Data Quality

Data Quality Begins With Defining What “Correct” Means for the Way You Use the Record

A dataset can be complete and still be unusable. Dates may follow different formats, categories may drift, identifiers may conflict and duplicate records may divide activity across several versions of the same entity.

SDES provides data quality services that compare records against approved business rules and available source evidence. We profile recurring issues, standardise values where authority is clear and report uncertain changes for an authorised decision.

The goal is not to make every cell look populated. It is to produce data that is consistent, traceable and fit for its intended process while preserving uncertainty where the evidence does not support a correction.

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Quality reviewers comparing source documents with validated structured records

5000+ Completed Projects

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Services We Offer

Accuracy, completeness, consistency and validity are different checks

Profile issue types before correction
Validate against approved rules
Standardise controlled values
Separate possible from confirmed duplicates
Retain source and correction traceability
Report unresolved records clearly

Accuracy asks whether a value agrees with trusted evidence. Completeness asks whether required information exists. Consistency asks whether equivalent values follow the same representation. Validity asks whether a value fits its field, format and permitted range.

A project may prioritise these dimensions differently. A migration may focus on target-system validity; a CRM cleanup may concentrate on duplicates and ownership; a catalog audit may examine required attributes and taxonomy conformity.

We therefore define the checks at field level. A valid postal code should not be treated as a verified address, and a similar company name should not automatically be treated as the same account.

Targeted checks for the problems actually present in the dataset

No single cleanup rule is safe for every field or every business use.

Data profiling

Measure missing values, format variation, unexpected terms, apparent duplicates and other patterns before selecting corrections.

Field validation

Check data type, length, pattern, range, controlled vocabulary and cross-field relationships against approved rules.

Standardisation

Apply agreed representations for dates, names, units, addresses, categories and other fields without changing supported meaning.

Duplicate analysis

Identify exact and possible matches using approved comparison fields, while reserving uncertain merge decisions for data owners.

Source verification

Compare priority fields with authorised source documents or reference data where verification is included in scope.

Exception and quality reporting

Deliver issue counts, correction status, unresolved records and reasons so the client can review remaining risk.

Business System Compatibility

Data Quality Assurance Services: Direct Integration and Software Compatibility

Outputs are prepared around the field structure, controlled values and import requirements of your destination environment. Files can be delivered for review, staging or authorised import without forcing your team to rebuild the completed work.

Supported destinations

Structured output for the platforms your team already uses

Files are mapped to the client’s approved template, naming rules, identifiers and system structure before full production begins.

Microsoft ExcelControlled worksheets and import tables
Google SheetsShared review and operational files
SharePointLists, libraries and metadata columns
SalesforceCRM objects and approved fields
ERP / CRM SystemsClient-defined import templates
Custom SQLStaging and relational tables

Source continuity

References stay connected

Source IDs, filenames, record keys and approved relationships remain available for review and downstream traceability.

Import control

Fields are mapped before production

Mandatory fields, formats, controlled values, character limits and relationship keys are checked against the destination specification.

Pilot validation

Test the handoff with a representative batch

Rejected rows, unsupported values and mapping conflicts are returned with exact references so approved corrections can be incorporated before full-volume delivery.

Delivery formatsStructured for review, staging or import

CSV
XLSX
XML

Column order, encoding, date rules, multi-value handling and destination-specific requirements can follow the receiving system’s approved specification.

Compatibility means SDES prepares outputs to specifications supplied or approved by the client. Product names identify commonly used destination systems and do not imply endorsement, certification or partnership.

Process, Quality and Security

A controlled data quality cycle

1. Profile the Dataset

Inspect representative and full-file patterns to identify issue types and likely concentrations.

2. Prioritise Consequence

Rank fields by their effect on operations, reporting, matching and system acceptance.

3. Approve the Rules

Document allowed corrections, trusted sources and conditions that require review.

4. Test Corrections

Run a sample and compare original, proposed and unresolved values before wider change.

5. Apply and Review

Process approved corrections with focused checks on high-risk fields and rule failures.

6. Report the Result

Deliver cleaned data with issue counts, exceptions and an audit trail appropriate to the project.

Quality work needs rules, evidence and a record of what changed

📂 Source formats we accept

Database or spreadsheet export
Field dictionary and mandatory rules
Controlled-value lists
Trusted reference sources
Duplicate and correction policy

📤 Delivery formats

Profile and issue summary
Corrected dataset
Possible-duplicate queue
Rejected or unresolved register
Change and quality report

Free accuracy test

Do you know which data problems are affecting the dataset?

