Catch bad data
before it ships.
Validate, clean, and enrich data at the source, not after it poisons your warehouse. Free 30-day assessment ends with a data-quality scorecard.
Before
After
Cases
Define representative records
Rules
Declare expected behavior
Test
Observe every outcome
Measure
Record customer-specific quality
/ Challenges
Challenges
Garbage In, Garbage Out
Bad data can reach platforms before validation, shifting cleanup and investigation downstream.
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.
Tested schema behavior
Data Cleansing
Clean data at creation point. Remove duplicates, fix formatting, standardize values. Bad data never reaches platforms.
Measured cleaning impact
Enrichment at Source
Add context, lookup values, join reference data at the edge. Enrich once, use everywhere.
Richer data, less platform load
Quality Metrics
Define and collect source-specific quality measures for the intended workload.
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
/ Evaluation Scenarios
Schema Validation Evaluation
Challenge
A team needs to validate required fields and formats before writing records to a destination.
Solution
Define representative valid and invalid records, configure documented processors, and observe every outcome.
Evidence
Evidence to collect: accepted, rejected, changed, and failed records plus destination receipt.
Normalization Evaluation
Challenge
A team needs to normalize fields from multiple source formats.
Solution
Specify expected transformations, test edge cases, and compare source records with configured outputs.
Evidence
Evidence to collect: transformation accuracy, error handling, throughput, and retained provenance.
Deduplication Evaluation
Challenge
A team needs to identify duplicate events before a configured output.
Solution
Define the identity and time-window rules, test representative duplicates, and verify output behavior.
Evidence
Evidence to collect: false positives, false negatives, state limits, replay behavior, and destination counts.
/ Book your free consultation
Catch bad data before it ships?
Pick a time. 30 minutes. No slides — we go straight to your data-quality gaps and the upstream rules that would have stopped them.
- 30-day assessment with a prioritized data-quality scorecard
- Free of charge — no commitment beyond the call
- We'll ask hard questions, not try to sell
