DataConsultant helps data, finance, technology, operations, risk, and programme teams confirm that migrated records remain complete, accurate, balanced, traceable, and usable. We design and execute source-to-target controls, investigate exceptions, support remediation, and prepare evidence for business acceptance, operational handover, and proportionate assurance.
Data reconciliation after migration is the structured comparison of source records, transformation outputs, and target-system data to determine whether information moved completely, accurately, consistently, and according to approved business rules. It typically supports migration sponsors, data owners, finance controllers, technology teams, risk functions, and operational users. Core outputs include a control matrix, reconciliation results, exception register, remediation evidence, sign-off pack, and residual-risk record. Delivery combines automated testing with business review; its reliability depends on approved mappings, accessible source evidence, stable environments, agreed tolerances, and accountable acceptance decisions.
The scope can cover one critical dataset, a migration wave, or an enterprise programme. Responsibilities and acceptance criteria are agreed before execution.
Review migration scope, business criticality, mapping specifications, source evidence, target design, cut-off rules, tolerances, and approval requirements.
Run record, value, balance, relationship, transformation, completeness, and usability checks across agreed migration cycles.
Support root-cause analysis, remediation verification, residual-risk decisions, business sign-off, runbook creation, and transition to operations.
Align critical data, tolerance rules, control evidence, and acceptance responsibilities before migration risk becomes an operational issue.
The service is intended to improve decision confidence and defect visibility without overstating what testing can prove.
Decision-makers receive traceable evidence, known exceptions, tolerances, and residual risks rather than relying on informal assurances.
Repeatable controls can identify missing, duplicated, transformed, truncated, or misclassified data before issues spread into operations.
Balances, totals, transactions, and key accounting attributes can be compared using agreed rules and materiality thresholds.
Controls, evidence, exceptions, remediation, retests, approvals, and limitations are connected in a reviewable record.
Critical data issues can be prioritised before they affect customer service, reporting, fulfilment, compliance, or downstream processing.
Queries, rules, dashboards, runbooks, and ownership can be transferred into ongoing data-quality or operational-control processes.
Post-migration defects often arise from a combination of source quality, mapping ambiguity, transformation logic, execution failures, and operational interpretation.
Impact: A technically complete load may still contain incorrect statuses, dates, categories, balances, or derived values.
Response: Add rule-level and business-semantic checks beyond simple row counts, with accountable owners for interpretation.
Impact: Customers, suppliers, products, transactions, or documents may be unavailable or counted more than once.
Response: Test completeness, uniqueness, key stability, rejected records, late-arriving data, and duplicate-generation logic.
Impact: Reporting, billing, inventory, ledger, or settlement processes may start from unreliable opening positions.
Response: Reconcile control totals, balances, currencies, periods, rounding, allocations, and materiality thresholds.
Impact: Records may exist but fail to connect to parents, references, documents, transactions, or master-data entities.
Response: Validate referential integrity, crosswalks, orphan records, hierarchy preservation, and dependency sequencing.
Impact: Teams cannot distinguish blockers, acceptable variances, deferred remediation, or residual risk.
Response: Establish classification, ownership, severity, evidence, retest, approval, and escalation workflows.
Impact: Leaders may be unable to show what was tested, who approved it, or which limitations remained.
Response: Maintain versioned control definitions, execution logs, exception records, approvals, and a final evidence index.
Build proportionate controls around the data, processes, and business outcomes that matter most.
Suitable for organisations completing system replacement, consolidation, cloud migration, modernisation, merger integration, ERP or CRM change, data-platform migration, or regulated data movement.
Validate opening balances, journals, payables, receivables, tax attributes, currencies, and period controls before finance acceptance.
Compare customer identities, consent flags, contact details, segmentation, ownership, relationships, and activity history.
Reconcile raw, transformed, curated, and reporting layers while testing lineage, aggregations, partitions, and downstream outputs.
Confirm that customer, supplier, product, employee, and transaction data from multiple entities is mapped and consolidated consistently.
Validate record completeness, identifiers, classifications, retention attributes, access flags, and traceability under stricter evidence requirements.
Reconcile products, prices, inventory, customers, orders, returns, promotions, and fulfilment statuses before trading cutover.
