/ Data Quality
Data Quality Consultation
Validate, clean, and enrich data at the source before it reaches platforms. Free 30-day assessment.
/ Challenges
Challenges
Garbage In, Garbage Out
Bad data reaches platforms before validation. Data teams spend 60% of time cleaning data instead of analyzing it.
Late Error Detection
Find data quality issues days or weeks after ingestion. By then, bad data has corrupted dashboards, reports, and ML models.
No Source Accountability
Can't trace bad data back to source. No way to fix root cause. Same errors repeat forever.
Platform-Level Validation
All validation happens in Snowflake, Databricks, or Splunk. Wasting expensive compute on data that should never have been ingested.
/ What We Do
Schema Validation
Enforce schemas at the source. Reject malformed data before it moves. Type checking, required fields, format validation.
Zero schema errors downstream
Data Cleansing
Clean data at creation point. Remove duplicates, fix formatting, standardize values. Bad data never reaches platforms.
60% less cleaning work
Enrichment at Source
Add context, lookup values, join reference data at the edge. Enrich once, use everywhere.
Richer data, less platform load
Quality Metrics
Track quality at every source. Identify problem sources, measure improvement, hold teams accountable.
Continuous improvement
/ What We Validate
Completeness
Required fields present, no missing values
Accuracy
Values within expected ranges, correct formats
Consistency
Data matches across sources, no contradictions
Timeliness
Data arrives when expected, no stale data
Validity
Values conform to business rules and constraints
Uniqueness
No duplicates, proper deduplication
/ What You Get
Quality Audit
Quality audit report with issue breakdownWe analyze your data quality issues - where they originate, what they cost, and how often they occur
Source Analysis
Source quality scorecardIdentify which sources produce the most errors and what validation rules would catch them
Validation Rules
Validation rule libraryDesign validation rules for each source - schemas, business rules, quality checks
Implementation Roadmap
90-day quality improvement planStep-by-step plan to implement source-level validation and quality monitoring
/ Real-World Examples
E-Commerce: Product Data Quality
Challenge
Product catalog with 1M+ SKUs from 500+ suppliers. 30% of products had missing or malformed data. Data team spent weeks cleaning before each catalog update.
Solution
Implemented schema validation at supplier integration point. Reject malformed data, send feedback to suppliers. Automated cleansing for common issues.
Result
95% data quality at ingestion, 80% less cleaning work, faster catalog updates
Healthcare: Lab Results Validation
Challenge
Hospital lab system with 50+ instruments. Different formats, units, and value ranges. Errors in lab results caused patient safety issues.
Solution
Deployed validation at instrument level. Check value ranges, standardize units, flag anomalies. Enrich with reference data (normal ranges, test metadata).
Result
Zero unit conversion errors, 99% data quality, improved patient safety
Financial Services: Transaction Validation
Challenge
Payment platform processing millions of transactions daily. 5% had data quality issues - missing fields, invalid amounts, wrong formats. Caused reconciliation nightmares.
Solution
Implemented real-time validation at payment gateway. Schema enforcement, business rule validation, duplicate detection. Reject bad transactions before processing.
Result
99.9% transaction quality, 90% less reconciliation work, faster settlement
/ Expected Outcomes
60%
Reduction in data cleaning work
95%
Data quality at ingestion
Zero
Schema errors reaching platforms
Real-time
Error detection vs. days/weeks later
Tired of Cleaning Bad Data?
If your data team spends more time cleaning than analyzing, we should talk. Book a free quality assessment.