Data Foundation Build

Making your data governed, trustworthy, and ready for anything

Unified data models, governance frameworks, automated quality checks, data lineage, semantic layers, and access controls. Turn fragmented data into a governed, reliable asset.

TL;DR

→What: Unified data model + governance framework + automated quality + lineage + semantic layer + access controls
→Impact: Data your organization can actually trust—governed, consistent, and ready for analytics or AI
→Timeline: 10-20 weeks depending on data complexity and number of sources

Infrastructure Isn't Enough

You can have the best cloud infrastructure in the world and still have a data mess. Pipelines running, data flowing, and nobody trusting any of it because there's no governance, no quality checks, and no common understanding of what anything means.

This is where we turn raw data into a governed asset. Unified data models that create a single source of truth. Governance frameworks that define who owns what. Quality checks that catch problems before they reach dashboards. Semantic layers that ensure everyone's speaking the same language.

The result: data your organization can actually trust. Data that's ready for analytics, AI, and whatever comes next.

Key Deliverables

Unified data model (conformed dimensions, fact tables)
Data governance framework (ownership, stewardship, policies)
Automated data quality checks and monitoring
Data lineage and impact analysis
Data catalog implementation
Semantic/metrics layer for consistent definitions
Access control policies and implementation
Retailer data harmonization playbook (for CPG)
APIs for downstream systems

What We Build

Unified Data Model

A single source of truth that reconciles data from across your organization. Conformed dimensions, standardized hierarchies, and fact tables that support your analytics needs.

One version of truthConsistent hierarchiesAnalysis-ready structures

Governance Framework

Clear ownership, documented policies, and operational processes that keep your data trustworthy over time. Not bureaucracy—practical governance that people actually follow.

Clear data ownershipDocumented policiesChange management process

Data Quality

Automated checks that catch problems at the source, not when they show up in executive dashboards. Monitoring, alerting, and remediation workflows.

Automated validationProactive alertingQuality scorecards

Semantic Layer

A common language for your metrics and dimensions. Everyone from finance to marketing to ops agrees on what 'revenue' means and how it's calculated.

Consistent definitionsSelf-service enabledReduced confusion

CPG & Retail Specific

Retailer Data Harmonization

CPG companies deal with data from dozens of retailers, each with their own formats, hierarchies, and definitions. We've built the playbooks and patterns to harmonize this data into a unified view.

Standardized product hierarchies across retailers
Normalized promotional and trade spend data
Unified store and geography mappings
Harmonized time periods and fiscal calendars
Cross-retailer performance comparisons

How It Works

Week 1-3

Discovery

Source analysis, business requirements, model design

Week 4-10

Build

Data modeling, governance setup, quality framework

Week 11-16

Integration

Source integration, semantic layer, access controls

Week 17-20

Operationalize

Documentation, training, support transition

Ready to Build a Data Foundation You Can Trust?

Let's discuss how to turn your fragmented data into a governed, reliable asset.

Book a 30-Minute Call

Let's Talk About Your Commercial Data Challenges

Book a 30-minute call to discuss how AI can help your CPG team work smarter