Define datasets, migration boundaries, critical data elements, source-of-record decisions, control objectives, tolerances, materiality, execution points, owners, and evidence requirements. Inputs include inventories, mappings, designs, risk registers, policies, and business-process dependencies. Outputs include the reconciliation strategy, control matrix, RACI, and acceptance model.
Design SQL, scripts, platform-native checks, hashes, aggregates, joins, referential tests, duplicate tests, data-profiling routines, and dashboards. Technology involvement can span databases, warehouses, lakes, ETL/ELT tools, ERP or CRM platforms, cloud services, and test-management tools. Access, performance, masking, and environment constraints are documented.
Translate mapping logic and business policy into checks for statuses, classifications, dates, derived values, balances, eligibility, consent, hierarchy, and operational usability. Business owners remain responsible for confirming meaning, acceptable tolerances, and the consequences of unresolved variances.
Classify findings by severity and cause, assign ownership, preserve evidence, support root-cause analysis, retest fixes, track waivers, and record residual risk. This does not replace software engineering, statutory audit, legal advice, or specialist cybersecurity services unless separately scoped.
Final deliverables are selected according to migration risk, programme stage, system landscape, and retained client responsibilities.
| Deliverable | What it includes | Format | Delivery stage | Client input required | Primary owner |
|---|---|---|---|---|---|
| Reconciliation strategy | Scope, objectives, principles, tolerances, evidence, roles, and limitations | Document and decision log | Planning | Risk appetite and acceptance needs | Migration assurance lead |
| Critical-data inventory | Entities, fields, balances, relationships, and business processes in scope | Controlled register | Discovery | Data owners and system SMEs | Data owner |
| Control matrix | Control logic, source, target, frequency, threshold, owner, and evidence | Matrix or test repository | Design | Mappings and transformation rules | Reconciliation lead |
| Automated check suite | Queries, scripts, platform jobs, parameters, and execution instructions | Version-controlled code | Build and test | Environment and data access | Data engineer |
| Exception register | Findings, severity, cause, ownership, remediation, retest, and decision | Register or workflow tool | Execution | Defect triage participation | Programme team |
| Evidence and sign-off pack | Results, approvals, unresolved issues, limitations, and residual risks | Dashboard and approval pack | Cutover and acceptance | Named approvers | Business owner |
| Operational runbook | Recurring controls, schedules, access, escalation, retention, and support | Runbook and handover | Transition | Operating-model decisions | Operations owner |
Connect every critical control to execution results, exceptions, remediation, approvals, and stated limitations.
Objective: Identify critical data, stakeholders, risks, tolerances, and decision rights.
Output: Agreed scope and acceptance charter.
Objective: Understand source structures, target design, transformations, cut-offs, and known limitations.
Output: Evidence assessment and control requirements.
Objective: Define completeness, accuracy, integrity, balance, and business-rule checks.
Output: Control matrix and test specifications.
Objective: Implement repeatable queries, scripts, dashboards, and review procedures.
Output: Tested reconciliation suite.
Objective: Run controls, classify exceptions, identify causes, and support prioritisation.
Output: Results and governed exception register.
Objective: Verify remediation, record residual risk, obtain approvals, and transition controls.
Output: Sign-off pack and operational runbook.
The approach is vendor-neutral and adapted to the systems already used by the organisation.
Applicability depends on sector, jurisdiction, contracts, internal policy, and authorised legal, risk, audit, privacy, or security interpretation.
Avoid generic test packs that ignore platform behaviour, transformation logic, business materiality, or evidence requirements.
| Model | Best suited to | Typical scope | Commercial basis | Client accountability |
|---|---|---|---|---|
| Focused assessment | Early planning or a high-risk dataset | Readiness, control gaps, scope, and recommendations | Fixed scope | Provide evidence and decide next steps |
| Project delivery | Defined migration wave or cutover | Design, build, execute, investigate, and evidence | Milestone or time-based | Own migration fixes and acceptance |
| Dedicated specialists | Programmes needing added capacity | Reconciliation leads, analysts, engineers, or QA support | Time and materials | Direct priorities and integrate the team |
| Managed reconciliation | Repeated waves or post-go-live monitoring | Scheduled runs, triage, reporting, and control maintenance | Recurring service | Retain data ownership and risk decisions |
| Independent assurance | Boards, risk teams, or programme governance | Challenge of strategy, coverage, evidence, and residual risk | Fixed or periodic review | Provide independence boundaries and approvals |
The following examples are neutral illustrations, not client results or guaranteed coverage.
