Who this is for
The right fit
- Companies whose dashboards disagree with each other
- Teams running reports out of spreadsheets and prayers
- Operators who can't trust the funnel numbers
- Orgs preparing data for AI and machine learning
What you can expect
Outcomes that matter
Hours
Time to new metric
vs. weeks of ad-hoc SQL
<15 min
Pipeline freshness
for operational dashboards
3-5x
Active analytics users
after self-serve rollout
Want a deeper benchmark? See real numbers in client work or read engineering insights.
Anatomy
Challenges, approach, outcomes
The core shape of every engagement.
Challenges Addressed
- •Data scattered across many systems
- •No single source of truth
- •Reports that don't match each other
- •Self-service analytics that aren't self-serve
Approach
- 1.Build unified data warehouse as single source
- 2.Implement data quality at ingestion
- 3.Create semantic layer for consistent metrics
- 4.Enable true self-service with guardrails
Outcomes
- Unified view of business performance
- Trustworthy metrics across the organization
- Self-service analytics adoption
- Data-informed decision culture
How we work
Engagement phases
A predictable rhythm from kickoff to handoff. Phases overlap when it makes sense.
Data Audit
Map sources, owners, freshness, and the metrics that actually drive decisions.
- Source inventory
- Metric catalog
- Quality gap analysis
Warehouse Foundation
Stand up the warehouse, ingestion, and dbt-based modeling layer.
- Warehouse setup
- Ingestion pipelines
- Core dbt models
Semantic Layer
Define the canonical metrics so every team pulls from the same definitions.
- Metric definitions
- Governance model
- BI integration
Self-Serve
Onboard teams, embed analytics, and feed clean data into AI features.
- Team enablement
- Embedded analytics
- ML feature store seeds
Curious how this maps to your context? Walk through the engagement process or jump straight to scoping a project.
Services
Services that deliver this solution
The capabilities Sri brings to bear on this engagement.
Stack
Technologies in play
The tools Sri reaches for when delivering this solution.
Industries
Best fit for
Sectors where this solution delivers the most value.
Proof
Recent work
Where this solution has delivered for real teams.
Real-Time Analytics Platform
Single-truth metrics across 12 teams
E-commerce Speed Optimization
Funnel insights into the 99th percentile
Browse the full case study library or see who Sri has worked with.
Dig deeper
Further reading
Playbooks, blueprints, and writings that go deeper on this solution.
FAQ
Common questions
What founders and engineering leaders ask before kicking off.
Do I need a warehouse if I'm small?
Yes, sooner than you think. A managed warehouse + dbt is cheaper than building reporting on read replicas, and prevents months of metric chaos later.
How do you keep numbers from drifting?
A semantic layer with version-controlled metric definitions. If marketing's 'active user' differs from product's, that disagreement gets surfaced, not buried.
More questions? Check pricing and engagement models or ask Sri directly.
Adjacent
Related solutions
Often paired with or sequenced after this engagement.
Ready to implement this solution?
Let's discuss how this approach can be tailored to your specific needs.