The Revenue Leader's Guide to Building a Sales Analytics Stack That Actually Works
You've seen it happen: your sales team is flying blind because reports take days to generate. Your revenue ops analyst is drowning in spreadsheet hell. Your board meeting slides are based on data from two weeks ago. Sound familiar?
Here's the uncomfortable truth: the difference between revenue leaders who make data-driven decisions and those who rely on gut instinct often comes down to infrastructure choices made months or years earlier.
I've watched companies scale from 50 to 500 employees, and the ones who nail their data infrastructure early have a massive advantage. They forecast more accurately. They identify churn risks faster. They optimize their GTM motion while competitors are still arguing about pipeline definitions.
Let's fix your stack.
Why Your Data Infrastructure Actually Matters (More Than You Think)
When you're a 75-person company doing $10M ARR, spending time on data infrastructure feels like over-engineering. You've got quotas to hit, territories to design, and a compensation plan to finalize.
But here's what happens when you get it wrong:
Your BI tool is so slow that reps stop checking their dashboards. Your data warehouse costs spiral because nobody optimized the queries. Your RevOps team spends 60% of their time wrangling data instead of surfacing insights. You're making million-dollar decisions based on stale or incomplete information.
The right infrastructure does three things:
Makes data accessible to non-technical users – Your VPs should be able to answer their own questions without Slack-bombing your analyst
Scales with your complexity – As you add products, segments, and go-to-market motions, your stack shouldn't break
Delivers insights at decision-speed – If it takes 3 days to answer "which enterprise deals are stalling?" you've already lost the deal
Bottom line: your data stack is either a competitive advantage or a liability. There's no middle ground.
Choosing Your Cloud Data Warehouse: What Actually Matters
Your data warehouse is the foundation. Pick wrong here, and everything built on top will suffer.
The Criteria That Matter
Query Performance at Your Scale
At 50-200 employees, you're probably looking at 5-50GB of sales data (CRM records, product usage, billing, support tickets). You need sub-second queries for dashboards and under-5-second queries for ad-hoc exploration. Anything slower and adoption tanks.
Cost Structure
Watch out for hidden costs. Some warehouses charge per query, others per storage, others per compute time. For revenue analytics with unpredictable query patterns, you want predictable costs that scale linearly - not exponentially - with usage.
SQL Compatibility
Your RevOps hire probably knows SQL. Your next data analyst definitely will. Choose a warehouse with standard SQL support so you're not locked into proprietary syntax or limiting your talent pool.
Integration Ecosystem
You're pulling from Salesforce, HubSpot, Stripe, your product database, maybe Gong or Outreach. The warehouse needs native connectors or works seamlessly with tools like Fivetran or Airbyte.
Separation of Storage and Compute
This is technical but critical: you want to pay for storage separately from compute. This lets you store everything cheaply while only paying for compute when you're actually running queries.
The Specific Recommendations
Choose Snowflake if: You need enterprise-grade performance and have budget flexibility ($200-1000+/month). Snowflake's separation of storage and compute means costs are predictable. The zero-maintenance aspect is clutch when you don't have a data engineering team. Best-in-class SQL support and integrations. This is the safe, scalable choice for companies with clear growth trajectories.
Choose BigQuery if: You're already in the Google Cloud ecosystem or you have spiky, unpredictable usage patterns. BigQuery's serverless architecture and pay-per-query pricing can be more economical at smaller scales. The learning curve is steeper and SQL has some quirks, but the performance is excellent and it scales infinitely.
Choose Databricks if: You're doing more than just BI - maybe predictive modeling, churn analysis, or ML-based forecasting. Databricks combines warehouse capabilities with advanced analytics. Overkill for most companies under 200 employees, but if you have a technical RevOps team with ambitions, it's worth considering.
Choose MotherDuck if: You're under 50 employees, your data fits in memory (under 10GB), and you want something stupid simple. MotherDuck runs DuckDB - a fast, embedded database that can query directly from your laptop or cloud. Minimal cost, minimal complexity. You'll eventually outgrow it, but it's a great starting point.
Choose Redshift if: You're already deep in AWS and have technical resources to manage it. Redshift is powerful but requires more hands-on tuning than Snowflake or BigQuery. Unless you have a data engineer on staff or strong AWS expertise, I'd skip it.
My recommendation for most revenue leaders reading this? Start with Snowflake if you can afford $300-500/month. It'll grow with you, and you won't regret it. If budget is tight, start with MotherDuck and plan to migrate when you hit 75+ employees or 10GB+ of data.
Choosing Your BI Tool: Where Insights Come to Life
Your warehouse is the engine; your BI tool is the dashboard. This is what your team actually interacts with, so usability matters more than raw power.
