or settlement data feeding regulated outputsConnect VEE, exceptions and reference data to report lineage

Tariff & finance supportBilling, revenue, cost, finance and allocation data supporting submissionsControl definitions, allocation logic, tie-outs and approvals

Regulator / audit responseEvidence requests, historical reconstruction and change explanationsRetrieve lineage, controls, approvals and issue history

Regulatory context: use authoritative sources and confirm applicability

Depending on jurisdiction, business model, regulated activity, data handled and applicable obligations, reporting requirements can differ significantly. For India-focused electricity-sector work, these official sources are examples we would review with the client’s authorised regulatory interpretation. They are not a universal checklist and do not replace legal or regulatory advice.

CEA Statistics and Returns Standards ↗

Official Central Electricity Authority source for statistics, returns and information regulations, format inventories, frequencies and target dates.

CEA Metering Regulations ↗

Official Central Electricity Authority source for current metering regulations and amendments, including the 2026 amendment listings.

CERC Current Regulations ↗

Official Central Electricity Regulatory Commission index of current regulations, including the Indian Electricity Grid Code and related procedures and amendments.

Source-to-Submission Architecture for Controlled Reporting Data

The target architecture does not require one particular vendor. It separates operational sources, integration, report-ready data, assurance controls and submission outputs so ownership, change and evidence can be managed at the right layer.

Reference Architecture

Illustrative categories only. The actual design follows the client’s systems, reporting obligations, security model, data volumes and existing platform standards.

Operational & commercial sources

AMI / MDMS / meter files

SCADA / EMS / DMS

Generation / plant systems

Market / scheduling / settlement

Billing / CIS / CRM

ERP / EAM / finance / controlled files

Integration & data control

Batch / API / file / event ingestion

Schema & completeness checks

Reference data alignment

Period / cut-off handling

Access & change controls

Observability & failure handling

Reporting data layer

Report-ready data model

Measures & dimensions

Versioned transformations

Mapping & allocation logic

Business metadata

Source-to-field lineage

Assurance & workflow

Quality rules

Reconciliations

Exception workflow

Review & approval

Evidence retention

Control monitoring

Submission & assurance outputs

Regulatory return / portal file

Supporting schedules

Management sign-off

Submission confirmation

Evidence pack

Issue / change backlog

Traceability by design

Controlled transformations

Proportionate quality rules

Least-privilege access

Reproducible evidence

Where AI Can Assist—Without Becoming the Regulatory Authority

AI can support selected operational tasks when the data, model/system risk and human oversight are appropriate. It should not invent regulatory interpretations or bypass accountable review.

Exception prioritisationRank anomalies for analyst attention using approved features and thresholds.

Metadata enrichmentSuggest descriptions or mappings for steward review.

Lineage gap detectionIdentify incomplete documentation or inconsistent dependencies.

Evidence search & draftingRetrieve approved evidence or draft narratives grounded in controlled data.

Minimum Governance Lens for AI-Assisted Work

Document intended purpose, accountable owner and prohibited use.

Assess data sensitivity, security, access rights and third-party model risk.

Evaluate output quality against defined tasks and error consequences.

Require human review for material reporting decisions and sign-off.

Retain evidence of model/system versions where material.

Monitor performance and change; restrict use when controls are insufficient.

Governance, Decision Rights and the Target Operating Model

A controlled reporting process needs more than data engineers. Reporting owners, operational teams, finance, data owners, compliance, technology and assurance functions need explicit responsibilities for definitions, rules, exceptions, changes, approvals and evidence.

Who Owns Which Decisions?

Regulatory Reporting OwnerSubmission scope, calendar, review process and accountable sign-off.

Compliance / Regulatory / LegalApplicability and authorised interpretation of obligations.

Business Data OwnerDefinitions, fitness criteria, material issues and remediation priorities.

Data StewardMetadata, quality rules, issues, lineage context and data decisions.

Operations / Market / Billing / FinanceSource processes, operational adjustments and supporting evidence.

Data Engineering & ArchitecturePipelines, models, transformations, metadata and technical controls.

Risk / Assurance / Internal AuditChallenge control design and review evidence according to mandate.

Platform / Security / ITAccess, change, resilience, security and platform controls.

Reporting Data Control Lifecycle

1. DefineConfirm obligation, report, measures, scope, period and owner.

2. MapConnect data elements to sources, reference data and lineage.

3. BuildImplement controlled transformations and report-ready data.

4. ValidateExecute quality rules, reconciliations and exception handling.

5. Approve & submitReview material variances, evidence and accountable sign-off.

6. Evidence & monitorRetain support, track controls, investigate issues and changes.

Turn Reporting Findings Into Implementable Controls and Accountable Decisions

Move from a gap list to named owners, testable rules, reconciliation logic, lineage requirements, evidence and a prioritised implementation backlog.

