Data Quality Consultation
Validate, clean, and enrich data at the source before it reaches platforms. Free 30-day assessment.
Common Data Quality 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.
Quality at the Source
Validate, clean, and enrich data where it's created - catch errors early, improve downstream quality
Schema Validation
Enforce schemas at the source. Reject malformed data before it moves. Type checking, required fields, format validation.
Zero schema errors downstreamData Cleansing
Clean data at creation point. Remove duplicates, fix formatting, standardize values. Bad data never reaches platforms.
60% less cleaning workEnrichment at Source
Add context, lookup values, join reference data at the edge. Enrich once, use everywhere.
Richer data, less platform loadQuality Metrics
Track quality at every source. Identify problem sources, measure improvement, hold teams accountable.
Continuous improvementWhat's Included in Your Assessment
Quality Audit
We analyze your data quality issues - where they originate, what they cost, and how often they occur
Source Analysis
Identify which sources produce the most errors and what validation rules would catch them
Validation Rules
Design validation rules for each source - schemas, business rules, quality checks
Implementation Roadmap
Step-by-step plan to implement source-level validation and quality monitoring
Real-World Examples
E-Commerce: Product Data Quality
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.
Implemented schema validation at supplier integration point. Reject malformed data, send feedback to suppliers. Automated cleansing for common issues.
95% data quality at ingestion, 80% less cleaning work, faster catalog updates
Healthcare: Lab Results Validation
Hospital lab system with 50+ instruments. Different formats, units, and value ranges. Errors in lab results caused patient safety issues.
Deployed validation at instrument level. Check value ranges, standardize units, flag anomalies. Enrich with reference data (normal ranges, test metadata).
Zero unit conversion errors, 99% data quality, improved patient safety
Financial Services: Transaction Validation
Payment platform processing millions of transactions daily. 5% had data quality issues - missing fields, invalid amounts, wrong formats. Caused reconciliation nightmares.
Implemented real-time validation at payment gateway. Schema enforcement, business rule validation, duplicate detection. Reject bad transactions before processing.
99.9% transaction quality, 90% less reconciliation work, faster settlement
Expected Outcomes
Tired of Cleaning Bad Data?
If your data team spends more time cleaning than analyzing, we should talk. Book a free quality assessment.