Enterprise · Data-driven solutions
Stop guessing the numbers. Decisions, in the dashboard.
Data warehouse, ETL pipelines, BI dashboards, and AI insights — wired to the systems that generate the data, so decisions stop relying on whoever knows the spreadsheet.
Data that drives decisions
From scattered spreadsheets to one source of truth.
Every business has data; not every business has answers. We've seen the same story 50 times — three dashboards that disagree, one analyst who knows the "real" query, and a weekly Slack thread arguing about whether revenue is up.
We pipe your operational systems into a warehouse, model the semantic layer in dbt, surface dashboards and alerts on top, and layer AI insights — forecasts, anomalies, root-cause — that don't just show the number, they recommend the next action.
- Warehouse on Snowflake, BigQuery, or Postgres
- ETL / ELT pipelines from operational systems → warehouse
- dbt models for a single semantic layer (KPIs agree)
- BI dashboards: Looker, Metabase, Mode, Hex
- AI insights: forecasting, anomaly detection, root cause
- Wired back into operational systems — insights act, not just inform
SELECT customer_id, churn_score
FROM dbt.marts.customer_churn
WHERE updated_at > now() - interval '7 days'
✓ Returned 1,247 rows · 0.4 s
from sklearn.ensemble import GradientBoostingClassifier
import mlflow
with mlflow.start_run():
model.fit(X_train, y_train)
✓ Run logged · MLflow #847 · F1 = 0.91
days_since_login · plan_tier · support_tickets · …
Stack · Databricks ML · Snowflake · dbt · MLflow · PySpark · scikit-learn
How we build it
From scattered spreadsheets to one source of truth.
- 01
Define the questions
What decisions need to get faster? Which metrics already disagree across teams? The data plan flows from this list — not from "let's load everything into the warehouse."
- 02
Pipe the warehouse
ELT from operational systems → warehouse (Snowflake, BigQuery, or Postgres). dbt models for the semantic layer, lineage and tests baked in.
- 03
Dashboards + alerts
BI tool of your choice (Looker, Metabase, Mode, Hex), role-based access, alerts on the metrics that matter. Business users self-serve.
- 04
AI layer on top
Anomaly detection, forecasting, root-cause analysis. The dashboard surfaces what to do, not just what happened. Reverse-ETL into ops systems where it matters.
By the numbers
One data stack, answering real questions.
4–6 wk
Dashboard timeline
Focused initiative: one warehouse + BI + top 5 KPI dashboards.
dbt
Semantic layer
Metric definitions in one place, tests on every model, lineage you can audit.
Snowflake · BQ · PG
Warehouse
Volume + budget + tooling drives the choice. Migrations supported.
AI
Insights baked in
Anomaly detection, forecasting, root-cause — surfaced with the data.
What you get
From source data to decisions you can defend.
Warehouse
Snowflake, BigQuery, or Postgres — whichever fits the data volume, latency budget, and existing tooling.
ETL / ELT
Pipelines from operational systems → warehouse, scheduled or event-driven. Airbyte, dbt, custom.
BI dashboards
Looker, Metabase, Mode, Hex — wired to warehouse with RBAC and embeddable views.
AI insights
Anomaly detection, forecasting, root-cause analysis — numbers come with "here’s what to do" attached.
Forecasting
Demand, churn, capacity planning — trained on your real history, validated on holdout.
Self-serve
Business users get dashboards + clean semantic layer. End of "can you pull this for me?" Slack DMs.
Data quality
Tests on every dbt model, alerts when rows drop, lineage you can audit.
KPI alignment
Same metric definition across product, sales, finance — encoded once, queried by every dashboard.
Reverse ETL
Insights written back into operational systems — analytics drives action, not just dashboards.
Built for these teams
Where the dashboards disagree with the spreadsheets.
E-commerce
Inventory + revenue + LTV in one warehouse
Reorder, restock, and discount decisions stop relying on whoever ran the export last night.
Operations
Multi-system ops + KPI dashboards
Same metric agrees across CRM, ERP, and finance. The 2-hour reconciliation meeting dies.
Marketing
Attribution + funnel + LTV analytics
CAC, payback, channel-level ROAS — measured the same way every time, by everyone.
Finance
Real-time financial close + forecast
Month-end close compresses; CFO stops being the bottleneck on every strategic question.
Product
Feature usage + cohort + experimentation
Decisions on what to build next come from data, not LinkedIn screenshots of competitors.
Customer Success
Health scores + churn signals + expansion
CSMs work the right accounts; renewals stop being a surprise the week before they expire.
Common questions
What teams ask before they consolidate their data.
Snowflake, BigQuery, or Postgres — which one?
Snowflake when storage + compute volume is large and you want the strongest BI ecosystem. BigQuery when you're already on GCP and want serverless. Postgres when your data fits, you want lower cost, and your team is more SQL-native than data-platform-native. The choice flows from data volume + budget + existing tooling — we recommend after the discovery week.What BI tool do you recommend?
Metabase for fastest time-to-dashboard and self-serve. Looker when the semantic layer matters and you want to centralize metric definitions. Mode or Hex when analysts need SQL + notebook flexibility. Tableau or PowerBI if your org already has commitments. We integrate to all of them; the semantic layer in dbt is portable across BI tools so you're not locked in.How do you handle data quality and tests?
dbt tests on every model — uniqueness, not-null, accepted-values, custom freshness windows. Alerts wired to Slack or PagerDuty when a model fails or row counts drop unexpectedly. Lineage tracking via dbt + your warehouse's metadata so you can audit "which dashboard breaks if this source table changes."Can you wire the AI insights back into our systems?
Yes — that's the difference between data warehouse work and data-driven work. Reverse ETL pipes warehouse-computed insights (LTV scores, churn predictions, reorder forecasts, SKU profitability rankings) back into the operational systems that act on them (CRM, ERP, e-commerce platform). Insights stop dying in dashboards.How long until I have a working dashboard?
For a focused initiative (one warehouse + one BI tool + the top 5 KPI dashboards), 4 to 6 weeks. For a multi-source initiative (3–5 operational systems + a semantic layer + 15+ dashboards + AI insights), 10–16 weeks. We always demo on real data by week 2 — there's no "wait until we finish modeling" gap.What about ongoing data work after launch?
Optional monthly retainer for ongoing modeling, dashboard requests from new teams, AI-insight refinement, and warehouse cost monitoring. About half our data clients keep us on; the other half hire a 1–2 person internal data team that takes over the dbt project. Either path is supported — the architecture is built to be team-friendly, not vendor-locked.
Tired of "ask Mike for the spreadsheet"?
Get a data stack that answers questions.
Book a discovery call. We'll review the data, the decisions, and the disagreements you want to end, then come back with a scoped warehouse + dashboard plan.