Discuss an Implementation Roadmap →

How DataConsultant Delivers Regulatory Reporting Data Work

The method starts with the reporting decision and evidence required, then works backward into data and forward into implementation. Activities are adapted to the client’s reporting scope, source landscape, control maturity and available documentation.

Discover & Scope

Confirm perimeter, stakeholders, pain points, systems and evidence.

Scoped assessment plan

Map Requirement to Data

Identify measures, elements, sources, owners, logic and lineage.

Obligation-to-data map

Assess & Challenge

Test quality, transformations, reconciliations and approvals.

Evidence-backed findings

Design Target Controls

Define data model, rules, lineage, governance and architecture.

Target design and backlog

Implement & Assure

Support pipelines, rules, metadata, workflow and testing.

Implemented capability where scoped

Operationalise & Transfer

Establish monitoring, runbooks, cadence, training and improvement.

Sustainable operating model

Wave 1 · Foundation

Establish the reporting-data baseline

Confirm report inventory, owners and critical elements.
Map sources, transformations and manual steps.
Baseline quality, reconciliation and lineage gaps.
Prioritise high-risk reports and recurring failures.

Wave 2 · Control rollout

Implement target data and controls

Build or revise report-ready models and mappings.
Implement quality rules, reconciliations and exception workflows.
Enable metadata, lineage, approvals and evidence capture.
Test with business, reporting and assurance stakeholders.

Wave 3 · Operate & improve

Make controls part of normal operations

Run control monitoring and issue-management cadence.
Manage regulatory, system and data-model changes.
Track recurring exceptions and remediation.
Transfer capability or transition to managed support.

Tangible Deliverables

Final outputs depend on scope; these are typical for a substantial regulatory reporting data engagement.

Reporting Data Assessment

Current-state findings, control gaps, dependencies, limitations and priorities.

Obligation-to-Data Matrix

Report fields, measures, data elements, sources, owners and dependencies.

Source & Critical Data Inventory

Authoritative sources, criticality, precedence and availability.

Reporting Data Model

Report-ready measures, dimensions, identifiers, periods and references.

Source-to-Report Lineage

Business and technical lineage through interfaces and transformations.

Rule & Reconciliation Catalogue

Quality rules, tolerances, totals, variances, exceptions and owners.

Control & Evidence Matrix

Control objectives, execution points, evidence, approvals and monitoring.

Governance & RACI

Owners, stewards, reporting roles, decisions and escalation paths.

Target Architecture & Backlog

Target design, implementation epics, dependencies and acceptance criteria.

Operating Procedures & Decision Pack

Runbooks, monitoring cadence, change process and executive decisions.

What We Need From the Client

Reporting inventory, submission calendar and in-scope legal entities.
Authorised regulatory interpretations and reporting owners.
Current templates, supporting schedules and submission evidence.
Source-system inventories, architecture and interface documentation.
Data dictionaries, mappings, calculations and reference data.
Existing quality rules, reconciliations, controls and issue logs.
Sample data or secure access appropriate to the assessment.
Access to operations, finance, compliance, data, technology and assurance stakeholders.

How DataConsultant Can Support Implementation

Data pipeline & model implementationTranslate approved source, mapping and report-ready requirements into delivery work.

Rule & reconciliation implementationConfigure checks, tolerances, totals, variances and exceptions.

Metadata & lineage enablementPopulate business metadata and connect technical lineage to reports.

Workflow & approval controlsDesign review, exception, override and sign-off workflows.

Testing & implementation assuranceValidate rule behaviour, reconciliation outcomes and acceptance evidence.

Runbooks, training & transitionPrepare operating procedures and transfer responsibilities.

DesignDefine data, controls, evidence, ownership and architecture.

MobilisePrioritise backlog, roles, dependencies and acceptance criteria.

ImplementBuild pipelines, rules, reconciliations, lineage and workflow.

OperateRun controls, triage exceptions and maintain evidence.

ImproveAddress root causes, defects, control gaps and friction.

Scale / TransferExtend to more reports or transfer capability to internal teams.

Define a Remediation and Monitoring Path Your Reporting Teams Can Operate

Sequence high-risk fixes, establish control monitoring, prepare runbooks and define whether the capability should be transferred, retained or supported as an ongoing service.

Plan the Next Assurance Step →

Engagement Model, Commercial Clarity and Buyer Fit

DataConsultant does not publish a fixed fee for this Regulatory Reporting Data service. Commercial scope is agreed after the reporting perimeter, systems, controls, stakeholders, evidence needs and implementation responsibilities are understood.

