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This is the second post in the Future of Revenue series. Check out the other posts below. Updated 5/29/25.
Today I'm going to show you how to create a single source of truth for all of your GTM data.
If you're part a GTM team in 2025, you're probably in one of two camps: either you have yet to collect and organize your team’s data and you feel like you’re flying blind, or you experiencing data overload - you’re swimming in information but drowning in noise. Every tool you add to your stack claims it will unlock the golden insight, but instead leaves you juggling fragmented views that each only paint one piece of the picture1.
We’ve heard the promises for years - a holistic 360-degree view of your accounts and leads - but the results have been lackluster and it’s started to feel more like a mirage than a realistic goal. Even if you’ve successfully unified your data (which for some teams can take years), and you have a big beautiful data warehouse, how do you actually leverage it to grow revenue in your day-to-day? There's always been a key piece missing - until now.
The rise of large language models (LLMs) has allowed for an incredible breakthrough - now AI can organize and interpret millions of datapoints to provide nuanced, actionable insights and personalized content at scale. You may have already tried some of these AI tools and been underwhelmed - AI is not a silver bullet!
AI without data is shallow and flat. Data without AI is idle and squandered.
The magic is in how the technology is integrated ✨.
In this series, we show you what a state of the art signals-based GTM automation system looks like, starting with the data architecture in this post.
Building a revenue automation system starts with robust data foundations. After all, a single source of truth that's empty is about as useful as a Tesla with no charge.
There’s a world of data out there, and below is an overview of essential types to consider. But don't worry - you don't need to catch 'em all!
The specific data sources you should prioritize will depend on several key factors, including:
Experimentation is key -find the signals and lead fit indicators most valuable for your unique business. For example, if you are a heavily sales driven organization with long sales cycles, maintaining high quality call transcripts, email history, and notes may be especially beneficial.
* Avina comes with integrations to all of the above data types and sources, except for those marked with a star.
Not all data is created equal. Knowing that a visitor spent 15 minutes on your pricing page isn’t groundbreaking intel, but knowing precisely what caught their attention during their session can make or break a sale.
At Avina we take the web interaction data a step further by also capturing the exact text that visitors read, actions like scrolling and clicking, and screenshots. We then resolve anonymous visitors at the contact and account level with a waterfall of data providers. With the help of AI, each session transforms into a detailed narrative of the visitor’s interests and persona:
Now that we’ve identified the data sources we want to integrate, the next step is to build the data pipelines and transformations to unify everything. Clean, unified data is the fuel that powers effective playbooks, workflow automations, and account-based marketing. On top of that, AI requires tidy data, and will reward you with the most effective recommendations and personalization.
The data unification process involves three main steps: Integration and Ingestion, the Unification Pipeline, and Enrichment:
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In order to start pulling in the data we need, we’ll need to integrate with those data sources through their application programming interfaces (APIs). For example, Hubspot, Google Ads, Outreach, and many other platforms offer public APIs.
If the thought of building countless integrations makes you want to book a one-way ticket to Tulum, I've got good news. ELT Tools like Airbyte, Fivetran, and Ampersand can streamline the heavy lifting - we use them extensively at Avina.
For custom web data without available APIs, you can use web scraping to pull it directly into your systems.
Once you have those integrations built out, you’ll typically store the data in a data warehouse or database like AWS Redshift or MongoDB.
The next step is to take all the data that we’ve pulled in and unify it together - the challenge is that each source structures data differently and contains duplicate records not just within each system but also across them.
The key is to first define a unified data model that is flexible enough to accommodate the idiosyncrasies of each source, then build a data pipeline that transforms data from each system into the unified schema. We’ll want a unified model for each of these entities:
The data pipeline should do the following:
At Avina, we make heavy use of the Python library pandas, which provides an extensive set of data manipulation utilities that make our lives much easier. We then orchestrate tasks using celery and AWS ECS to run transformation for each customer. Other excellent tools for transformations and task orchestration include dbt, Airflow, and Hatchet.
The third and final step in building our data architecture is to fill in data gaps using a waterfall of providers.
