B2B attribution platforms show you which touchpoints actually drive revenue, but the architecture underneath determines how adaptable, scalable, and intelligent your insights really are. HockeyStack and Dreamdata both solve the attribution problem; however HockeyStack is built for enterprise‑level complexity, offering customizable data models, AI‑powered analysis, and governed flexibility that evolve with every GTM motion.

This comparison breaks down how each platform handles multi-touch attribution, identity resolution, data processing, AI capabilities, and enterprise requirements so you can determine which approach fits your GTM stack.

What Sets HockeyStack and Dreamdata Apart

Dreamdata operates as a warehouse-first platform built on BigQuery, which means it excels at deep analysis and data export for teams that already have strong data engineering resources. 

HockeyStack, on the other hand, emphasizes customizability, enterprise scalability, and AI‑powered intelligence through its proprietary Atlas data foundation. It’s designed for complex enterprise teams managing fragmented, multi‑system data; delivering real‑time insights, unified governance, and adaptable analytics models that evolve with your GTM motion.

The practical difference shows up in three key areas:

1. Dreamdata uses batch processing, so there's a delay between when something happens and when you see it in reports. HockeyStack processes data in real-time, which means conversions appear instantly. 
2. Dreamdata gives you warehouse-level control for custom analysis, while HockeyStack prioritizes an easier interface that marketing and RevOps teams can use without SQL. 
3. HockeyStack builds cookieless journey mapping into its core architecture, whereas Dreamdata focuses more on company-level identification.

HockeyStack Platform Overview

HockeyStack is an AI GTM intelligence platform that unifies data from CRMs, marketing automation, ad platforms, web activity, product usage, and customer success tools. At its core sits Atlas, a proprietary data foundation that ingests raw, structured, and unstructured data into a single model.

What makes Atlas different is its assumption that your data is imperfect. Enterprise CRMs contain duplicates, conflicting field values, and disconnected contacts. Atlas automatically normalizes, deduplicates, and reconciles this messy data without manual cleanup. The platform also captures anonymous engagement before form fills, which typically reveals four to six times more touchpoints than CRM-based models alone.

Dreamdata Platform Overview

Dreamdata takes a warehouse-first approach, meaning it unifies your GTM data directly in your existing data warehouse, primarily BigQuery. This architecture gives data teams unrestricted access to export and query attribution data however they want.

The trade-off is that batch-based processing introduces latency. Insights aren't available in real-time because the system waits for batch jobs to complete before updating reports. For teams with strong data engineering capabilities who want to own their attribution data directly, this approach works well. For teams that want instant feedback loops, the delay can be limiting.

Multi-Touch Attribution Models and Methodology

Multi-touch attribution assigns credit across all the touchpoints that influence a deal, rather than giving all credit to the first or last interaction. Both platforms support the standard models, though they differ in flexibility and speed of implementation.

Attribution Models Available in HockeyStack

HockeyStack supports first‑touch, last‑touch, linear, time‑decay, position‑based, and fully custom attribution models, and its key advantage is the ability to switch between models instantly, without reprocessing data, waiting on engineering, or disrupting analysis.

Beyond standard models, HockeyStack allows teams to define their own attribution rules, funnel stages, and channel groupings. When any definition changes, every dashboard, report, and AI query updates automatically. This flexibility matters because attribution logic often evolves as your understanding of the buyer journey deepens.

Attribution Models Available in Dreamdata

Dreamdata offers similar model options, supporting custom B2B attribution needs. Your data team can export, slice, and model attribution data for analysis in your own warehouse.

Model changes typically require batch reprocessing, however, which introduces a delay before updated results appear. Dreamdata provides analytical flexibility, provided you’re comfortable with the trade-off.

Pre-Conversion and Anonymous Touchpoint Handling

Here's where the platforms diverge most significantly. B2B buyers spend considerable time researching before they ever fill out a form, and capturing that anonymous activity determines how complete your attribution picture actually is.

HockeyStack captures anonymous account-level web engagement and ties those early touches to eventual pipeline and revenue. For HockeyStack, cookieless tracking is the foundation of how the platform works. 

Dreamdata's cookieless tracking is more limited—it identifies companies visiting your site but doesn't map complete user journeys before identification. This gap means Dreamdata may miss the research phase that often shapes buying decisions.

