Validate structured data against clear quality and business rules before it reaches dashboards, migrations, operational workflows or downstream systems. Rudrriv can check completeness, duplicates, formats, allowed values, relationships, reconciliation differences and custom rule exceptions within an agreed scope.
Scope, rules, data handling and final pricing are confirmed after the dataset structure and validation requirement are reviewed.
Illustrative validation workspace — rules and outputs are tailored to the agreed dataset.
Start with a focused quality check or add business rules, reconciliation and documented exception reporting. Larger files, multiple sources and recurring validation are scoped separately.
For a focused spreadsheet or CSV quality check
A practical entry-level review for one small dataset that needs core validation before analysis, reporting or upload.
For business-rule checks and dataset comparison
A deeper validation pass for teams that need rule testing, mismatch identification and clearer exception evidence.
For broader validation and decision-ready QA evidence
A structured review for larger or more complex files requiring multiple rule groups, reconciliation and documented findings.
Pricing is for clearly defined validation scopes. Very large datasets, complex SQL access, extensive remediation, custom automation, regulated-data handling or recurring control execution may require a custom quote.
Get in touch with our expert. Tell us what you need, and we'll help identify the most suitable service, scope and pricing for your requirement.
The workflow starts with the rules that make your data acceptable, then applies those checks in a controlled sequence so exceptions can be reviewed and traced.
Confirm the dataset, purpose, critical fields, expected values, tolerances and output required.
Review structure, columns, types, null patterns, duplicates and obvious integrity issues before deeper checks.
Translate agreed quality and business requirements into clear, testable validation conditions.
Run structural, field, relationship and comparison checks according to the confirmed validation scope.
Organize mismatches and rule failures so the issues can be checked against context and source evidence.
Provide the agreed validation output, exception evidence and practical notes for the next action.
The right validation design depends on what the data represents and how it will be used. Checks can be combined into a rule set that separates structural errors, data-quality issues and business-rule exceptions.
Identify blanks, missing mandatory attributes and incomplete records based on the agreed field requirements.
Check dates, numbers, codes, text patterns, boundaries and field structures against accepted formats.
Find repeated identifiers, duplicate rows or combinations that should occur only once.
Compare fields against approved code lists, categories, master values or reference datasets.
Check whether related records, parent-child keys or lookup values connect as expected across data structures.
Test relationships between fields when valid data depends on more than one value or condition.
Compare mapped source and target records to identify missing rows, mismatches or transformation differences.
Organize findings by rule, field, severity or review status so the next action is easier to prioritize.
Validation for a one-off spreadsheet is different from validation for a system migration or reporting feed. The service can be structured around the stage where data quality needs to be proven.
Useful before uploads, reporting, sharing or analysis when a file needs a defined quality check.
Useful when SQL or system extracts need consistency checks before downstream use or comparison.
Useful when data moves between systems and source-to-target evidence is needed for mapped fields.
Useful before dashboards, models or recurring reports consume data that must meet agreed rules.
Deliverables are selected according to the plan and agreed rules. The goal is to give you traceable findings that can be reviewed, corrected or used as evidence for the next data step.
Data validation does not guarantee that every source value is correct. It creates a defined basis for identifying where data meets the agreed rules and where review is still required.
Validation can be applied whenever data must meet known requirements before it is loaded, shared, reconciled or used to support a business process.
Compare source and target extracts to identify missing records, mapping differences and field-level mismatches.
Check identifiers, contact fields, duplicates, required values and allowed statuses before upload or use.
Validate critical fields and business rules before data is consumed by dashboards, models or recurring reports.
Check required attributes, category values, identifiers, duplicate records and formatting consistency.
Apply agreed field, amount, date and reference rules before reconciliation, reporting or downstream processing.
Review completeness, valid response codes, duplicates and structural consistency before analysis begins.
Good validation depends on knowing what the data is meant to represent. The more clearly the expected rules are defined, the more useful the exception output can be.
A structured validation step can help teams see issues earlier, apply rules consistently and reduce ambiguity when data is handed to another process or system.
These answers cover the practical details buyers usually need before deciding the right validation scope.
Data validation is the process of checking whether data follows agreed structural, quality and business rules before it is used for reporting, analysis, migration, automation or operational decisions. Checks can cover completeness, formats, duplicates, valid values, relationships, reconciliation and custom rules.
The service can be scoped around common business datasets such as Excel or CSV files, database or SQL extracts, reporting exports, source-to-target migration files, CRM or customer records, product data and other structured tabular data. Final scope depends on the file structure, rules and access available.
Depending on the selected plan and agreed scope, checks can include required fields, nulls, duplicates, data types, formats, ranges, allowed values, key integrity, cross-field logic, reference-data matching, source-to-target reconciliation and custom business rules.
Data Validation plans start at $15 USD for a focused entry-level dataset check. The $30 and $50 plans add deeper rule testing, reconciliation and reporting. Larger datasets, recurring validation, multiple sources or complex business rules are quoted after scope review.
The standard delivery window is 5–7 working days. Timing can vary with dataset size, file quality, number of validation rules, source-to-target comparisons and how quickly the required business rules or reference data are supplied.
Please provide the dataset or a representative sample, the purpose of the data, important field definitions, known business rules, accepted values or tolerances, reference data where relevant and any source-to-target mapping needed for reconciliation.
Yes. A validation scope can compare source and target extracts, record counts, key fields, required values and mapped attributes to identify missing, mismatched, duplicate or invalid records. The exact checks depend on the migration design and evidence available.
The primary service is validation and exception identification. Agreed low-risk corrections or a flagged output can be included where appropriate, but large-scale remediation, enrichment or transformation should be scoped separately so changes remain controlled and traceable.
Yes. Custom rules can be included when they are clearly defined and testable, such as valid status values, date relationships, amount thresholds, mandatory combinations, uniqueness requirements or reference-data mappings.
Deliverables depend on the selected plan and can include a validation summary, flagged dataset or exception file, mismatch log, reconciliation findings, rule outcome report and practical notes explaining what was checked and which issues need attention.
Yes. If the same dataset or feed needs regular checks, Rudrriv can scope a repeatable rule set, recurring validation cadence and reporting format as a custom engagement after reviewing the data flow and operating requirements.
No. Use the first enquiry to describe the dataset, approximate size, data type and validation requirement without attaching highly sensitive information. Appropriate file-sharing and handling arrangements can be agreed after the scope is reviewed.
Please describe the requirement without sending highly sensitive data in the first enquiry. We will confirm scope and the appropriate file-sharing approach before work begins.