Custom Scope & Pricing

Choose the level of support that matches the decision you need to make. Timeline is confirmed after scoping; no fixed turnaround is assumed.

Focused Reporting Data AssessmentDefined report set, recurring issue, audit finding or control concern.

Target Data & Control DesignSource-to-submission model, lineage, rules, reconciliations and governance.

Implementation & Assurance SupportPipelines, controls, metadata, workflow, testing and evidence.

Ongoing Reporting Data OperationsControl monitoring, lineage maintenance, issue management and improvement.

PricingRequest a Quote. Final fee depends on scope and delivery model.

TimelineConfirmed after scoping based on reporting cycles, evidence, systems and implementation depth.

Third-party technologyCloud, software, platform and licence costs are separate unless explicitly included.

Implementation boundaryAssessment, design, implementation and managed support are distinguished in the statement of work.

Key scope factors

Number and criticality of regulatory returns
Legal entities, business units and jurisdictions
Source systems, interfaces and data volumes
Critical data elements and transformation complexity
Manual adjustments and spreadsheet dependencies
Data-quality and reconciliation maturity
Lineage and metadata depth required
Control, evidence and assurance expectations
Stakeholder workshops and review cycles
Implementation, migration, training and ongoing support

Request a Scoped Quote →

Good Fit When

Reporting teams repeatedly reconcile or rebuild numbers near deadlines.
Source-to-report lineage is incomplete or difficult to evidence.
Audit or regulator questions expose weak data-control documentation.
Metering, billing, market, finance or platform changes increase reporting risk.
Multiple units apply inconsistent definitions or manual adjustments.
You need a sustainable operating capability, not another one-off fix.

May Not Be the Right Service When

The only requirement is legal interpretation without a data/control scope.
The request is for statutory audit, certification or guaranteed compliance.
The issue is purely physical meter installation or network engineering.
No accountable owner can approve definitions, access or remediation.
Required data cannot be accessed lawfully and securely for the agreed work.

Business Outcomes From a More Controlled Reporting-Data Capability

The value is not a generic compliance promise. It is a more reproducible, accountable and operable data foundation for recurring reporting, assurance and change.

Clearer reporting accountability

Connect report fields and measures to named business owners, data owners, stewards and accountable sign-off roles.

More reproducible submissions

Retain source, logic, lineage, reconciliation, exception and approval evidence so teams can reconstruct how a reported value was produced.

Earlier control visibility

Move quality checks and reconciliations upstream so recurring meter, market, billing, asset or finance issues are surfaced before final submission activity.

Better change impact analysis

Trace system, mapping, reference-data and reporting changes to affected measures, controls, evidence and downstream submissions.

Stronger assurance evidence

Give reporting, risk and assurance teams a clearer view of control execution, exceptions, approvals, remediation and known limitations.

A sustainable operating model

Replace deadline-driven reconstruction with repeatable monitoring, issue management, runbooks, ownership and capability transfer.

Why DataConsultant for This Reporting-Data Problem

Credibility comes from making the problem inspectable: linking requirements to data, controls, architecture, ownership, implementation and operating evidence.

Business requirement → data design

Start with the reporting decision and confirmed requirement, not a generic platform feature list.

Energy-specific data context

Meter, grid, generation, market, billing, asset and finance dependencies are treated as connected inputs where relevant.

Quality + reconciliation + lineage

Controls are connected rather than handled as separate workstreams with separate evidence.

Governance by decision right

Owners, stewards, reporting teams and compliance are tied to specific approvals and exceptions.

Design through implementation

The engagement can continue into backlog, assurance, runbooks and operational support when scoped.

Requirements-led technology

Work with existing utility and enterprise platforms and change only what the target capability requires.

Regulatory reporting problems can expose a deeper meter-data, asset-governance or AI-control issue. Treat these as adjacent capability decisions when the root cause extends beyond the reporting process.

Explore the Energy & Utilities Industry Portfolio →

Frequently Asked Questions

Practical answers for energy and utilities leaders assessing regulatory reporting data scope, controls, architecture, delivery, implementation and ongoing support.

What is Regulatory Reporting Data for energy and utilities?

Regulatory Reporting Data is the governed data capability that connects confirmed reporting requirements to source systems, data definitions, transformations, validations, reconciliations, approvals, submissions and retained evidence. For energy and utilities organisations it can involve meter, network, generation, market, billing, customer, asset, finance and reference data depending on the regulated activity and reporting obligation.

What does DataConsultant include in a Regulatory Reporting Data engagement?

Scope can include an obligation-to-data inventory, source and lineage mapping, critical data element identification, reporting data models, transformation and mapping controls, data-quality rules, reconciliations, exception workflows, approval evidence, governance roles, target architecture, implementation backlog and operating procedures. Final scope is agreed after discovery.