Identity Resolution and Anonymous Visitor Tracking

Identity resolution is the process of connecting touchpoints to the right accounts and people across systems and devices. Enterprise data is inherently messy, and how each platform handles that messiness directly affects attribution accuracy.

HockeyStack Account Stitching and Deduplication

Atlas assumes your data is imperfect by design. The platform automatically applies normalization, deduplication, and reconciliation across all connected systems without requiring manual cleanup.

Consider what enterprise CRMs actually look like: duplicate records, conflicting field values, inconsistent naming conventions, and contacts disconnected from their accounts. Atlas resolves these issues automatically, unifying identities at both the person and account level. This means you see the complete picture of how buying committees engage with your brand, not just fragments scattered across systems.

Dreamdata Tracking Scope and Limitations

Dreamdata relies more heavily on known contacts and company identification. Once a visitor becomes known, the platform tracks their journey effectively. However, the anonymous research phase—before any form fill—is where gaps appear.

This limitation matters because the research phase often includes the touchpoints that actually shape buying decisions. If you can't see what happened before someone identified themselves, you're missing a significant part of the story.

Touchpoint Visibility and Volume Differences

• Anonymous web engagement: HockeyStack captures pre-identification activity through fingerprinting; Dreamdata focuses on company-level identification after the fact
• Cross-device stitching: HockeyStack connects the same buyer across devices; Dreamdata's cross-device capabilities are more limited
• Account-level rollup: Both platforms aggregate individual touches to buying committees, though HockeyStack typically surfaces 80 to 120 actions per opportunity

Data Architecture and Real-Time Processing Performance

The database technology underneath an attribution platform shapes everything from query speed to cost predictability. This isn't just a technical detail but foundational architecture that affects how quickly your team can act on insights and how much you'll pay as usage grows.

ClickHouse vs BigQuery Technical Comparison

HockeyStack runs on ClickHouse, a columnar database designed specifically for real-time analysis. ClickHouse processes millions of rows per second with sub-second query latency, even on billions of rows. Companies like OpenAI, Anthropic, Uber, and Cloudflare use the same technology for applications that require instant data access at massive scale.

Dreamdata uses BigQuery, which was built for  batch analysis rather than  continuous high-frequency updates.  Its batch-based architecture means data isn't available in real-time.

Query Speed and Data Freshness Benchmarks

With HockeyStack, streaming updates instantly reflect conversions, usage, and engagement. You see what's happening now, not what happened hours ago. This matters for teams running real-time routing, automation, or making quick decisions based on campaign performance.

BigQuery's batch approach means waiting for jobs to complete before data appears in reports. For some use cases, this delay is acceptable. For teams that want instant feedback loops, the latency breaks decision-making workflows.

Cost Behavior at High Query Volumes

BigQuery charges based on query volume, which can spike unpredictably as your team runs more analyses. ClickHouse's columnar, compressed storage keeps costs stable even as data volume increases.

This predictability matters for enterprise budgeting. You don't want attribution costs to surprise you at quarter-end because your team asked more questions than expected.

AI Capabilities for B2B Marketing Attribution

AI features in attribution platforms are only valuable if they produce accurate, actionable insights. The architecture behind the AI determines whether you can trust the outputs for real business decisions.

HockeyStack Odin AI Analyst Features

Odin is HockeyStack's AI analyst that answers GTM questions in natural language. Instead of producing charts you have to interpret, Odin delivers metrics, analysis, and recommendations directly using your funnel definitions, attribution rules, and business logic.

You can ask questions like "Which Q3 assets influenced enterprise pipeline the most?" or "What actions could accelerate deals in mid-market?" and get specific, actionable answers. Over 60% of HockeyStack customers actively use Odin on a monthly basis and nearly 90% of questions are answered without clarification.

Accuracy Validation and Hallucination Prevention

Enterprise teams can't afford AI that makes up numbers. HockeyStack addresses this concern through a multi-layered validation approach to ensure utmost accuracy:

• Multi-agent orchestration: Specialized agents handle different types of analysis rather than relying on a single LLM guess
• Deterministic code execution: Numbers are generated by actual code running against real data, not LLM reasoning
• Evaluation agent: Every insight is validated against source datasets before reaching users

Further, customer data is never used to train global models. AI features are opt-in, and customers retain full ownership of their data and outputs. Every number Odin produces can be traced back to source datasets.

