Is the data warehouse the new system of record?

Living in a data warehouse first world

Isaac Pohl-Zaretsky
March 24, 2022
Is the data warehouse the new system of record?

Modern data infrastructure looks very different in 2022 than it did even 10 years ago. Today, top-performing SaaS teams leverage “the Modern Data Stack,” a combination of tools that make managing your enterprise data simple. 

In the last decade, data has become a major driver of business value, which is great for data nerds like me. The need for management, analysis, and operationalization of data has forced every business, from SaaS to retail to industrials, to take a look at optimizing data infrastructure to drive better outcomes for the business. 

Through this process, best practices have started to emerge, including the dominance of the modern data warehouse over other data stores. 

In this post, I’m going to explore the data warehouse, its place in the Modern Data Stack, and why I (and the team at Pocus) believe it will be the system of record for modern GTM teams. 

What is a data warehouse? 

Before we dive in, let’s define some basics. 

What is a data warehouse (DWH)? A data warehouse is a central place to store data from various sources for the purpose of doing further analysis or reporting. DWH contains both current and historical data in a structured format, making it easier to run queries (usually in SQL) to answer your business questions.

Examples of popular data warehouses include: 

  • Snowflake 
  • Google BigQuery
  • Amazon Redshift

Data warehouse’s rise in popularity

After speaking with hundreds of business leaders, it’s clear that data-driven decision-making directly contributes to positive business outcomes. That might seem obvious, but in practice, the promise of data-driven and the practical realities of enabling such a system are very different. 

Major shifts in GTM strategy, data architecture, and business goals have accelerated the urgent need for many companies to invest in data infrastructure. 

Broadly there are three trends that have led to this shift:

  1. Product usage data and data, in general, plays a bigger role in driving business outcomes 
  2. It is increasingly cheap and cost-effective to push information from disparate sources to a DWH and do transformations later 
  3. “Modern data stack” is now seen as best practice  

Product usage and data-driven teams 

In speaking with hundreds of GTM leaders at PLG companies, we have noticed a massive need for data, specifically product usage data to enable better GTM decision-making. 

Product interactions are happening earlier in the user journey and that data is increasingly stored in the data warehouse. Why the data warehouse? Data warehouses make it easier for the data team to do additional analysis in a Business Intelligence tool. With more GTM teams needing access to this data, we expect data warehouses to continue to rise in popularity.  

Cheap storage and ELT 

Another trend pushing the popularity of data warehouses is the dramatic decrease in cost associated with cloud storage.

A decade ago, teams would never dream of dumping all of their unfiltered and unprocessed data from various sources into a cloud data store. The cost of putting data that may have zero value in a central repository was too high. Today, the economics are such that it is relatively inexpensive to push all of your data into a central repository like a data warehouse, and process that data once loaded. 

This process is called ELT or extract, load, transform. Instead of delaying the migration of data into the DWH to do some preprocessing and transformation, modern tools like dbt allow you to transform the data once it’s already loaded into the DWH. 

This makes it easier for data teams to push data to the DWH and make it accessible to business teams for their use cases, faster. 

Modern Data Stack

Okay, stay with me as this next trend requires a bit of technical nerding out. 

First, let me define what I mean by the Modern Data Stack (MDS). 

MDS has become the go-to cloud data architecture for top-performing SaaS businesses. It consists of the following layers:

  • Ingestion: Fivetran, Stitch
  • Warehousing: Snowflake, BigQuery, Redshift
  • Transformation: dbt
  • BI: Looker, Mode, Chartio
  • Custom Business Apps: Finance, Growth, Sales, etc. 

How and why did MDS become the go-to? In my opinion, it has everything to do with modern data warehouses and their structure. The folks at dbt put it better than I could in this article, “Looker on Postgres is fine, but Looker on Redshift is awesome.”

The problem: GTM teams can’t access the data warehouse 

In most companies, the DWH is not the only system of record or single source of truth.

