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Catalogue › Data & Artificial Intelligence › Data Sourcing and Cleaning as a Service
Data & Artificial Intelligence
Data Sourcing and Cleaning as a Service
The data groundwork every model and dashboard depends on, run as a managed service: locating and acquiring the right data, lawful-basis checks, de-duplication, standardisation, labelling coordination and quality measurement - delivered as documented, model-ready datasets.
- Model-ready data with documented lineage
- Duplicates, gaps and inconsistencies driven down measurably
- Lawful sourcing the compliance team can verify
- Datasets a new team member can understand from the docs
- Source identification and acquisition
- Lawful-basis and licensing checks
- De-duplication and entity resolution
- Standardisation and schema mapping
- Labelling pipeline coordination
- Data-quality scoring and documentation
- Source inventory with lawful-basis record
- Cleaned, standardised datasets with lineage
- Data-quality scorecards before and after
- Labelling guidelines and labelled sets where scoped
- Refresh procedures and handover documentation
- One-time sourcing and clean-up
- Recurring data-quality service
- Labelling coordination
- Migration-linked cleansing
- Source count and access complexity
- Volume and duplication rate
- Labelling scope
- Compliance and licensing checks
- Refresh cadence
Content on this page comes from the governed ARRIX catalogue record DAI-12; pricing is confirmed only through a reviewed quotation.
What This Is
Data Quality & Trust Engineering
Data Quality & Trust Engineering turns inconsistent, incomplete, duplicated and unreliable enterprise data into validated data that decisions, automation and AI can be built on.
The problem this answers
Two reports disagree and nobody can say which is right. The same customer appears four times under slightly different names. A field that everyone assumed was mandatory is empty in a third of records. Automation fails on data it was never told to expect, and the fix is somebody correcting rows by hand, again.
Decisions rest on figures that survive scrutiny
Validated, reconciled data means the number in the report can be traced and defended.
Automation stops breaking on its own inputs
Standardised, validated fields mean downstream processes meet the shape they expect.
AI answers improve without touching the model
Model and retrieval accuracy are bounded by input quality; raising the input raises the ceiling.
Manual correction stops being a standing cost
Rules catch at entry and in pipeline what people currently repair by hand after the fact.
Quality stays fixed
Monitoring detects drift, so a cleansing exercise does not decay back to where it started.
Why It Matters
Where this changes the outcome
Quality is the constraint that quietly limits everything above it. A forecast, an automated decision or an AI answer inherits the correctness of its input, and the cost of bad data is paid downstream - in wrong decisions, failed automation and lost confidence - long after the point where it was cheap to fix.
| Organisation | Need | What ARRIX does | Outcome |
|---|---|---|---|
| A finance function | Figures that reconcile and can be defended | Validation, reconciliation and quality monitoring | Reporting disputes fall because the data has a traceable, checked lineage. |
| A customer-facing business | One record per customer | Matching, deduplication and standardisation | Communication, billing and service stop contradicting each other. |
| A team automating a process | Inputs the automation can rely on | Data contracts and validation at the boundary | Exceptions become rare and explicit rather than routine and manual. |
| An organisation preparing for AI | Training and retrieval data that is actually correct | Profiling, cleansing and quality gates before ingestion | Model and retrieval quality is not capped by input error. |
| A regulated institution | Evidence that submitted data is accurate | Rule-based validation with recorded results | The quality position is documented rather than asserted. |
How It Works
The path, stage by stage
- RAW DATA
- PROFILE
- CLEAN
- STANDARDISE
- VALIDATE
- MONITOR
- TRUSTED DATA
Raw data
Take the data as it is, not as it is believed to be.
Working sample and full profile
Profile
Measure completeness, validity, uniqueness, consistency and timeliness.
Measured quality baseline
Clean
Resolve duplicates, repair what can be repaired, quarantine what cannot.
Cleansed dataset and exception set
Standardise
Align formats, units, codes and reference values.
Standardised dataset
Validate
Apply rules and contracts at the boundary so new data is checked on arrival.
Validation gates
Monitor
Track quality metrics over time and alert on drift.
Quality monitoring
Trusted data
Data with a measured, maintained and evidenced quality position.