Integration Ecosystem and Supported Data Sources

HockeyStack's native support for Salesforce custom objects is particularly important for enterprise configurations. Many organizations store critical data in custom objects that standard integrations miss entirely.

Reporting Flexibility for Marketing and Revenue Teams

The best attribution platform is one your team actually uses daily. Self-serve capabilities determine whether insights stay locked with the data team or spread across the organization.

Self-Service Analytics for Non-Technical Users

HockeyStack enables marketing and RevOps teams to slice data across geography, product, channel, and ACV without writing SQL or waiting for engineering support. Cohort analysis, lift measurement, funnel visualization, journey mapping, and segmentation tools are all built in.

Dreamdata's warehouse-first approach offers more power for data teams who want to write custom queries. However, this flexibility may require technical involvement for day-to-day analysis, which can create bottlenecks.

Business-Defined Attribution Logic and Governance

Unlike rigid attribution tools that forces teams into predefined funnels or channel logic, HockeyStack allows you to define your own funnel stages, channel groupings, touchpoint categories, and attribution rules.

The moment a definition changes, every dashboard and Odin query updates instantly—without reprocessing, ETL work, or engineering tickets. This flexibility lets attribution logic evolve alongside your understanding of the buyer journey.

How to Choose Between HockeyStack and Dreamdata

The right choice depends on your specific situation, team capabilities, and data environment. Here are the key questions to consider:

• Data complexity: How messy is your CRM, and how many systems feed into attribution?
• Real-time requirements: Do you require instant feedback loops for routing and automation?
• Team technical capacity: Who will use the platform daily, and what's their SQL comfort level?
• Budget predictability: Can you absorb variable query-based costs, or do you prefer predictable pricing?

When Dreamdata May Fit Your Needs

Dreamdata works well for smb teams with an existing BigQuery investment, strong data engineering capabilities, and squeaky clean data. If you want deep warehouse-level control and unrestricted data export for custom analysis, Dreamdata offers that flexibility.

Teams that already have data engineers comfortable with BigQuery and want to own their attribution data directly will find Dreamdata's architecture familiar and capable

When HockeyStack Is the Stronger Choice

HockeyStack fits enterprise teams with complex buyer journeys and real-time requirements.   If you want complete visibility across anonymous and known touchpoints without having to build custom pipelines and clean all your data first, HockeyStack delivers that capability out of the box.

The platform also makes sense for organizations where marketing and RevOps teams want to self-serve their own analysis without depending on data engineering resources.

Why HockeyStack Delivers Complete B2B Revenue Attribution

HockeyStack provides the foundation for enterprise-grade attribution: 

• Unified visibility across marketing, sales, product, and partner channels
• Real-time processing that keeps pace with GTM operations
• AI-powered insights validated against source data
• Infrastructure that scales without unpredictable costs.

The platform eliminates fragile internal pipelines, removes dependency on individual engineers, and ensures continuous, governed data readiness. For teams ready to see their complete buyer journey, book a demo to see HockeyStack in action.

Frequently Asked Questions About HockeyStack vs Dreamdata

Can HockeyStack and Dreamdata integrate with Salesforce custom objects?

HockeyStack natively supports Salesforce custom objects, which is critical for enterprise configurations where standard objects don't capture the full picture. Dreamdata focuses primarily on standard objects, which may limit visibility for complex Salesforce implementations.

How do HockeyStack and Dreamdata handle multi-brand enterprise account structures?

HockeyStack's Atlas data foundation supports heterogeneous hierarchies, showing each brand's journey separately while rolling up meaningfully at the parent level. This approach avoids over-deduplication that distorts how buyers actually behave. Dreamdata has more limited hierarchy handling for multi-brand organizations and global subsidiaries.

What technical expertise is required to use HockeyStack or Dreamdata daily?

HockeyStack is designed for self-serve use by marketing and RevOps teams without SQL knowledge. Dreamdata's warehouse-first approach may require more technical involvement for custom analysis and report building, making it better suited for teams with dedicated data engineering resources.

Can HockeyStack or Dreamdata attribute offline events and partner channel activities?

HockeyStack ingests offline events and partner data through its unified touchpoint model, treating them the same as digital touchpoints. Dreamdata offers partial support depending on how data flows into the warehouse, which may require additional configuration.

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