Control: Compare active source customers with target customers after applying approved exclusions and merge rules.
Exception: Missing target records, unexpected duplicates, or changed consent status.
Decision: Remediate, accept under tolerance, or record residual risk.
Control: Compare source closing balances with target opening balances by entity, account, currency, and period.
Exception: Rounding, mapping, timing, or omitted journal differences.
Decision: Correct mapping, post adjustment, or obtain controller approval.
Control: Validate order counts, values, statuses, line relationships, returns, and fulfilment events.
Exception: Orphan lines, changed status, missing return, or duplicated order.
Decision: Reload, repair, exclude under rule, or preserve in archive.
Measures should be baselined, segmented by criticality, and interpreted with known coverage limitations.
| Outcome area | Possible KPI | Interpretation | Important caution |
|---|---|---|---|
| Completeness | Critical records reconciled | Coverage of in-scope business-critical records | Counts alone do not prove correctness |
| Accuracy | Value or balance variance | Difference against approved source and transformation rules | Tolerances must reflect materiality |
| Integrity | Duplicate, orphan, or broken-link rate | Quality of entity and relationship preservation | Some target models intentionally change relationships |
| Defect management | Critical exception closure rate | Progress resolving migration blockers | Closure quality requires retest evidence |
| Acceptance | Controls approved by accountable owners | Decision readiness across business domains | Approval does not remove residual risk |
| Operational transition | Recurring controls handed over | Sustainability after project completion | Ownership and capacity must remain funded |
A written estimate should follow review of the migration scope, systems, mappings, data risks, control expectations, and programme schedule.
Number of entities, fields, records, historical periods, countries, and migration waves.
Mappings, consolidations, splits, derivations, currencies, hierarchies, and business rules.
Counts versus field-level tests, balance controls, sampling, regulatory evidence, and independent review.
Tooling, access, masking, environment stability, performance, vendor dependencies, and onsite needs.
Exception volume, root-cause complexity, remediation cycles, retesting, and approval effort.
Number of owners, business units, jurisdictions, review forums, and decision cycles.
Whether checks are one-time, reusable across waves, or transferred into managed operations.
Assessment, fixed project, dedicated team, independent assurance, or recurring managed service.
Share the system landscape, data domains, migration stage, key controls, and target acceptance date for a practical estimate.
The service combines data engineering, data quality, governance, assurance, and business acceptance perspectives.
Checks are designed around business meaning as well as physical data movement, with explicit dependencies and limitations.
Evidence to review: sample control matrix and anonymised deliverable structure.
Scope, rules, versions, executions, exceptions, decisions, and approvals can be maintained as connected evidence.
Evidence to review: quality-assurance and documentation approach.
Engagements can range from focused challenge to end-to-end reconciliation engineering and managed operations.
Evidence to review: role profiles, availability, and current service terms.
Identify the right mix of control design, engineering, business validation, and evidence support.
Controls are proportionate to data classification, contractual obligations, jurisdictions, system access, and client policy. DataConsultant supports compliance enablement but does not guarantee compliance, certification, security, or regulatory acceptance.
Least privilege, role-based access, multi-factor authentication, segregated duties, approved service accounts, and timely access removal.
Use limited fields, masked data, controlled samples, approved extracts, and purpose-bound access where full production data is unnecessary.
Encrypted transfer, controlled workspaces, credential protection, version control, retention rules, and secure deletion processes.
Peer review, test-data controls, repeatable execution, change control, evidence retention, and reconciliation of the reconciliation process itself.
Trace control logic to mappings, sources, runs, exceptions, fixes, retests, approvals, and residual-risk decisions.
Escalation routes, backup staffing, execution recovery, evidence preservation, third-party coordination, and business-continuity considerations.
Reconciliation normally spans source-system owners, migration engineers, platform vendors, data-quality teams, business owners, finance, operations, security, privacy, risk, audit, and programme governance.
Extract logic, cut-off, historical availability, source quality, and evidence integrity.
Mappings, transformation jobs, rejects, run identifiers, logs, and rerun behaviour.
Load rules, target constraints, derived values, application behaviour, and downstream use.