In order to give our GTM system the most complete context and allow us to close the loop by getting in touch with our leads, we’ll want to acquire the following information wherever it’s missing:
Since it’s impossible get 100% accuracy with any one data provider, we recommend using a pool of data providers (at Avina we use People Data Labs, Wiza, etc). It’s also more economical to enrich the information only when you need it - you don’t need to enrich the emails of all 500k contacts in your CRM if you are not going to email them right away, so having a system that does real-time enrichment is helpful.
Congratulations! With that we’ve completed the data foundation for our cutting-edge GTM engine by establishing a powerful single source of truth for all of our leads and target accounts 🎉. If that sounded like a lot of work, well Christmas came early and Santa just showed up with…
Avina is unique among GTM automation platforms in that it automatically creates a full-fledged customer data platform (CDP) for you, taking all of this work completely off your plate. The CDP ensuring that every layer of intelligence applied on top of it is working with the most complete, accurate, and fresh information. The system syncs and unifies all of your data sources every 2 hours.
For some, hearing CDP might give them PTSD from spending months wrangling dbt materializations and schema mappings (*cough* Bizible *cough* Salesforce Data Cloud). With Avina, implementation takes a full… brace yourselves… 30 min! Don’t believe me? Try it out yourself.
On top of feeding the most complete context to the AI, you have direct access to every touchpoint in the dashboard and direct access to the database itself.
On the other hand, if a tool does not integrate with these data sources or unify them effectively, it’s simply not possible for it to have all the context on your accounts. Any insights, scores, recommendations, and personalization will inevitably miss the mark if they are not aware of key information like deal history, past communications, and marketing engagement.
While Avina is plug and plug and comes with the default best practices out of the box, we know one size doesn’t fit all. On the CDP side alone, these capabilities range from custom deduplication logic, reading custom columns and objects, and data filters, to custom new hire and social keyword monitoring, and custom call transcript extraction.
For a detailed look into all of the customization available, stay tuned for a dedicated post where we explore the full range of possibilities.
An added benefit of having a single source of truth is that it lays the groundwork to align the sales and marketing teams.
The best companies think of GTM as one continuous motion - companies that align their sales and marketing teams see 36% higher customer retention and 38% higher win rates. True alignment can only be achieved when the entire revenue org speaks the same language, measures the same metrics, and coordinates its actions around the same data.
Now that we’ve laid a rock-solid data foundation - a unified, enriched, and fully customizable source of truth - it’s time to put that data to work ⚡️. Clean data sitting in a warehouse doesn’t close deals. The real value comes when that data brings the perfect leads to your doorstep, fuels the right plays, drives personalized messaging, and shows up at the exact moment your reps need it most.
In the next post, we’ll show you how to make that leap - from data to decisions. We’ll explore how AI can transform raw data into clear signals and guidance: who to target, what to say, and when to say it. You’ll see how we extract deep insights from customer behavior, spot intent early, and surface the highest-impact actions across your pipeline.
The Future of Revenue: From Chaos to Clarity
Part 1: The Single Source of Truth (you are here)
Part 2: Finding Signal in the Noise (coming soon)
Part 3: Revenue on Autopilot (coming soon)
Part 4: The New GTM Frontier (coming soon)
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Avina is a signals-based GTM automation platform for B2B teams that uses cutting-edge artificial intelligence to convert data into revenue.
Ready to move from chaos to clarity? Book a demo with us here.
Have questions, feedback, or ideas? Reach out to hello@avina.io - we would love to hear from you!
Here’s what our customers are saying:
“Avina has completely changed the way I work. Before, I felt like I was shooting in the dark to get responses, but now I have the highest propensity buyers being delivered to my doorstep, and I can focus on closing those deals.”
- James, Director of Strategic Partnerships, Ingenious (Case study)
“In sales, it’s all about getting more quality at-bats. Products that help do better cold outbound are faster horses - Avina’s warm inbound alerts is a car.”
- Ethan Pope, VP Revenue Operations, Nitrogen Wealth
