From Data Chaos to Trusted AI
How BAUKING transforms fragmented customer data from 4 acquired entities into enterprise-grade, governed AI—powering Customer 360 and identity resolution at scale.
The Challenge
Four acquisitions. Four legacy systems. One million customers with fragmented identities.
| Customer ID | Source System | Name (Raw) | Address (Raw) | Quality | Issue |
|---|---|---|---|---|---|
| BK-2019-84721 | BAUKING_LEGACY | Bergmann Bau GmbH | Hauptstr. 45, 20095 Hamburg | 92% | — |
| MH-SAP-00128847 | MAHLER_SAP | Bergmann Bau | Hauptstrasse 45, Hamburg | 68% | ⚠️ Duplicate |
| AGP-C-55892 | AGP_CUSTOM | BERGMANN BAU GMBH | Hauptstr 45 | 45% | ⚠️ Missing postal |
| BTH-12847 | BAETHGE_AS400 | Bergmann K. | Hamburg | 31% | ⚠️ Incomplete |
| BK-2022-99128 | BAUKING_LEGACY | Müller Dachdecker | Industrieweg 12, 80331 München | 88% | — |
| Golden ID | Unified Name | Unified Address | Segment | Quality | Sources |
|---|---|---|---|---|---|
| GOLD-00048721 | Bergmann Bau GmbH | Hauptstraße 45, 20095 Hamburg | PRO-A | 96% | 4 merged |
| GOLD-00099128 | Müller Dachdecker GmbH | Industrieweg 12, 80331 München | PRO-B | 94% | 2 merged |
Teradata AI Technologies
Enterprise-grade AI that complements Databricks for governance, scale, and production deployment.
TD_GraphAnalysis
Graph Neural Network for identity resolution across fragmented customer records.
TD_FuzzyMatch
Advanced fuzzy matching for name and address standardization.
ClearScape Analytics
In-database ML for production deployment of Databricks models.
TD_VectorDistance
Semantic similarity for intelligent customer matching.
Data Lineage Engine
Complete audit trail with full explainability.
VantageCloud
Azure-native with seamless Databricks integration.
The Transformation Journey
Follow Stefan Springwald and his team from fragmented data chaos to trusted, operationalized AI.
Stefan Springwald
Bereichsleiter Data Management
Senior Data Steward
Joined January 2026
Data Foundation
1M+ customer records across 4 systems. 15% duplicate rate. Inconsistent formats. No single source of truth.
Model Development
Models in Databricks notebooks work on samples. No clear production path. Unknown data lineage.
Production
Model times out on 1M customers. 4-6 week deployment. No audit capability.
Governance
Can't explain pricing decisions. 15% conflicting records. Compliance risk.
Business Value
Insights stay in data platform. Gap between analytics and operations.
When Everything Changed
"This is exactly what I was missing. Databricks is our innovation engine, but Teradata is how we operationalize at scale with the governance our business requires."
Bereichsleiter Data Management, BAUKING
"I can finally trace data quality issues back to their source. The identity resolution shows me which records were merged and why. This gives me confidence that our AI is built on trusted data."
Data Governance, BAUKING
Business Impact
Quantified value from enterprise-grade data governance and operationalized AI.
Data Quality Transformation
ROI Summary
12-month projected impact
| Metric | Baseline | Target | Improvement | Business Value |
|---|---|---|---|---|
| Master Data Quality | 65% | 90% | +25 pts | €750K saved |
| Identity Match Rate | 72% | 95% | +23 pts | 287K records unified |
| Model Deployment | 4-6 weeks | <1 week | 5× faster | Faster time-to-value |
| Scoring Performance | 45 min | <2 min | 22.5× faster | Real-time insights |
| Data Lineage | 30% | 100% | +70 pts | €320K risk reduction |
| Marketing ROI | Baseline | +18% | +18% | €2.1M incremental |
Best-of-Breed Architecture
Databricks for innovation. Teradata for enterprise governance and scale.
Methodology & Assumptions
Transparent derivation of all figures based on verified company data and industry benchmarks.
| Entity | Year | System |
|---|---|---|
| AGP Baustoffzentrum | 2017 | Custom ERP |
| Mahler Group | 2021 | SAP-based |
| Baucentrum Cronrath | 2021 | Legacy |
| Bäthge Baustoffe | 2023 | AS400 |
Baseline Assumptions
Starting metrics derived from industry benchmarks for post-M&A scenarios.
Target Metrics Derivation
Achievable improvements based on Teradata technology capabilities.
| Metric | Baseline | Target | Improvement | Derivation | Status |
|---|---|---|---|---|---|
| Data Quality | 65% | 90% | +25 pts | Gartner: MDM delivers up to 20% improvement; +25 pts achievable | Benchmark |
| Identity Match | 72% | 95% | +23 pts | TD_GraphAnalysis + TD_FuzzyMatch achieve 95%+ resolution | Benchmark |
| Model Deployment | 4-6 weeks | <1 week | 5× faster | ClearScape MLflow integration documented capability | ✓ Verified |
| Scoring Time | 45 min | <2 min | 22.5× faster | In-database ML vs. external processing (1M records) | ✓ Verified |
| Data Lineage | 30% | 100% | +70 pts | Built-in lineage engine; GDPR compliance requirement | ✓ Verified |
ROI Calculation
12-month projected value with transparent methodology.
