TARAN PHILLIPS

Data Architecture

Modern Data Platform Design for Enterprise Analytics

A strong data architecture does more than move data. It creates a trusted foundation for executive reporting, KPI governance, self-service analytics, and scalable decision support across the organization.

Architecture Overview

Modern analytics platforms must support performance, trust, flexibility, and maintainability. The most effective architectures create a clear path from raw source data to business-ready reporting assets while preserving governance and reducing reporting inconsistencies.

My approach focuses on building data ecosystems that support both technical scalability and business usability. That means structuring the platform so data engineers, analysts, and executives all work from the same trusted foundation.

Modern Enterprise Data Architecture Diagram

Architecture Principles

Centralize Critical Data

Bring key operational, financial, and business data into a governed platform to reduce fragmentation and improve trust.

Separate Data Layers

Use clear raw, transform, and business-ready layers to improve maintainability and support consistent reporting logic.

Standardize KPI Logic

Define metrics once and reuse them across dashboards, reports, and analyses to eliminate conflicting numbers.

Design for Scale

Build architectures that can grow across departments, subject areas, and reporting needs without breaking trust.

Support Self-Service

Make analytics-ready datasets available so business users can answer questions faster with less dependency on ad hoc requests.

Govern for Trust

Ensure lineage, documentation, testing, and ownership are built into the architecture rather than added later.

Reference Architecture Flow

Source Systems
→
Ingestion Layer
→
Snowflake Warehouse
→
DBT Transformations
→
Business Logic Layer
→
BI & Executive Reporting

Core Platform Components

1. Source Systems

  • ERP and finance platforms
  • CRM systems
  • Operational applications
  • Workforce and HR systems
  • Support and service management platforms
  • Spreadsheets and flat file feeds

2. Ingestion Layer

  • Automated data ingestion pipelines
  • Batch and scheduled loads
  • Structured landing zones for incoming data
  • Load tracking and refresh monitoring
  • Error handling and retry logic
  • Controlled movement into warehouse layers

3. Data Warehouse

  • Centralized data storage in Snowflake
  • Scalable compute for enterprise reporting
  • Subject-area organization for maintainability
  • Historical storage for trend analysis
  • Performance optimization for business workloads
  • Secure access aligned to business roles

4. Transformation Layer

  • DBT models for transformation and logic reuse
  • Data cleansing and normalization
  • Conformed dimensions and fact tables
  • KPI logic standardization
  • Reusable models for reporting teams
  • Testing and documentation within the pipeline

5. Business Logic Layer

  • Analytics-ready data products
  • Executive KPI datasets
  • Department-specific subject marts
  • Cross-functional metric alignment
  • Reusable reporting definitions
  • Curated tables for self-service analytics

6. Reporting & Consumption Layer

  • Power BI executive dashboards
  • Operational performance reporting
  • Strategic scorecards
  • Departmental BI dashboards
  • Ad hoc analysis support
  • Leadership-ready visualizations

Layered Data Model

Raw Layer

Stores ingested source data with minimal transformation. This layer preserves original source structure for traceability and troubleshooting.

  • Source-aligned tables
  • Audit fields
  • Load timestamps
  • Minimal business logic

Transform Layer

Applies cleansing, joins, standardization, and business rules to create trusted, reusable data models.

  • Conformed dimensions
  • Business rules
  • Validation checks
  • Reusable intermediate models

Business Layer

Exposes analytics-ready datasets designed for dashboards, scorecards, KPI tracking, and self-service reporting.

  • Executive-ready metrics
  • Stable reporting logic
  • Subject-area marts
  • Self-service consumption

Governance Framework

Metric Governance

  • Documented KPI definitions
  • Calculation standardization
  • Source-to-report traceability
  • Ownership by business domain
  • Version control for logic changes
  • Executive alignment on metric meaning

Data Quality Controls

  • Null and freshness checks
  • Duplicate detection
  • Range and logic validation
  • Schema monitoring
  • Load completion verification
  • Exception handling and escalation

Documentation & Lineage

  • Model documentation in DBT
  • Business definition tracking
  • Upstream and downstream dependency visibility
  • Data catalog support
  • Source system mapping
  • Analyst onboarding acceleration

Access & Security

  • Role-based access controls
  • Restricted access for sensitive data
  • Clear separation of environments
  • Consumer-level permissions
  • Governed report distribution
  • Secure data sharing practices

Typical KPI Domains Supported

Financial Analytics

  • Revenue trends
  • Budget vs actual
  • Expense tracking
  • Margin visibility

Operational Analytics

  • Throughput and volume
  • Cycle time
  • Service performance
  • Backlog monitoring

Strategic Analytics

  • Growth metrics
  • Engagement trends
  • Program performance
  • Leadership scorecards

Technology Stack

Core Technologies

Snowflake DBT SQL Power BI SSRS SQL Server MySQL Oracle Python Azure

Platform Outcomes

  • Improved reporting consistency
  • Reduced manual effort
  • Faster dashboard delivery
  • Stronger KPI trust
  • Better executive visibility
  • Scalable analytics operations

What Strong Data Architecture Enables

For Leadership

  • Trusted executive dashboards
  • Faster access to performance trends
  • Better strategic planning support
  • Clearer cross-functional alignment

For Analytics Teams

  • Reusable data models
  • Lower ad hoc request volume
  • Better data quality controls
  • More time spent on insight generation