“We need a data warehouse.”

I hear this from growing businesses, usually after reading about analytics and data-driven decision making. The phrase sounds sophisticated. The need might be real, or it might be overkill.

Let me help you figure out which.

What a Data Warehouse Actually Is

A data warehouse is a central repository where data from multiple sources is combined for analysis.

Your CRM has customer data. Your accounting software has financial data. Your marketing tools have campaign data. Your e-commerce platform has sales data.

Each lives in its own system. To answer questions like “which marketing campaigns drive the most profitable customers,” you need to combine data across systems.

That’s what a data warehouse does. It pulls data from multiple sources into one place where you can query and analyze it together.

Signs You Might Need One

You’re Exporting and Combining Data Manually

If someone exports CSVs from three systems weekly and combines them in Excel to build reports, that’s a sign. You’re doing data warehousing manually.

Your Questions Span Multiple Systems

“Which customers acquired from Facebook ads have the highest lifetime value?” That requires customer data, advertising data, and purchase history. If these live in different systems, answering is hard.

Reports Take Too Long

When building a monthly report takes two days because of data gathering and formatting, there’s a problem. A warehouse with connected dashboards can make that instantaneous.

You’re Making Decisions on Gut Rather Than Data

Not because you don’t have data, but because getting to it is too hard.

Signs You Don’t Need One

Your Data Lives in One System

If you’re an all-HubSpot shop (CRM, marketing, CMS, analytics), HubSpot’s reporting might be enough. No integration needed.

Your Questions Are Simple

Basic questions that single tools answer don’t need a warehouse. “How many deals did we close?” CRM answers that. “What’s our revenue this month?” Accounting answers that.

You Don’t Have Analyst Capacity

A warehouse is useless without someone who can use it. If you don’t have a data-focused person (or plan to get one), the warehouse will sit unused.

Your Business Is Very Small

Under 20 employees, under $2M revenue, simple business model? Manual reporting probably works. The overhead of a warehouse isn’t worth it.

The Modern, SMB-Accessible Options

Traditional data warehouses were expensive and complex. Modern tools changed that.

Cloud Warehouses

BigQuery (Google): Free tier available. Pay for what you use. Handles massive scale.

Snowflake: Usage-based pricing. Separates compute from storage. Popular and powerful.

Amazon Redshift: AWS’s warehouse. Integrates with AWS ecosystem.

For most SMBs, BigQuery or Snowflake at small scale is affordable (maybe $50-200/month for modest usage).

ETL/Integration Tools

Getting data into the warehouse requires ETL (Extract, Transform, Load) tools:

Fivetran: Connects to hundreds of sources. Simple setup. $$$

Airbyte: Open source. Self-hosted option. Cheaper, more work.

Stitch: Mid-tier pricing. Acquired by Talend.

These pull data from your CRM, advertising, accounting, etc., and load it into the warehouse.

BI/Analytics Tools

Query the warehouse and build dashboards:

Looker Studio: Free. Connects to BigQuery easily. Good for basic dashboards.

Metabase: Open source. Self-host or cloud. More capable.

Tableau/Power BI: Enterprise features. Higher cost.

A Realistic SMB Data Stack

If you decide to proceed, here’s a reasonable starting point:

• Warehouse: BigQuery (start free, pay as you grow)
• ETL: Airbyte or Stitch (based on budget and complexity)
• BI: Looker Studio (free) or Metabase

Total cost might be $100-500/month depending on data volume and tool choices.

Implementation Reality

Setting this up isn’t trivial. Expect:

• Choosing which data sources to connect (start with 2-3)
• Configuring ETL pipelines (several hours each)
• Modelling data relationships (how do tables connect?)
• Building initial dashboards
• Testing and validating data accuracy

Time investment: 20-40 hours to get something basic working. More for complex environments.

You need someone with technical ability. This could be:

• An analytically-minded operations person willing to learn
• A part-time data analyst
• A consultant for initial setup

The Build vs Buy Question

Some SaaS products promise to do this for you.

Segment: Customer data platform. Collects events and sends to warehouses.

Customer.io: Marketing automation with analytics.

Industry-specific analytics platforms: Exist for e-commerce, SaaS, etc.

These are simpler but less flexible. Trade-off: easier to start, harder to customize, potentially more expensive at scale.

For many SMBs, an industry-specific analytics tool beats building a custom warehouse.

The Honest Assessment

Do you really need this?

Before investing in a data warehouse:

1. What questions can’t you answer today?
2. How often do you need to answer them?
3. What decisions would change with better data?
4. Who would actually use the warehouse?

If your answers are vague, you might be solving a problem you don’t have.

If your answers are specific and frequent, it might be worth the investment.

Start Smaller

Before building a warehouse, try simpler approaches:

Native integrations: Can your CRM connect to your analytics? Can your dashboard pull from multiple sources?

Zapier to Google Sheets: Low-tech but works for small scale. Push data to sheets, build charts there.

Pre-built connectors: Supermetrics for marketing data. Similar tools for other domains.

Only graduate to a real warehouse when simpler approaches genuinely fail.

The Bottom Line

Data warehouses are powerful. They’re also effort.

Most SMBs can wait. A few genuinely need them.

If you find yourself manually combining data regularly, asking questions that span systems, and making decisions that could benefit from data you can’t easily access, explore the options.

Otherwise, focus your technology effort elsewhere. The warehouse will still be there when you need it.