Enterprise Data Quality
& Taxonomy Standardization
Transform messy legacy data into standardized, governance-ready assets
Why Your Data Quality Initiatives Fail
Legacy systems with inconsistent classification schemes
- Decades of informal taxonomies and ad-hoc categorizations
- Different departments using different classification systems
- No formal specifications or version control
- Business logic exists only in tribal knowledge
Real cost: Unreliable analytics, failed integrations, compliance gaps
M&A and system integrations create data chaos
- Acquired companies bring conflicting taxonomies
- Product catalogs that don't align
- Customer data with incompatible schemas
- Manual mapping efforts that never finish
Real cost: Integration delays, lost synergies, duplicate data
Purpose-Built for Data Standardization
Standardized, governed data assets in weeks
- Proprietary taxonomy standardization toolkit
- Formal specifications with URIs and version control
- Automated validation and quality scoring
- Audit-ready documentation for compliance
Enterprise quality at sustainable cost
- ML-Ops trained teams who understand data architecture
- Fixed-price, fixed-timeline delivery
- 50-60% less than Big 4 consultancies
- Ongoing governance and maintenance included
Real-World Impact
Supply Chain Rate Standardization
Challenge: Supply chain planning and services providers lacked internal data classifications for rates, locations and modes (air, sea, rail, road) across multiple jurisdictions
Impact: Standardized rate management system, facilitating instant quoting and improved routing of cargo
E-Commerce Pricing Analytics
Challenge: Market-leading e-commerce provider had not deployed defined taxonomies within its data warehouse and was unable to marry internal data with external demographic codesets
Impact: Implemented workable, robust analytics including an associative (semi-dynamic) pricing engine
Alternative Credit Scoring System
Challenge: FinTech needed production-ready credit scoring for MSME lending with inconsistent data sources and no standardized feature taxonomy
Impact: Built data lake with standardized feature engineering pipeline, deployed ensemble model with documented ML-Ops workflow
Hedge Fund Indicator Rationalization
Challenge: Hedge fund had disparate pricing and performance indicators across multiple data sources without clear taxonomic structure
Impact: Used analytical models to identify most predictive indicators, re-categorizing data along performance-oriented lines
Proven Process
Assessment
2 weeks
- Map existing classification systems
- Identify conflicts and gaps
- Deliver standardization roadmap
Standardization
8-16 weeks
- Formalize taxonomies with URIs
- Create mapping and validation rules
- Migrate and validate data
Governance
Ongoing
- Version control and change management
- Quality monitoring and validation
- New system integration
Start with a Data Quality Assessment
- Complete taxonomy and classification audit
- Data quality scoring across systems
- Conflict and gap analysis
- Detailed standardization roadmap
- Cost and timeline estimates
Money-back guarantee if you're not satisfied with deliverables