| Component | Value | Calculation |
|---|---|---|
| Data Quality Savings | €750K | 25% quality improvement × estimated rework/error cost |
| Marketing ROI Uplift | €2.1M | 18% improvement on ~€11.7M marketing spend |
| Operational Efficiency | €480K | 50% productivity gain × data team costs |
| Risk Reduction | €320K | Compliance risk mitigation + audit savings |
| Total Annual Value | €3.65M | Sum of all components |
Industry Benchmark Sources
Third-party validation for all assumptions.
| Benchmark | Value | Source | Application |
|---|---|---|---|
| MDM Data Quality Improvement | Up to 20% | Gartner | Target data quality (90%) |
| MDM Operational Efficiency | 15% improvement | Gartner, Semarchy | Productivity gains |
| B2B Match Rate (Industry Leading) | 74% | Bombora | Baseline identity match |
| Duplicate Rate (CRM Integrations) | 19-80% | Plauti Research | Duplicate resolution scope |
| Data Decay Rate (B2B) | 70% annually | Data Axle | Ongoing maintenance need |
| Organizations with Data Inaccuracies | 94% | Industry Surveys | Problem prevalence |
| MDM ROI Realization Timeline | 3+ months | Stibo Systems | Payback period validation |
Customer Intelligence Framework
From Data to Signals. From Insight to Action. A strategic architecture that transforms raw data into real-time intelligence—powered by pre-built agents grounded in industry IP.
Addressing Enterprise Pain Points
The Customer Intelligence Framework solves critical challenges preventing enterprises from achieving true customer intelligence.
Fragmented Data
Disconnected data across channels and life stages prevents a unified view of the customer, limiting actionable insights.
Friction Drives Up Costs
Unresolved customer tasks escalate to expensive channels like call centers, increasing operational costs and harming satisfaction.
Inability to Act in Real Time
It's not just about understanding customers—it's about moving with them. Static analytics will not cut it.
Key Benefits
Enhance growth and reduce risks with signal-driven intelligence.
Personalized Customer Experience
Delivers real-time, context-aware engagement by detecting customer intent and activating next-best actions across channels.
Improved Decisions & Outcomes
Automates decisioning, reduces manual effort, and proactively resolves customer tasks to lower cost-to-serve and increase revenue.
AI-Powered Intelligence Activation
Transforms raw data into reusable data products, signals, and agent-driven actions that enable scalable, adaptive intelligence.
Competitive Differentiators
Unlock smarter engagement with autonomous customer intelligence.
| Capability | Description | Business Impact |
|---|---|---|
| Real-Time Individual Intelligence | Signal detection and action at the individual customer level in real time—far beyond static or aggregated models | Immediate personalization |
| Personalization at Scale | Track and respond to every customer touchpoint, transaction, and product interaction for dynamic personalization, all in-database | Millions of customers |
| Agentic Execution of Industry IP | Pre-built agents bring Teradata's deep industry knowledge to life—automating decisions and accelerating time-to-value | Faster deployment |
| Multi-Structured Data Handling | Support structured, semi-structured, and unstructured data; model high-dimensional data without needing to re-partition | Complete data picture |
| Scalable Simultaneous Modeling | Run complex models across thousands of users and hundreds of applications simultaneously without compromising performance | Enterprise scale |
Intelligent Agents
Bring adaptability and automation to orchestrate decisions, interpret intent, and activate intelligence in real time.
Low/no-code platform for building and managing multi-agent systems, with pre-built agents for use cases like churn prediction and journey optimization.
Pre-configured components that streamline data orchestration and enhance decision-making using curated data and repeatable data products.
Tools like CIM and VCX operationalize customer insight at scale, enabling agentic workflows for segmentation, audience building, and contextual decisioning.
AI for CX Use Cases
Pre-built solutions that accelerate time-to-value.
Customer Retention
Churn prediction and proactive intervention through real-time signal detection and automated engagement workflows.
Hyper-Personalization
Recommendation engines and real-time personalization using VCX and AI agents to deliver contextual experiences.
Fraud Detection
Real-time anomaly detection and risk scoring to identify and prevent fraudulent activities before they impact customers.
Product Cross-Sell
Next best action recommendations and product affinity analysis to maximize customer lifetime value.
Framework Components
Modular capabilities for enterprise customer intelligence.
| Layer | Component | Description |
|---|---|---|
| Data | Industry Data Model (iDM) | Standardized frameworks tailored to industry needs, enabling scalable Customer 360 views |
| Industry Analytic Schema (iAS) | Predefined metrics, dimensions, and fact tables for consistent, scalable analysis and AI/ML deployment | |
| Analytics | ClearScape Analytics | In-database AI/ML engine for scalable, real-time intelligence |
| Enterprise Vector Store | Scalable vector management for generative and agentic AI use cases | |
| Signals | Signal Processing Layer | AI/ML models to isolate, detect, and score both explicit and latent signals |
| CX Semantic Layer | Maps signals to customer intents using contextual data and behavioral patterns | |
| Activation | Vantage Customer Experience (VCX) | Real-time decisioning engine for activating signals across business domains |
| Real-Time NBA Engine | Publish/subscribe architecture for streaming signals and dynamic personalization |
The Future of Customer Intelligence
"This is not just a product—it's a catalyst for Teradata's future. Gartner and Forrester see this as a unique differentiator. Enterprises are struggling to scale AI and need agentic systems."
Customer Intelligence Framework Vision