Which energy and utility processes can be covered?

Depending on the organisation, scope can cover generation, transmission, distribution, metering, market and settlement processes, billing, tariff and revenue processes, asset and network operations, finance, environmental or operational reporting, and the regulatory submission process itself. Only processes relevant to the confirmed reporting scope should be included.

Which data domains are most relevant?

Common domains include meter and interval data, generation, network and grid data, asset data, customer and account data, tariff and billing data, market and settlement data, finance data, master and reference data, reporting metadata, lineage and control evidence. The engagement identifies which of these are critical for the specific submissions in scope.

Does DataConsultant interpret regulations or guarantee compliance?

No. DataConsultant can help translate confirmed obligations and authorised interpretations into data requirements, controls, lineage and operating processes. Applicability and legal interpretation should be confirmed by the client with its legal, compliance or regulatory specialists. The service supports reporting readiness and control design; it does not guarantee compliance or replace statutory audit or legal advice.

How are data quality and reconciliations handled?

We can define critical data elements, quality dimensions, validation rules, tolerances, control totals, cross-system reconciliations, exception categories, ownership and remediation workflows. Rules are designed around the intended report, materiality, source behaviour and approved business logic rather than using one generic quality score.

How is source-to-submission lineage documented?

Lineage can connect report fields and measures back through calculations, mappings, curated datasets, interfaces and source systems. The level of detail is set by the reporting risk, assurance need and available metadata. Business lineage and technical lineage can be combined with ownership, control points and change history.

Can you work with our existing utility platforms and data stack?

Yes. The service is requirements-led and can work with the organisation’s existing metering, network, billing, market, ERP, EAM, data platform, BI, metadata, quality and workflow technologies. DataConsultant does not assume a specific vendor stack. Platform configuration or procurement support is included only when agreed in scope.

Can AI be used in the regulatory reporting data process?

AI can sometimes assist with anomaly prioritisation, exception triage, metadata enrichment, lineage gap detection, document search or drafting narratives from approved data. AI should not be treated as an authoritative regulatory interpreter, and material outputs require appropriate evaluation, access controls, human review, change control and monitoring.

What deliverables will we receive?

Typical deliverables can include a reporting-data assessment, obligation-to-data matrix, source inventory, critical data element register, source-to-report lineage, reporting data model, rule and reconciliation catalogue, control and evidence matrix, governance and RACI model, target architecture, implementation backlog, operating procedures and executive decision pack. Deliverables are adapted to the agreed reporting scope.

Can DataConsultant implement the recommended controls and data flows?

Yes, where separately scoped. Implementation support can cover data pipelines and mappings, rule configuration, reconciliation logic, metadata and lineage enablement, workflow and approval controls, monitoring, testing, documentation, runbooks, training and implementation assurance. Responsibilities and acceptance criteria are agreed before delivery starts.

Can DataConsultant support ongoing regulatory reporting data operations?

Yes. Ongoing support can include governance operations, data-quality and reconciliation monitoring, issue and remediation management, metadata and lineage maintenance, control evidence reporting, change impact assessment, retained advisory or capability transfer. Service boundaries, responsibilities and support arrangements are defined commercially.

How long does the engagement take and how is pricing determined?

Timeline and pricing are confirmed after scoping. They depend on the number of reporting obligations, entities, jurisdictions, reporting periods, source systems, critical data elements, transformations, controls, stakeholder groups, evidence depth, implementation requirements and ongoing support needs. DataConsultant does not publish a fixed fee for this page.

What should we prepare before starting?

Useful inputs include the reporting inventory and calendar, authorised interpretations of applicable requirements, current submission templates, source-system and data documentation, mappings and calculations, existing reconciliations, quality reports, control evidence, issue logs, architecture diagrams and access to accountable reporting, compliance, operations, finance, data and technology stakeholders.

Build Reporting Evidence Your Organisation Can Reproduce and Act On

Describe the reports, systems, recurring exceptions or control concerns in scope. We will use the enquiry to understand the problem before proposing a commercial approach.

Clarify the reporting perimeter and accountable stakeholders.
Identify high-risk source, transformation, quality and reconciliation points.
Determine whether you need assessment, target design, implementation or ongoing support.
Separate regulatory interpretation from data/control implementation.
Define the evidence and deliverables required for decision-making.
Confirm timeline and commercial scope only after discovery.

For trust, privacy and data-handling information, review the DataConsultant Trust Center. Please do not send highly sensitive production data through the initial web form.

Request a Scoped Consultation

A fixed price or timeline cannot be provided without discovery. Request a scoped estimate based on your specific reporting-data needs.