See exactly what you'll get
This is a realistic sample deliverable for a fictional 120-person B2B SaaS company. Every audit is unique to your stack — but this shows the depth, format, and clarity you can expect.
Example scenario
Meet “ScaleMetrics” — a fictional SaaS company
We created this example to show you the exact depth and format of our deliverables. The company is fictional, but the problems are real — we see them in nearly every scaling SaaS team.
Stage
Series B
$18M ARR
Team
120 people
3 data engineers
Stack
PostgreSQL + Metabase
No warehouse
Pain
Nobody trusts the data
5-day request queue
The situation: ScaleMetrics has grown from 30 to 120 people in 18 months. Their data stack was set up early on by an engineer who has since left. Analytics queries hit the production database directly, dashboards show conflicting numbers, and the data team spends 80% of their time on ad-hoc requests instead of building infrastructure. The VP of Engineering wants to hire 2 more data engineers — but the CTO isn't sure that's the right move before understanding what's actually broken.
Sound familiar? Most SaaS teams between 50–500 people face some version of this. See how your stack compares →
Sample findings from a Free Audit
The free audit identifies your top 3 priorities with enough context to act immediately. Here's what ScaleMetrics' free audit uncovered:
No central data warehouse
CriticalAnalytics queries run directly against production PostgreSQL, causing latency spikes during business hours. This is the single biggest bottleneck.
Metric definitions are inconsistent
High"Monthly Active Users" has 3 different definitions across product, marketing, and finance. Board reporting uses a blend of all three.
Dashboard sprawl causing mistrust
Medium12 Metabase dashboards exist, but only 4 are actively used. The rest show stale or conflicting data, eroding team confidence in analytics.
Free audit summary
ScaleMetrics has strong product-market fit but is flying blind on data. The #1 priority is standing up a central warehouse to stop querying production. A Deep Dive Audit would map the full data flow, assess all tooling, and produce a detailed 90-day plan to get from here to a mature, trusted data stack.
Free · 1-day turnaround · No commitment
Before & after
What changes after acting on the audit
Data Maturity scores on our 5-dimension framework (1–5 scale). These are illustrative outcomes based on typical improvements we see when teams execute on audit recommendations.
Infrastructure
1.5 → 4Before
5 disconnected data sources, no central warehouse. Engineers manually export CSVs from production DB for analytics.
After 90 days
Centralized Snowflake warehouse with automated ingestion from all 5 sources. Production DB no longer touched for analytics.
Governance
1 → 3.5Before
No data ownership. 3 different definitions of 'active user' across teams. No access controls beyond admin/non-admin.
After 90 days
Documented metric definitions in dbt. Data owners assigned per domain. Role-based access with row-level security.
Analytics
2 → 4Before
12 Metabase dashboards, 4 abandoned. Finance uses spreadsheets because they don't trust the dashboards.
After 90 days
3 canonical dashboards (exec, product, finance) with certified metrics. Finance uses live dashboards for board reporting.
Culture
1.5 → 3.5Before
Data requests go through engineering. Average wait time: 5 days. Teams have stopped asking.
After 90 days
Self-serve analytics for product and marketing. Engineering handles infra only. Wait time: same-day.
Roadmap
1 → 4Before
No data strategy document. Tool purchases driven by individual preferences. $47K/yr in overlapping tool spend.
After 90 days
12-month data roadmap aligned with product milestones. Consolidated tooling saves $28K/yr.
Recommendations
3 concrete next steps
Every audit includes actionable recommendations — not vague advice. Here's the level of specificity you can expect.
Stand up a cloud data warehouse within 30 days
What to do
Migrate analytics workloads from production PostgreSQL to Snowflake (or BigQuery). Use Fivetran or Airbyte for automated ingestion from your 5 core sources: production DB, Stripe, HubSpot, Intercom, and Google Analytics.
Why it matters
Eliminates production impact from analytics queries, enables joins across data sources, and establishes a single source of truth.
