HockeyStack Data Foundation - Atlas

When you read “What Will My Data Look Like?”, you saw how HockeyStack transforms siloed actions and Touch-points into a unified, action-based model. Now let’s go “under the hood” and explore how Atlas - HockeyStack’s core data foundation - makes that transformation possible step by step.

Each layer of Atlas builds confidence in the next: raw ingestion, cleaning, governance, and activation all operate under the same unified model. The goal isn’t just visibility - it’s Trust in the data.

Stage 1: Data Acquisition

Atlas begins where your data lives: it ingests raw, structured and unstructured data from every system touching your customer journey - CRMs, marketing automation platforms, ad systems, website activity, offline events, PLG motion product telemetry, and data warehouses.

This means nothing is left behind - every touchpoint object, field, event, and record is eligible for transformation. Before analytics even begins, siloed data is centralized.

For customers already centralizing data in Snowflake, Amazon s3, BigQuery, Google Sheets or other cloud data stores - Atlas can pull historical website actions, custom actions, breakdown properties, spend data, or metadata - from those sources.

Importantly, much of our tracking begins long before a visitor ever self-identifies with anonymous device IDs through fingerprinting technology.

Most internal reporting starts at the point of conversion. Atlas extends visibility to the entire pre-conversion journey - tracking anonymous traffic, multi-site engagement, and device-level pre-conversion tracking at the account level that traditional CRM or warehouse models can’t capture.

Stage 2: Deduplication + Identity Resolution

Atlas is built on the assumption your data is messy.

No CRM is ever perfectly clean. HockeyStack was built from the ground up to handle messy, incomplete, and legacy data structures. We design for imperfection - anticipating inconsistencies, duplicates, and misaligned definitions - and apply built-in validation and governance standards as part of every onboarding.

Your data likely has duplicates, varying naming conventions, and disconnected records. Atlas resolves that by normalizing everything into four canonical building blocks:

• Person - Who performed the action

• Company - Where they’re associated

• Action - What they did

• Metadata - Campaign details, cost, timestamp, and context

We unify every touchpoint at two levels: the account level, using unique domains, and the individual level, using email addresses.

With those building blocks, Atlas ensures that multiple versions of the same user or account collapse into a single, accurate identity across systems.

Once deduplication is resolved, Atlas aligns every action on a common timeline - ad impressions, website events, form submissions, sales touches - all merged under a single identity.

Some questions you can now answer instantly:

• “Show me every action John Smith has taken.”

• “How much have we spent reaching ABC Corp?”

• “How many unique people from ABC Corp have engaged?”

All of those touch-points from different systems are now on a single timeline, without you having to go stitch it together every time there's a new business question or time period of interest.

This turns fragmented, time-ordered events into one coherent data fabric.

Stage 3: Data Categorization & Governance

With your data unified,we help you define core Hockeystack data primitives. Atlas doesn’t impose a rigid model - you set:

• Funnel stages (e.g. MQL → SQL → Opportunity)

• Touchpoint types (e.g. organic vs. paid, campaign groups)

• Breakdowns & segments (ICP, geography, ABM tiers)

These definitions live inside your instance as self service editable rules, so if your marketing team adjusts how “Pipeline Created” or “Organic Search” is defined, every dashboard and report automatically reflects it in real time.

You’re going to define funnel stages according to your business definitions, you don’t have to dull your definitions down for the system.

Changing logic, definitions, or breakdowns doesn’t require manual rework - the data model stays flexible and self-service through point and click.

Stage 4: Action Layer — From Data to Insight

Atlas isn’t just a data store. It powers the action layer:

• Dashboards & Account Intelligence UI

• Odin (AI Analyst) — automated query + interpretation

• Nova (Sales AI Assistant) — real-time engagement

• Workflows — trigger-based automation

AI isn’t hallucinating off the raw data. It’s generating reports from modeled data, with visibility into reasoning for reports that are built for each specific question, and then interpreting them - so the logic chain is grounded in your data.

This layer turns your unified model into live insight and decision automation.

Build vs. Buy Efficiency

Many teams already have a data warehouse and even internal Marketing Attribution, Funnel, and Customer Journey reporting. Atlas isn’t a replacement for that foundation - it’s the layer that completes it.

• Full-Funnel Capture: Internal models typically start post-conversion. Atlas fills the blind spot by tracking anonymous and pre-lead activity, creating the first unified view of awareness-to-revenue.

• Cookieless Tracking: Fingerprinting and reverse-IP identity mapping maintain long-term accuracy where cookies expire.

• Cross-Domain & Multi-Product Visibility: Atlas unifies product lines, microsites, and brand domains under one account identity.

• Self-Service Flexibility: Marketing, sales, and leadership can ask and answer questions directly - without data-team dependencies or static dashboards.

• Time-to-Value: Customers often reach validated insights in weeks, not months, because Atlas layers on top of your existing definitions rather than rebuilding them.

Across HockeyStack customers, Atlas surfaces an average of 80–120 Touch-points per opportunity, revealing 4–6x more journey data than CRM-only or silo'd models. This expanded visibility helps teams reallocate up to 20–30% of wasted ad spend. It also unites sales and marketing reporting around a single source of truth and provides forward-looking guidance on where to invest next.

Summary

Atlas is the Data Foundation that turns what you read in “What Will My Data Look Like?” into a living, governed, and actionable model. It:

• Ingests all your systems

• Resolves duplicates and identities

• Aligns every action on a clean timeline

• Lets you define funnels and logic

• Activates insights via AI and workflows

• Embeds verification and governance from day one

From here, the next topic - how HockeyStack handles complex account hierarchies - details how Atlas merges identity and touchpoint insight across nested, duplicate, dirty Account Hierarchies seamlessly.