File intake
Source folders, uploads, scheduled pulls, APIs, exports, or recurring files from the places they already arrive.
Solutions
Data Preparation
Data Preparation is the preparation layer of an automated reporting system. It turns recurring files, exports, PDFs, spreadsheets, and system data into prepared reporting data for dashboards, reports, exports, or review workflows.
Preparation layer
Prepared data comes before the dashboard, report, or review step.
Data Preparation gets recurring reporting inputs ready. The downstream layer depends on what the team needs to review, share, export, or approve.
Data Preparation
Gets recurring files, exports, PDFs, spreadsheets, and system data cleaned, combined, mapped, validated, and ready for reporting.
Reporting Dashboards
Use prepared reporting data as the view layer for review, monitoring, filtering, and exploration.
Report Generation
Creates packaged outputs such as PDFs, exports, summaries, or stakeholder-ready reports from prepared data.
Review Workflows
Handles exceptions, corrections, approvals, status, and review-before-output when the data still needs judgment.
When preparation needs structure
Recurring inputs need more than cleanup when the work has to repeat reliably.
Most reporting has data preparation somewhere. This is the point where cleanup steps need to become a repeatable workflow around files, mapping, validation, exceptions, and handoffs.
Recurring inputs
Files arrive every month or quarter from multiple systems, teams, clients, vendors, operators, entities, or locations.
Changing structure
Similar files have changing tabs, headers, names, dates, labels, fields, layouts, or source folder patterns.
Reporting dependency
Dashboards, report packages, exports, Power BI models, or metrics depend on manual ETL, spreadsheet consolidation, or a fragile master workbook.
Trust and handoff
Validation, exception handling, source history, field mapping, or review before output is needed before downstream reporting uses the data.
Existing tools
Build around the tools already in place.
Excel, Power Query, Power BI dataflows, Tableau Prep, connector tools, scripts, and warehouses may already be the right preparation layer. Valiance Labs fits when the workflow around those tools needs custom intake, validation, mapping, source history, review, or prepared outputs.
Keep the existing tool
Improve intake, source folders, validation, or review around Excel, Power Query, Power BI dataflows, Tableau Prep, or connector workflows.
Build dashboard-ready data
Prepare a cleaner source for Power BI, Tableau, Excel, internal dashboards, databases, report-generation feeds, or review workflows.
Add business-specific rules
Handle field mapping, entity matching, date normalization, changed headers, unusual totals, or file rules that are awkward to maintain in a workbook.
Add review before output
Surface missing fields, exceptions, source history, or handoff steps before prepared data flows into downstream reporting.
What it can include
Intake, mapping, validation, source history, and prepared outputs.
A preparation layer should stay close to the recurring reporting inputs and the downstream output it needs to support.
Spreadsheet, CSV, and PDF handling
Structured files can often be standardized faster; PDFs and semi-structured reports may need extraction and review.
Field mapping
Source-specific columns, labels, account names, entities, and dates mapped into a consistent reporting structure.
Cleaning and shaping
Rows, tabs, headers, merged fields, blank values, and file-specific formats prepared for repeatable reporting.
Name and date normalization
Entity matching, name normalization, date normalization, and period handling for recurring reporting data.
Validation checks
Required fields, totals, expected ranges, changed layouts, and source completeness checked before output.
Exception flags
Records that need review are surfaced instead of silently flowing into dashboards or reports.
Source history
A record of which file arrived, where it came from, when it was processed, and what changed.
Prepared outputs
Dashboard-ready data, report-ready data, exports, feeds, or database tables for the next reporting layer.
Prepared outputs
Prepared reporting data should have a clear destination.
The output may still be a spreadsheet, but it should be repeatable, traceable, and ready for the next reporting layer.
Standardized source table
A consistent table for recurring files that would otherwise stay scattered across workbooks, exports, folders, or tabs.
Clean CSV or Excel export
A repeatable file output for teams that still need Excel or CSV in the reporting handoff.
Prepared reporting data that can feed Power BI, Tableau, Excel, internal dashboards, or a focused reporting dashboard.
Power BI or Tableau-ready source
A prepared source layer that existing BI tools can refresh from without relying on as much manual cleanup.
A structured feed for recurring reports, exports, summaries, PDFs, or stakeholder packages.
A reviewable list of missing fields, changed headers, unusual totals, unmapped values, or source files that need attention.
Source file history
A record of what arrived, when it arrived, where it came from, and how it was prepared.
Review before output
Some data needs a checkpoint before it feeds reporting.
Review does not mean the workflow failed. It means the system is honest about what still needs confirmation before dashboards, reports, or exports use the data.
Source files change
New tabs, changed headers, late files, unexpected file types, or shifted source folders should be caught before refresh.
PDFs are involved
PDF extraction depends on structure, scan quality, table layout, and review rules; it should not be treated as automatic by default.
Required fields are missing
Missing names, dates, entities, amounts, departments, clients, or periods should create an exception before downstream reporting uses the record.
Numbers need judgment
Unusual totals, manual adjustments, entity mismatches, or metric definitions may need confirmation before a dashboard or report is released.
Tool or workflow
Keep it in the tool, or turn it into a workflow.
Most reporting already has some form of data preparation behind it. The decision is whether that work can stay inside the current tool, or whether it needs structure around intake, validation, review, source history, and downstream handoff.
Keep it in the tool when...
- Source files are stable and use the same schema each cycle.
- The preparation logic is simple enough for one team to maintain.
- Excel, Power Query, Power BI, Tableau Prep, or another existing tool already refreshes cleanly.
- The prepared output only feeds one clear reporting need.
Turn it into a workflow when...
- Recurring file prep becomes fragile, repeated, or hard to audit across cycles.
- Formats, tabs, headers, source folders, names, dates, or entities change often enough to break trust.
- Prepared data feeds multiple dashboards, reports, exports, databases, or review steps.
- Validation, exception handling, source history, review, or handoff needs to be visible before output.
Start a project
Start with the reporting workflow that keeps getting stuck.
Valiance Labs can help decide whether the right move is Power Query support, dashboard-ready data, a custom preparation workflow, or another reporting layer.