Effort
2–3 weeks with a senior data engineer
Impact
High — unblocks every other improvement
Define and codify your 10 core metrics in dbt
What to do
Create a dbt project with documented models for your key business metrics: MRR, churn rate, active users, CAC, LTV, NPS, pipeline velocity, expansion revenue, support ticket volume, and feature adoption.
Why it matters
Ends the 'whose numbers are right?' debate. Every dashboard and report pulls from the same tested, version-controlled definitions.
Effort
1–2 weeks with a data analyst + stakeholder alignment
Impact
High — builds trust in data across the org
Consolidate to 3 canonical dashboards
What to do
Replace 12 fragmented dashboards with 3 certified views: Executive (MRR, churn, runway), Product (activation, retention, feature adoption), and Finance (revenue recognition, CAC payback, cash flow). Archive the rest.
Why it matters
Fewer, trusted dashboards get more usage than many uncertain ones. Finance stops maintaining parallel spreadsheets.
Effort
1 week to build, ongoing curation
Impact
Medium — visible quick win that rebuilds confidence
Action plan preview
Example 90-day action plan
The Deep Dive includes a complete, prioritized action plan. Here's a condensed preview showing the structure and specificity.
Provision Snowflake account and configure networking
Set up Fivetran connectors for Postgres, Stripe, HubSpot
Draft and align on 10 core metric definitions with stakeholders
Create dbt project skeleton with initial staging models
Build dbt marts for executive, product, and finance domains
Implement data quality tests in dbt (not-null, unique, accepted values)
Build 3 canonical Metabase dashboards from dbt marts
Set up role-based access in Snowflake (admin, analyst, viewer)
Run 2 training sessions for self-serve analytics (product + marketing)
Archive deprecated dashboards and document data catalog
Decommission direct production DB access for analytics
Present data roadmap to leadership for next quarter
Full plan included in the Deep Dive
See what's includedReturn on investment
Why teams invest in the Deep Dive
The $1,500 Deep Dive typically pays for itself within weeks. Here's a realistic value breakdown based on ScaleMetrics' scenario:
Saved in tool consolidation
Redundant tools identified and eliminated
Engineering time recovered
From ad-hoc requests to self-serve analytics
Data request wait time
Teams get same-day answers, not week-long queues
The math: $28K in annual savings + recovered engineering time = the Deep Dive pays for itself within the first month.
Deep Dive Audit — $1,500
Everything above, plus
The free audit gives you the headlines. The Deep Dive gives you the complete playbook. Here's what the $1,500 engagement adds:
| Deliverable | Free Audit$0 | Deep Dive$1,500 |
|---|---|---|
| Top 3 findings with severity ratings | ||
| High-level recommendation summary | ||
| Next-step suggestion | ||
| Full infrastructure mapping | — | |
| Data governance review | — | |
| Tooling assessment + cost analysis | — | |
| Detailed recommendations with effort/impact | — | |
| 90-day prioritized action plan | — | |
| Executive presentation deck | — | |
| 30-minute walkthrough call | — |
Built by practitioners
Our team has designed data architectures from seed stage through IPO. We know what scales and what doesn't.
5-dimension framework
A structured, repeatable methodology — not ad-hoc opinions. Every assessment uses the same rigor.
SaaS-focused
We work exclusively with SaaS companies scaling past $10M. Every recommendation is calibrated for your stage.
About this example
ScaleMetrics is a fictional company created to illustrate the depth and format of our audit deliverables. The findings, recommendations, and action plan are representative of what we deliver — but every real audit is tailored to your specific stack, team, and challenges. No two audits are the same.
Ready to see what's actually going on with your data?
Start with a free audit — we'll review your stack and send you a clear summary within 1 business day. If you want the full analysis, the $1,500 Deep Dive has you covered.
No sales calls required. Free audit is genuinely free — no credit card, no catch.