Share a representative export and its intended use. We can identify visible issue patterns and the decisions needed before a safe correction plan is defined.

✓ No credit card required✓ No contract required✓ 24–48 hour return

Review a Data Sample

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Source sampleyour_sample_data.csv

Received

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Verified deliveryverified_output.xlsx

Reviewed

▣ Encrypted transfer◉ Quality controlled

Why Outsource to SDES?

Why quality should be designed into the workflow rather than added at delivery

Independent reviewers reconciling source records and separating exceptions

Field-level rules
Risk-based review
Original values retained where required
Possible duplicates kept separate
Correction reasons recorded
Recurring issues fed back to the process

A final random check may find errors but cannot repair an unclear rule used throughout production. Quality begins with definitions and sample testing, continues through validation and ends with transparent reporting.

For recurring work, issue patterns can reveal where source forms, dropdowns or instructions need improvement. That feedback can prevent future defects instead of repeatedly cleaning the same problem.

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Industries We Support

Quality priorities change with the dataset

Product Data

Category, SKU, attribute, variation and image relationships checked for catalog usability.

Healthcare Administration

Identifiers, dates and required fields reviewed under client-approved handling rules.

Finance Records

Amounts, references, periods and duplicate indicators checked without assuming accounting approval.

Property Data

Parcel, address, owner and document references reviewed against supplied evidence.

Supply Chain Data

Vendor, item, shipment and location records standardised for operational matching.

CRM Data

Account, contact, ownership and duplicate issues prepared for responsible data owners.

Case Studies

Relevant Project Experience

CRM Duplicate and Ownership Review

Project Name: CRM Duplicate and Ownership Review
Volume: 46,800 account and contact records — completed in 7 weeks
Problem: Similar accounts, former employees and inconsistent domains made sales reporting unreliable.
Solution: Exact duplicates were separated from possible matches, and ownership conflicts were routed to account owners.
Outcome: The client received a corrected file plus decision queues for uncertain merges and ownership.
Title: CRM Data Governance Manager
Industry: Business Software
Country: United States

Product Attribute Quality Audit

Project Name: Product Attribute Quality Audit
Volume: 21,300 active SKUs — completed in 3 weeks
Problem: Required attributes and unit formats varied across supplier-fed categories.
Solution: Category-specific rules were applied and unsupported attribute conflicts were reported by supplier.
Outcome: The catalog team received prioritised defects and corrected records suitable for controlled re-import.
Title: Product Information Lead
Industry: Retail
Country: Netherlands

Legacy Spreadsheet Validation

Project Name: Legacy Spreadsheet Validation
Volume: 185 workbooks covering seven years — completed in 12 weeks
Problem: Date, identifier and status fields did not follow one structure across reporting periods.
Solution: Period-specific patterns were profiled before approved fields were standardised into a consolidated table.
Outcome: Analysts received a consistent dataset and a register of source periods requiring further interpretation.
Title: Reporting Manager
Industry: Non-Profit
Country: New Zealand

Client Feedback

What Clients Say About Our Work

5.0/5

The quality review focused on the identifiers, dates and relationships that could actually break our import. It was far more useful than receiving a generic accuracy percentage. Wonderful job by the SDES team; the completed output was exceptional.

Benjamin R. Data Quality Manager · United States

4.0/5

Rejected records came back with reasons we could act on immediately. We were able to improve the source instructions instead of correcting the same problem after every delivery. The agreed turnaround was maintained and saved our team useful time.

Emily J. Master Data Lead · Australia

4.5/5

I was relieved that possible duplicates were not merged automatically. Our account owners received the evidence they needed, and important customer relationships stayed intact. The service has been well worth outsourcing to SDES and offers strong value for the cost.

Oliver G. CRM Governance Manager · United Kingdom

FAQs

Questions about outsourced data quality services

Can you guarantee that every value is correct?

No responsible provider can guarantee unsupported facts. We validate against defined rules and available evidence, then report unresolved items.

Do you automatically merge duplicate records?

Only exact or otherwise approved match conditions may be processed automatically. Possible matches should remain in a decision queue.

Can you clean data before a migration?

Yes. Target-system rules, required fields and mapping decisions should be defined before corrections are applied.

Will we receive the original values?

Where required, delivery can include original value, corrected value, rule or reason, and status for traceability.

Can quality checks become recurring?

Yes. The approved checks can be applied to scheduled new-data batches and reported through agreed quality measures.

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