Trusted dataset
Detail
For whoever has to sign it off
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What ARRIX delivers
- Data profiling and measured quality baseline
- Duplicate detection and record matching
- Cleansing, standardisation and format normalisation
- Reference data alignment and enrichment where a trustworthy source exists
- Validation rules and data contracts at pipeline boundaries
- Anomaly detection over distributions and volumes
- Reconciliation between systems
- Ongoing quality monitoring with alerting and reporting
What you receive
- Quality baseline report per dataset and critical field
- Cleansed and standardised datasets
- Duplicate resolution report
- Validation rule set and data contracts
- Quality monitoring with dashboards and alerting
- Remediation runbook
Technical benefits
- Profiling that measures completeness, validity, uniqueness, consistency and timeliness rather than asserting them
- Deterministic and probabilistic matching for duplicates that exact comparison misses
- Standardisation of formats, units, codes and reference values
- Validation rules expressed as testable data contracts, not as tribal knowledge
- Anomaly detection over distributions, so a silent shift is noticed
- Quality metrics tracked over time and attached to the datasets they describe
Governance and security
- Corrections are auditable - what changed, when, on what rule, and by whose authority
- Original values retained so a correction can be reversed
- Deduplication rules approved by the data owner, because a wrong merge is harder to undo than a missed one
- Personal data handled under the applicable privacy obligations during cleansing and enrichment
- Enrichment only from sources whose licensing and lawful basis are established
Technology options
| Area | Candidates | How it is chosen |
|---|---|---|
| Profiling and quality | Open-source data quality frameworks, Platform-native quality features, Commercial data quality tooling | Selected to the estate's scale and the team's ability to operate it; a rule framework in version control often beats a tool nobody opens. |
| Matching | Deterministic rules, Probabilistic and fuzzy matching, Reference-data alignment | Exact matching alone misses most real-world duplicates; the threshold is a business decision because it trades false merges against missed ones. |
| Monitoring | Metric tracking in the data platform, Alerting into existing operational channels | Quality monitoring is only useful if the alert reaches someone who can act. |
When you may not need this
Where data is captured once, by a controlled process, into a single system with enforced validation, quality may already be adequate and the profiling will say so. The case strengthens wherever data is entered by many people, arrives from outside the organisation, or has been merged from systems that used different conventions.
Common Questions
Answered plainly
Can you just clean our data once?
It can be done, and for a migration or a one-off analysis that is sometimes the right scope. But data quality decays: new records arrive with the same defects the old ones had. Without validation at the boundary and monitoring afterwards, a cleansing exercise buys a temporary improvement rather than a fixed problem.
How do you decide which duplicate is the correct record?
Rules that the data owner approves - most recent activity, most complete record, the system designated as authoritative for that field, or a combination. It is deliberately a business decision rather than a technical one, because merging two records that were genuinely different is much harder to undo than failing to merge two that were the same.
Will this fix our reporting disagreements?
It fixes the part caused by the underlying data. Where two reports disagree because they define the metric differently - one counting orders, the other counting invoices - that is a semantic problem, and the answer is a defined metric layer rather than cleansing. Profiling usually reveals which of the two you have, and it is often both.
Does AI need clean data, or can it cope?
Models tolerate noise but they inherit bias and error, and a retrieval system will confidently return whatever the underlying record says. Duplicate and contradictory records are particularly damaging because the system has no basis for choosing between them. Quality raises the ceiling on accuracy in a way that prompt or model changes cannot.
Should we fix the source system instead?
Wherever you can, yes - correcting at entry is cheaper than correcting downstream forever. That is often a validation change in the source application, and it may be outside what its licensing or vendor arrangement permits. In practice most estates need both: enforcement at source where it is possible, and cleansing plus validation for everything already recorded and everything arriving from outside.
Next Step
What ARRIX needs to know
These are the questions a scoped proposal answers. Bring what you can; the rest is established in the conversation.
- Which datasets and which fields matter most to the decisions you are making?
- What goes wrong today because the data is wrong - and what does it cost?
- Do you know your current quality level, or is it an impression?
- Where does the data originate, and can it be corrected at source?
- Are there duplicate records, and is there an agreed rule for which one wins?
- Is there a reference or master list that should be authoritative?
- Who owns each dataset and can approve a correction?
- Is this a one-off cleanup or does quality need to be maintained?