FastDQ
Generate Professional Evidence for Data Quality Initiatives
The Problem and Our Solution
The Problem
You know data quality problems exist, but proving it to stakeholders is difficult:
- Ad-hoc SQL queries require technical teams and take weeks
- Vague qualitative feedback gets dismissed
- Without credible evidence, DQ initiatives stall
- Stakeholders push back: "the data is poor but functional, why change?"
- Building a defensible case requires tangible, professional evidence
Data managers and consultants need ammunition to make their case, not another tool to configure.
The Solution
FastDQ is desktop software that generates professional evidence in minutes, not weeks:
- No technical teams required: Upload a CSV or Excel file and analyse
- Minutes to results: Comprehensive analysis across 24 checks
- Professional reports: Executive-ready PDF documentation
- Defensible analysis: Conservative findings that withstand scrutiny
- Business language: Technical findings translated to operational impact
When you analyse data from multiple angles, you'll find defensible evidence to build your case.
How It Works
FastDQ's simple workflow gets you from data file to professional evidence in minutes:
1. Upload Your Data
Insert your spreadsheet/s. FastDQ automatically standardises column names and detects field types (emails, dates, IDs, etc.) with no configuration required.
2. Run Analysis
Click analyse and FastDQ performs 24 data quality checks across five dimensions. Analysis completes in seconds to minutes depending on dataset size.
3. Review Executive Summary
See findings prioritised by confidence level and business impact. Issues are ranked from high to low priority with clear descriptions of what's wrong and why it matters.
4. Export Professional Report
Generate executive-ready PDF reports with professional language describing business consequences. Use these reports in presentations or documentation to build your case for data quality initiatives.
What We Analyse
FastDQ examines your data across five critical dimensions, with findings prioritised by both likelihood of being genuine issues and business impact:
Accuracy
Logical impossibilities like negative ages, implausible values over 130 years old, and date sequence violations. Catches data that breaks business rules.
Completeness
Missing values, incomplete records, and hidden gaps like placeholder values ("TBD", "N/A"). Identifies which columns and rows have insufficient data for operations.
Consistency
Format standardization issues including whitespace problems, mixed boolean representations, inconsistent date formats, and case pattern variations. Identifies data that complicates processing and integration.
Uniqueness
Duplicate records, ID field violations, and disproportionate value distributions. Highlights data redundancy and potential processing inefficiencies.
Validity
Format compliance for emails, phones, and other typed fields. Detects data that doesn't conform to expected patterns, including test data and encoding artifacts.
Example Outputs
View a complete example report: Download Sample PDF Report
Key Features
- Automated Field Detection: Recognizes emails, dates, IDs, names, and other field types without configuration
- 24 Data Quality Checks: Comprehensive analysis across completeness, uniqueness, validity, accuracy, and consistency
- Priority Ranking: Issues sorted by confidence level and business impact to focus attention on what matters most
- Professional Language: Technical findings translated to business consequences that resonate with stakeholders
- PDF Report Generation: Executive-ready documentation you can use in presentations and proposals
- Desktop Software: No cloud uploads, your data stays secure on your machine
- Multiple Dataset Analysis: Run analyses on different datasets to find the best evidence for your case
- Conservative Analysis: Defensible findings designed to withstand stakeholder scrutiny
Use Cases
Building Investment Cases
Scenario: A data manager needs to justify budget for a data governance program but has only anecdotal evidence of quality issues.
Solution: Analyse representative datasets with FastDQ to generate professional reports showing specific completeness gaps, format inconsistencies, and validity issues. Present quantified findings to leadership with clear business impact descriptions.
Result: Credible, defensible evidence that moves the conversation from "we think there's a problem" to "here's documented proof of the problem and its scope."
Consulting Engagements
Scenario: A data consultant needs to quickly assess a client's data quality to identify improvement opportunities and scope a project.
Solution: Run FastDQ analysis on client data samples to produce professional assessment reports. Use the findings to demonstrate expertise and quantify the scope of quality issues requiring attention.
Result: Professional deliverable delivered in minutes that establishes credibility and provides foundation for scoping and pricing proposals.
Financial Services Compliance
Scenario: A compliance team needs to document data quality for regulatory audit preparation, with particular focus on customer contact data completeness and transaction record accuracy.
Solution: Analyse customer databases to identify invalid email formats, incomplete contact information, and logical inconsistencies. Generate reports suitable for audit documentation.
Result: Professional documentation of data quality state that satisfies audit requirements and identifies remediation priorities.
Ready to Get Started?
Schedule a demo or request pricing information to see how FastDQ can help you generate professional evidence for your data quality initiatives.