Scheduling, triage, issue ownership, reporting, access, retention, and continuous improvement.
Representative feedback is presented below to illustrate the delivery qualities organisations commonly value in this type of engagement; it is not presented as independently verified performance evidence.
“The reconciliation approach gave finance and technology one shared view of the migration. Control totals, mapping assumptions, exceptions, and acceptance decisions were documented clearly, which helped us focus the cutover discussion on material issues rather than competing spreadsheets.”
“Stakeholder workshops were practical and well managed. The team helped data owners agree critical fields, tolerances, and decision rights before testing began. That preparation reduced ambiguity when exceptions appeared and gave the programme board a clearer basis for prioritising remediation.”
“We needed more than row counts. The controls covered relationships, transformation rules, duplicate risks, and business semantics, while still separating what could be automated from what required owner judgement. The resulting control matrix became a useful reference for later migration waves.”
“The team established clear severity criteria and exception workflows rather than treating every variance the same. This helped operations understand which issues blocked go-live, which required post-launch monitoring, and which were expected consequences of the approved target design.”
“The technical checks were delivered with readable logic, parameter notes, and handover guidance. Our engineers could rerun the suite, trace failures, and adapt controls for the next wave. The knowledge transfer was as valuable as the immediate reconciliation results.”
“Communication remained precise throughout the engagement. Findings were explained without exaggeration, revisions were handled quickly, and the final evidence pack connected controls, exceptions, retests, approvals, and limitations in a form that both executives and technical reviewers could follow.”
Data reconciliation after migration is the structured comparison of source data, transformed data, and target-system data to confirm that records, values, balances, relationships, and business rules migrated as intended. It combines automated checks, exception investigation, remediation, evidence, and business acceptance.
Planning should begin before migration design is finalised so control totals, critical fields, tolerance rules, ownership, and evidence requirements are agreed. Execution usually occurs during test migrations, cutover rehearsals, production cutover, and the stabilisation period.
The service can cover customer, product, supplier, employee, finance, transaction, reference, master, historical, document metadata, and other structured datasets. Scope depends on business criticality, source accessibility, target design, privacy constraints, and available control evidence.
Checks may include record counts, control totals, hash comparisons, field-level matching, transformation-rule validation, referential-integrity checks, duplicate detection, completeness tests, balance reconciliation, and sampling. Results are logged, investigated, classified, and linked to remediation and acceptance decisions.
Yes. Reconciliation can use mapping specifications, transformation logic, canonical fields, derived measures, crosswalk tables, and tolerance rules to compare equivalent business meaning rather than identical physical structures. Unclear mappings or undocumented transformations must be resolved with accountable stakeholders.
Typical deliverables include a reconciliation strategy, scope and critical-data inventory, control matrix, executable checks or queries, exception register, defect evidence, remediation tracking, reconciliation dashboards, business sign-off pack, residual-risk record, runbook, and handover documentation.
Duration depends on dataset volume, number of systems, transformation complexity, migration waves, quality of mappings, defect levels, environment access, automation, stakeholder availability, and acceptance cycles. A reliable estimate follows discovery and review of migration artefacts.
Cost is influenced by data volume, number of entities and systems, field-level depth, criticality, migration waves, automation needs, platform access, regulatory evidence, remediation support, onsite requirements, and whether support continues through stabilisation or managed operations.
No. Reconciliation reduces uncertainty and provides documented evidence, but it cannot guarantee that every defect is found. Coverage depends on source quality, control design, mapping accuracy, test depth, tolerances, available evidence, and agreed scope. Residual risks and limitations should be recorded.
The engagement can apply least-privilege access, data minimisation, masking, secure transfer, encryption, controlled workspaces, audit logging, retention limits, access removal, and incident escalation. Legal interpretation, certification, and specialist security testing remain separate responsibilities unless explicitly commissioned.
It can produce traceable control definitions, execution results, exception records, approvals, and residual-risk documentation that may support internal assurance or regulatory review. It does not constitute a statutory audit, legal opinion, certification, or regulatory approval.
Yes. Ongoing support can include scheduled reconciliation runs, exception triage, control monitoring, issue reporting, change-impact reviews, runbook maintenance, and knowledge transfer. The retained operating model, responsibilities, service levels, and escalation process are agreed separately.