I’ve talked about this in a previous post, where I go into detail about what we’ve seen at most PLG companies (especially those adopting a Product-Led Sales approach), which is two systems of record - the data warehouse and the CRM. 

Here’s what that might look like:

In this example, you see the data warehouse is the system of record for product data and data from the website, which then gets piped into various tools that are primarily for product, data, and growth teams. In a separate silo is the system of record for GTM teams, the CRM, which powers tools for marketing, sales, and customer success. These two tools don’t talk to each other. 

That is the problem - in a modern Product-Led Sales go-to-market, the sales, marketing, and customer success teams need better access to valuable data that lives in the data warehouse. 

Data warehouse vs. CRM 

Without one system of record, a single source of truth can’t exist.

In terms of the database, either the CRM or data warehouse has to win…

We believe that the data warehouse will become the system of record for modern GTM teams over time…

  1. Data Architecture: CRMs don’t currently don’t support the kind of data, flexibility, and volume PLG companies need. DWH supports a flexible approach to data that can handle a large volume and variety of data. 
  2. Maintenance Costs: CRMs penalize you with high costs as you start to bring in even a modest amount of product data volume. It has become increasingly inexpensive to store data in your DWH. 
  3. Source of truth: CRMs are currently only a source of truth for a very specific subset of customer data for sales teams. As we’ve seen with PLG companies, non-technical teams will need access to data beyond the CRM. DWH enables better alignment across an organization as the single source of truth.

Data warehouse: The preferred system of record

Today, PLG companies are already facing the limitations of the CRM’s data model. The rigid hierarchy of contacts, leads, and accounts doesn’t always fit a PLG GTM motion. It’s why so many PLG companies have already adopted data warehouses and are increasingly treating it as their primary system of record. In the future, as every SaaS business introduces product touch points earlier in the user journey, they will also see more data about users moving to the data warehouse. 

We believe that over the next decade, the data warehouse will become the system of record for SaaS businesses, for the ability to handle any data model and the flexibility to build robust applications on top of that data. 

Future Modern Data Stack 

As the data warehouse becomes the preferred system of record, we see the Modern Data Stack evolving to include more than just business intelligence tools. 

Enabling GTM teams with the Modern Data Stack

Today, the Modern Data Stack is the best thing since sliced bread for data teams, but in the future, the MDS will be just as important to non-technical teams like sales, marketing, operations, and customer success. So the entire company can realize the benefits of this data infrastructure. 

Today, non-technical teams must rely on engineering and data resources to access valuable data from the DWH, our vision for the future is an easier interface into the value of the MDS for non-technical users - an application layer.

This application layer needs to:

  • Be flexible to and accommodate any data model
  • Enable non-technical teams to build out their use cases without code (dashboards, scoring models, workflows)
  • Deliver insights about data that is transparent and explainable

Pocus’ Product-Led Sales Platform 

We’re building Pocus to enable this future vision, for all the reasons above. The GTM motion of every SaaS business (not just native PLG companies) is changing. As more data that is important to the success of go-to-market teams lives in the data warehouse, there needs to be an intuitive, easy-to-use, and powerful interface for those teams to use. 

Pocus becomes that application layer on top of the Modern Data Stack to operationalize the goldmine of information that is otherwise trapped in the data warehouse.

The future of modern data infrastructure is flexible and no-code so we’re building Pocus’ platform on those two core principles. Enabling GTM teams from sales and marketing to customer success and growth to build dashboards, scoring models, and workflows that fit their needs and goals. 

Join our beta 

Want to try Pocus? We’re still working through our waitlist, so make sure to sign up here. Do you absolutely NEED Pocus? Shoot us an email to and tell us why. 

Further reading and sources:

  • Everything from the folks at dbt on the Modern Data Stack is great but especially this piece
  • Emerging Architectures for Modern Data Infrastructure via a16z
  • Reverse ETL vs. ELT via Hightouch
  • OLTP vs. OLAP via Stich
About the author
Isaac Pohl-Zaretsky
CTO & Co-founder @ Pocus
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