Follow critical numbers from source systems through transformations, manual edits, semantic models, and final reports.
Data Quality Review
Data quality review for teams tired of reconciling the same numbers.
Data quality problems show up as reporting problems: dashboards do not match, leaders question the numbers, teams keep offline spreadsheets, and analysts spend too much time explaining exceptions. Parallax Data Lab reviews the path from source systems to reporting outputs so teams can find where trust is breaking and what needs to be fixed first.
Source Systems
Manual Patches
Metric Drift
Reconciliation
Trust Review
What Data Quality Means In Reporting
Data quality is not only whether a table has blanks. In reporting, quality means the data is accurate enough for the decision, complete enough for the audience, consistent across systems, timely enough for the meeting, and traceable enough for leaders to understand. A late order status, duplicated customer record, unowned mapping table, or manually corrected spreadsheet can each break trust in a different way.
Where Quality Breaks
Quality issues often begin upstream: duplicated customer records, inconsistent product hierarchies, late operational entries, manual spreadsheet patches, unowned mapping tables, or business rules that changed without documentation. By the time the issue reaches Power BI or an executive scorecard, the dashboard is blamed for a problem that started much earlier. A review traces the path instead of guessing at the symptom.
How Parallax Helps
Parallax reviews key reports, source extracts, transformations, metric definitions, and exception handling. The output is a clear map of the trust breaks: what is wrong, why it matters, who needs to own it, and which fixes should happen before more reporting automation or dashboard development. The goal is not perfect data. It is known confidence: what can be trusted now, what has limits, and what needs a fix before leaders rely on it.
Related Expertise
Source Trace
Follows a disputed number from source entry through extracts, transformations, manual edits, semantic logic, and final dashboard display.
Explore Source Trace
Exception Review
Separates normal business exceptions from hidden patches, one-off overrides, late entries, and rules nobody has documented.
Explore Exception Review
Trust Map
Ranks trust breaks by decision impact so teams can fix the issues that matter before chasing cosmetic cleanup.
Explore Trust MapSource Trace
Source Trace in practice
Source tracing is useful when leaders ask why a dashboard, spreadsheet, and source system all show different answers. The work follows the number through each handoff and names where the logic changes.
That produces a practical lineage view: source, extract, transformation, model logic, report filter, manual adjustment, and owner.
Exception Review
Exception Review in practice
Exception review looks for the business cases that quietly bend the rules: late orders, merged customers, manual credits, incomplete jobs, reclassified products, or one-off leadership adjustments.
The point is not to eliminate every exception. It is to make exceptions visible enough that recurring reporting can handle them without rebuilding trust each month.
Trust Map
Trust Map in practice
A trust map separates issues that affect leadership decisions from issues that are annoying but low impact. This keeps the cleanup effort from spreading into everything at once.
The map usually identifies quick fixes, owner decisions, structural source problems, and items that should be monitored rather than immediately rebuilt.
Identify missing records, late data, incomplete dimensions, and fields that limit decision usefulness.
Find where systems, teams, dashboards, or spreadsheets define the same business object differently.
Document manual overrides, special cases, one-off corrections, and judgment calls that affect reported numbers.
Clarify who owns source fixes, transformation logic, definitions, and ongoing monitoring.
Separate urgent trust breaks from nice-to-have cleanup so the team can make progress without boiling the ocean.
Questions
What teams usually ask.
Is this the same as a technical data audit?
Not exactly. The review includes technical tracing, but it is focused on reporting trust and business decision impact.
Do we need perfect data before improving dashboards?
No. You need known data. Leaders can act with imperfect data when the limits, owners, and confidence level are clear.
Can this come before automation?
Often it should. Automating a low-quality process can make unreliable reporting spread faster.
Start with fit
Not sure which expertise path fits your reporting problem?
Start with the free Fit Check. The goal is to route the problem to the smallest useful next step, whether that is a focused expertise review or a broader offering.
Book a Fit Check