Data Infrastructure for B2B: Why Your Numbers Don't Match (and How to Fix It)

Every B2B company hits the same wall around $2M–$5M ARR. Marketing says CAC is $180. Finance says it's $340. Sales says pipeline is $2.4M. The CEO saw a different number in last month's board deck. Everyone has a dashboard. None of them agree.

This isn't a math problem. Your teams aren't bad at calculations. It's a plumbing problem. Your customer data lives in six different tools, CRM, billing, product analytics, support tickets, marketing automation, and a spreadsheet that somebody created for "temporary" tracking 18 months ago. Each tool has its own definition of a "customer," its own way of calculating revenue, and its own reporting dashboard built by a different person at a different time.

The cost of fragmented data

Fragmented data doesn't just cause arguments. It has concrete business costs:

Bad decisions. If marketing is optimizing for a CAC that's calculated differently from what finance reports to investors, someone is wrong, and money is being misallocated.

Slow reporting. When pulling a board report requires two engineers and a week of SQL queries, you're burning senior time on a task that should take minutes.

Broken AI. If you're building AI features, and in 2026, you should be, fragmented data means your agents and models are working from incomplete, inconsistent, or stale information.

Fundraising risk. When an investor asks for your unit economics during due diligence and you need three days to produce the numbers, they notice. Accurate, real-time metrics signal operational maturity.

The modern B2B data stack

For most B2B companies under $50M ARR, the right data infrastructure looks like this:

Data warehouse: BigQuery or Snowflake. This is your single source of truth, the one place where all your data lives in a standardized format.

ELT pipelines: Fivetran or Airbyte. These tools pull data from your source systems (CRM, billing, product, support, marketing) into your warehouse automatically.

Transformation: dbt (data build tool). dbt models clean, standardize, and transform raw data into analytics-ready tables. This is where you define "what is a customer?" once, and every dashboard inherits that definition.

BI layer: Metabase or Looker. Dashboards that connect directly to your warehouse. Executive overview, marketing attribution, sales pipeline, product usage, each team gets the views they need, all from the same data.

Orchestration: Dagster or Airflow. These tools schedule and monitor your data pipelines, alerting you when something breaks.

The total cost for this stack is typically $2K–$5K/month in tooling, plus the implementation effort. For what it gives you, real-time, trustworthy metrics across every function, it's one of the highest-ROI investments a B2B company can make.

Where to start

Start with the data that's most broken. Usually that's either: (a) revenue metrics, because billing and CRM don't agree, or (b) marketing attribution, because nobody can connect marketing spend to closed deals.

Pick one problem. Connect the relevant data sources to your warehouse. Build the dbt models that define the truth. Create one dashboard that replaces the conflicting spreadsheets. Get your leadership team looking at the same numbers every Monday.

Then expand. Add product usage data. Add support ticket analytics. Connect the AI features you want to build. Each layer builds on the same foundation, and each one becomes easier because the infrastructure is already in place.

Related: AI Agents for B2B → · Revenue Operations → · Our Data practice →