# Part VI — Customer References, Methodology & Commercial Model

BDB's enterprise strategy combines platform deployment with an operational data management framework. This section details globally validated customer implementations, our delivery framework aligned with DAMA-DMBOK, and available engagement and pricing models.

### Global Customer References

The platform supports data management, lakehouse architectures, and generative AI initiatives across several industries.

<table data-header-hidden><thead><tr><th width="127.800048828125"></th><th width="199.5999755859375"></th><th width="173.199951171875"></th><th></th></tr></thead><tbody><tr><td><strong>Customer</strong></td><td><strong>Sector</strong></td><td><strong>Operational Scale &#x26; Model</strong></td><td><strong>Strategic Architecture &#x26; Relevance</strong></td></tr><tr><td>Toyota Connected Europe</td><td>Automotive (Connected Vehicles)</td><td>5M connected vehicles; petabyte-scale data; across 25 countries.</td><td>Served as a co-design reference for our greenfield framework. Integrates a native platform deployment with a DAMA-aligned data management operating model.</td></tr><tr><td>PowerSchool</td><td>K-12 EdTech (US)</td><td>$5M engagement; Build-Operate-Transfer (BOT) operational model.</td><td>Multi-tenant data layer consolidating 8 Student Information System (SIS) platforms post-acquisition for a Vista Equity Partners-backed enterprise.</td></tr><tr><td>MTN Group</td><td>Telecom (Africa)</td><td>13 operating companies (OPCOs); 65,000 records/sec real-time ingestion; petabyte scale; 100M+ subscribers.</td><td>Production reference for high-throughput streaming and multi-tenant isolation boundaries.</td></tr><tr><td>Sony GCC</td><td>Consumer Electronics</td><td>$667K ARR; 3 active concurrent projects; production environment.</td><td>Enterprise implementation utilizing Generative AI agents and automated synthetic data generation pipelines.</td></tr><tr><td>NWEA</td><td>Educational Assessment (US)</td><td>Core educational assessment sector deployment.</td><td>Provides comparable domain implementation data within the North American education market.</td></tr><tr><td>AT&#x26;T Mexico</td><td>Telecommunications</td><td>Regional petabyte-scale production deployment.</td><td>High-volume operational reference framework, deployed via Comviva integration channels.</td></tr><tr><td>BASF</td><td>Chemicals &#x26; Manufacturing</td><td>Multi-site industrial manufacturing deployment.</td><td>Validated infrastructure reference for high-capacity, petabyte-scale industrial IoT and transactional processing.</td></tr><tr><td>Sanjeevni University</td><td>Higher Education</td><td>3.5-year long-term enterprise contract.</td><td>End-to-end institutional deployment utilizing the underlying Yujaa analytics engine.</td></tr><tr><td>HBKU (Hamad Bin Khalifa University)</td><td>Higher Education (Qatar)</td><td>Multiple concurrent institutional project streams.</td><td>Higher education reference architecture built atop the modular Yujaa platform.</td></tr><tr><td>Indian Oil (IOCL)</td><td>Energy &#x26; Utilities</td><td>Enterprise-wide production environment.</td><td>Validated utility deployment, supported by an official BDB technical reference whitepaper.</td></tr></tbody></table>

> Governance Note: Citation permissions have been validated case-by-case with BDB Customer Success. For prospective procurement cycles where formal external citation is not yet authorized, fully anonymized architectural equivalents are available upon request.

### Methodology: Operational Alignment with DAMA-DMBOK

Deploying enterprise data software without an operational framework can result in fragmented systems. BDB addresses this by embedding the 11 Knowledge Areas of the Data Management Body of Knowledge (DAMA-DMBOK) directly into the platform's core functional capabilities and service delivery.

```
                         [ DATA GOVERNANCE ]
                                  │
       ┌──────────────────────────┼──────────────────────────┐
       ▼                          ▼                          ▼
[ ARCHITECTURE ]          [ DATA QUALITY ]          [ DATA SECURITY ]
 • Layered Design          • 9 OOTB Rules            • RBAC & Masking
 • Medallion Patterns      • ML Anomaly Detection    • Audit Ledgers
       │                          │                          │
       ├──────────────────────────┼──────────────────────────┤
       ▼                          ▼                          ▼
  [ INTEGRATION ]           [ METADATA ]               [ MASTER DATA ]
 • 100+ Connectors         • Lineage Graphs          • Taxonomies
 • Open Table Formats      • Semantic Catalogs       • Ontologies (Future)
```