The Criteria That Matter
Ease of Use for Non-Technical Users
Can your VP of Sales build a pipeline report without calling IT? If the tool requires training sessions and SQL knowledge, adoption will suffer. Look for drag-and-drop interfaces with intuitive filtering.
Embedded Analytics
Do you need to surface metrics inside Salesforce, Slack, or your own product? Some BI tools embed beautifully; others don't. If you're planning to operationalize insights, this matters.
Governance and Data Modeling
As you scale, you need consistent definitions. "Pipeline" means the same thing in every dashboard. Look for tools with semantic layers or metric stores that enforce consistency.
Collaboration Features
Can your team comment on dashboards? Schedule reports? Set up alerts when ARR dips or churn spikes? The best BI tools feel collaborative, not transactional.
Mobile Experience
Your CRO doesn't want to open a laptop to check this week's bookings. Mobile-first or mobile-friendly experiences are table stakes in 2025.
Speed and Performance
Even with a fast warehouse, a poorly optimized BI tool will destroy the experience. Look for tools that cache intelligently and pre-aggregate common queries.
The Specific Recommendations
Choose Tableau if: You want best-in-class visual analytics that will scale with your company. Yes, it's an investment ($70-150/user/month) and has a learning curve, but Tableau delivers pixel-perfect, highly customizable visualizations that make data compelling. Once your team gets over the initial hump, they'll be able to build sophisticated dashboards that actually influence executive decisions. For revenue leaders who want their data to tell a story—not just display numbers—Tableau is worth it. The ecosystem is mature, the community is massive, and you won't outgrow it.
Choose Power BI if: You're a Microsoft shop (Office 365, Azure, Dynamics) or need a cost-effective enterprise solution. Power BI integrates seamlessly with your existing Microsoft stack and costs significantly less than competitors ($10-20/user/month). The interface feels familiar to anyone who's used Excel, which lowers the adoption barrier. The trade-offs? It feels a bit dated compared to newer tools, and the learning curve is steeper than it should be. But for Microsoft-centric organizations, the tight integration and pricing make it a strong runner-up.
Choose Looker if: You have technical resources and centralized governance is non-negotiable. Looker's LookML modeling layer ensures everyone in your organization uses the same metric definitions—critical as you scale from 100 to 500 employees. It's expensive ($3,000-5,000/month minimum) and requires more setup than drag-and-drop tools, but it prevents the metric chaos that destroys data trust at scale. If you're building a data-driven culture for the long haul and have the resources to invest upfront, Looker pays dividends.
Choose Metabase if: You're budget-conscious and want simplicity. Metabase is open-source (free self-hosted or ~$85/month cloud), has a clean interface, and gets 80% of teams 80% of what they need. It won't impress your board, but it'll get your team the answers they need quickly.
Choose Sigma if: You want spreadsheet-like flexibility with SQL power under the hood. Sigma feels like Google Sheets but queries your warehouse directly. Non-technical users love it because it's familiar. Technical users love it because it generates efficient SQL. At $30-60/user/month, it's pricey but worth it for teams that live in spreadsheets.
Choose Hex or Mode if: Your RevOps team writes SQL and Python. These tools are for technical users who need to combine SQL queries, Python analysis, and visualizations in notebooks. If your team is sophisticated, these are game-changers. If they're not, these tools will sit unused.
My recommendation for most revenue leaders reading this? Invest in Tableau if your budget allows—the upfront learning curve pays off in richer insights and better storytelling. If you're in the Microsoft ecosystem, Power BI is the pragmatic choice that balances cost and capability. If neither fits, Looker is the enterprise-grade option for governance-first organizations.
Putting It Together: Your Decision Framework
Here's how I'd actually make this decision if I were a CRO at a 100-person company tomorrow:
Step 1: Map your current data sources (CRM, billing, product, support) and estimate total data volume. Under 10GB? Consider MotherDuck. Over 10GB or growing fast? Snowflake or BigQuery.
Step 2: Assess your team's technical sophistication. If your RevOps person codes, you have more options. If not, prioritize ease of use in your BI tool.
Step 3: Define your must-have reports. Pipeline coverage, win rates, sales cycle length, ARR movements, retention cohorts. Make sure your BI tool can build these without custom code.
Step 4: Set a realistic budget. For a 100-person company, expect $500-1,500/month total (warehouse + BI tool). That's cheap insurance for better revenue visibility.
Step 5: Start simple, plan to iterate. You don't need to nail this perfectly on day one. Pick tools that won't lock you in and can grow with you.
The companies that win with revenue analytics aren't the ones with the fanciest stack. They're the ones who choose tools that match their team's capabilities, ship dashboards quickly, and iterate based on what actually gets used.