Required field checks
Check whether required fields are present and populated before the output is used.
Data Preparation Capability
Data Validation Services
Check prepared data for missing fields, duplicates, invalid values, changed layouts, unusual rows, and exceptions before the output is used.
Problem
The file may look clean, but the team needs to know what is missing, unusual, invalid, duplicated, changed, or risky.
Data Validation checks whether the prepared output is usable. It surfaces required field issues, missing values, duplicate IDs, invalid dates, changed layouts, unexpected categories, unusual values, rejected rows, and exceptions before the data moves forward.
A clean-looking file can still fail
Imports, dashboards, reports, migrations, and analysis can break when required fields or formats are wrong.
Exceptions should be visible
Rows that need human review should be surfaced clearly instead of buried inside a prepared file.
Validation is not a guarantee
Checks make issues visible against known requirements; they do not replace business judgment or review.
What this includes
What Data Validation includes.
The work is focused on checking prepared data before it is imported, reported on, analyzed, migrated, or handed off.
Missing value checks
Flag blanks or missing values that matter for the destination workflow.
Duplicate checks
Surface duplicate rows, duplicate IDs, or repeated records that need review.
Invalid value checks
Check invalid dates, unexpected types, suspicious values, and values outside known rules.
Changed header or layout checks
Catch changed fields, headers, layouts, or file shapes before they break the workflow.
Exception lists and reports
Produce reviewable validation reports, rejected-row lists, and exception reports.
What Valiance Labs works with
Business files that often need validation.
Validation matters when prepared data is about to feed something important and the team needs to know what should be reviewed first.
- spreadsheets
- CSV files
- exports
- CRM exports
- customer, vendor, contact, product, or location lists
- operational reports
- system downloads
- platform import templates
- dashboard or report source files
- structured PDFs or reports where feasible
Clean output
What the validated output looks like.
The deliverable makes validation issues visible so the team can decide what to fix, reject, or review.
validation report
A summary of checks run, issues found, and rows or files that need review.
rejected-row list
Rows that failed known requirements and should not move forward without review.
exception list
Unusual, missing, duplicate, invalid, or changed values surfaced for human judgment.
missing-field report
Required fields or expected columns that are missing, blank, or changed.
validated output file
A checked CSV/XLSX, table, or dataset prepared for the next workflow.
data quality checks
Documented checks that can be repeated against future versions of the file pattern.
Within Data Preparation
How Data Validation fits into Data Preparation.
Data Validation is one part of Data Preparation. A full data preparation workflow may also require cleansing, standardization, mapping, or consolidation depending on the files and desired output.
Data Preparation
The broader service for preparing recurring business files and surfacing exceptions before use.
Imports and Migrations
Useful when files need required field, duplicate, and rejected-row checks before upload.
Reporting and Dashboards
Useful when dashboard inputs need to be checked before refresh or review.
Report Outputs
Useful when report-ready exports need exception checks before distribution.
Analysis and Operations
Useful when operational files need missing, duplicate, or unusual rows surfaced before analysis.
Existing tools
We work alongside the validation tools your team already uses.
Spreadsheets, import tools, Power Query, CRM or SaaS import checks, BI checks, data quality checks, and scripts can catch basic issues when the rules are known. Valiance Labs fits when validation needs to sit inside a repeatable preparation workflow, flag exceptions, and show whether the cleaned output is ready for import, reporting, migration, analysis, or handoff.
Basic checks can stay simple
If the checks are a few required fields, duplicate IDs, invalid dates, or known values, an existing spreadsheet, import, or script-based check may be enough.
Keep useful import or quality checks
If a CRM importer, SaaS template, BI check, Power Query step, or data quality script already catches useful issues, Valiance Labs can build around it.
Add structure when validation affects the next workflow
When rejected rows, changed layouts, exception lists, or validation reports determine whether the output is ready, the checks may need to become part of the workflow.
When structure matters
When validation needs structure.
Valiance Labs fits when validation repeats, exceptions matter, and the prepared output should not be used until issues are visible.
The output feeds an important workflow
Imports, migrations, dashboards, reports, analysis, or operations depend on the prepared data.
Source formats change
Headers, layouts, required fields, dates, or categories change enough that checks need to catch them.
Rejected rows need review
The team needs row-level issues, exception lists, or validation reports before handoff.
One person owns the manual QA
The validation process lives in memory, spreadsheet filters, or informal checks that are hard to repeat.
Project start
Start with the checks the output must pass.
Share the prepared file, required fields, known failure cases, and what needs to be reviewed before the output is used.
Start a project
Start with the files and the output you need.
Share the spreadsheets, CSVs, exports, reports, or business files your team works with, and what the cleaned output needs to support.