#### Framework Mapping Matrix

The platform's native functional layers map directly to specific DAMA-DMBOK functional disciplines:

* **Data Governance**
  * ***Platform Implementation:*** Handled via native framework co-design workshops, explicit data stewardship approval workflows, and rule-driven Multi-Step Actions.
* **Data Architecture**
  * ***Platform Implementation:*** Delivered through an enterprise-layered lakehouse architecture using standardized Medallion storage patterns (Bronze/Silver/Gold) and explicit Semantic Business Object models.
* **Data Modeling & Design**
  * ***Platform Implementation:*** Defined via logical Business Objects, decoupled Vocabulary Taxonomies, and upcoming polymorphic Ontology classes.
* **Data Storage & Operations**
  * ***Platform Implementation:*** Standardized on an Apache Hudi lakehouse structure running across cloud-agnostic, containerized Kubernetes infrastructure.
* **Data Security**
  * ***Platform Implementation:*** Enforced using granular Role-Based Access Control (RBAC), data entitlement policies, column-level masking, dynamic row filtering, storage encryption, and unified audit tracking.
* **Data Integration & Interoperability**
  * ***Platform Implementation:*** Supported by more than 100 native pre-built data connectors, open table formats, and secure REST/OData application programming interfaces.
* **Document & Content Management**
  * ***Platform Implementation:*** Unified within an enterprise data catalog, supplemented by Generative AI utility suites for automated documentation drafting.
* **Reference & Master Data**
  * ***Platform Implementation:*** Managed through unified Vocabulary Taxonomies, expanding into formal Ontology classes to drive cross-system entity resolution.
* **Data Warehousing & Business Intelligence**
  * ***Platform Implementation:*** Delivered via native lakehouse analytics, self-service BI environments, and governed enterprise dashboard delivery.
* **Metadata Management**
  * ***Platform Implementation:*** Driven by an automated metadata catalog with semantic analysis engines, end-to-end data lineage tracking, and asset classification.
* **Data Quality**
  * ***Platform Implementation:*** Monitored via 9 out-of-the-box (OOTB) data quality rule archetypes, machine-learning-driven anomaly detection models, and calculated data Trust Scores.

### Commercial & Engagement Models

BDB offers three primary commercial structures designed to align with corporate capital deployment preferences, infrastructure requirements, and scaling patterns.

**Select the Commercial Model Archetype:** Capital Allocation Strategy.

Organizations choose from three core structural frameworks based on their preference for CapEx or OpEx optimization:

* Subscription SaaS: Annual subscription model inclusive of vendor-managed cloud infrastructure. Offered natively across standard United States (US) and European Union (EU) cloud regions.
* Perpetual License: A traditional Capital Expenditure (CapEx) model providing a one-time deployment license. This includes optional Annual Maintenance Contract (AMC) structures scaled at 18% to 22% of the base license value. Customer-owned source code can be positioned under a structured escrow agreement.
* Hybrid Consumption: A blended framework combining a baseline platform license with per-data-source and per-user consumption metering to link costs directly to business outcomes.

**Map Operational Scaling Parameters:** Sizing Dimensions.

Final pricing structures are calculated across five operational dimensions:

* **Data Sources:** Total number and structural complexity of required source system connectors.
* **Active Seat Distribution:** User count partitioned explicitly by functional persona (e.g., Data Engineers, Analysts, Business Consumers).
* **Data Volume:** Total physical data scale measured under active management.
* **Compute & AI Consumption:** Operational runtime volume dedicated to GenAI agents and processing pipelines.
* **Satellite App Footprint:** Total count of isolated sub-applications or dashboards hosted on the platform.

**Execute Architectural Scoping:** Final Proposal Formulation.

RFI responses provide ballpark baseline pricing estimates based on standard operational tiers. Final contractual pricing is scoped directly against the customer's specific technical architecture, high-availability parameters, and deployment environment.


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