Intelligence & Analytics.
The layer that turns data into decisions.
A peek at the work in motion.
Rotating snapshot of the actual day-to-day — CRM syncs, BI refreshes, deploys, infra. Not a marketing animation; a real-time render of the kind of commands our team runs.
Six pillars of the work.
A typical Stage 04 engagement touches three or four of these. Some span all six.
Semantic models
The contract between raw data and every report that depends on it. We build them in Power BI (with named, governed DAX measures and Tabular-Editor-friendly structure) so reports inherit definitions instead of inventing them.
Executive scorecards
Five numbers, one screen. The metrics the leadership team actually decides from — with definitions documented and lineage traceable. The kind of dashboard board members read in 90 seconds.
Operational dashboards
Front-line monitoring for ops teams: real-time KPIs, alert thresholds, drill-down to the row. Built to be lived in, not visited.
Data engineering
The plumbing under the dashboards: medallion architecture (bronze → silver → gold), ELT pipelines, Power Query M transformations, data quality and lineage work.
Ad-hoc analytics
When the question doesn't fit a dashboard. Statistical analysis in Python (pandas, scikit-learn) or R (tidyverse, ggplot2). Forecasting, cohort work, custom transformations.
Self-service + RLS
Row-level security, named user roles, governance baked in. Ten people in your org should be able to extend the model safely — not just the one who built it.
An anonymized executive scorecard.
Schematic view, no real client data. Demonstrates layout, metric hierarchy, and tile composition we apply across mid-market engagements.
More schematics: See the show-the-work gallery →
A reliable engagement shape.
A typical Stage 04 engagement runs 6–12 weeks. The shape compresses or expands; the steps don't change.
Discovery & KPI workshop
One half-day session. We define the 5 numbers that matter, name the source systems, and agree on what "the same metric, the same definition" looks like across teams.
Source audit + data quality pass
We trace lineage from your operational systems to your reporting needs. Identify the gaps, the duplicates, the broken joins. Quality flags get documented before the model is built.
Semantic model + data marts
Star-schema where it fits, denormalized where the workload demands it. Power Query M for transformations, DAX measures named so a team member can read them six months later. Row-level security baked in.
Reports + dashboards
Executive scorecard plus one or two operational dashboards layered on the model. Designed for the actual decision — not "more charts, please."
Training + handoff
Hands-on sessions for the designated owner: how to extend a measure, how to add a report, how RLS works, how to release responsibly. Loom recordings of every session, included.
Post-handoff support window
30 days of "we'll fix it" support after handoff. Bugs, edge cases, the inevitable "what does this measure actually mean?" question. Then we step back and let your team own it.
Tools we use, named.
Microsoft-leaning by default (it's what fits the missing-middle Canadian buyer best), but we work where your data lives.
A few things we don't compromise on.
We don't write a single DAX measure that the team can't read six months later. Names matter. Comments matter. A measure called SalesYoY beats m_xxx_001 in every meeting that follows.
Row-level security is not optional. If the model serves more than one user, RLS is configured before the first dashboard ships. Not after the first leak.
One definition per metric. If two teams define "Active Customer" differently, we don't pick a winner — we surface the ambiguity, drive a decision, document it, and move on.
Knowledge transfer is the deliverable. The dashboard is the artifact; your team owning it is the actual outcome.
Common questions.
How fast can you stand up a working Power BI environment?
Two-week sprint for a starter pack (one semantic model, three dashboards). Six-to-twelve-week engagement for a full multi-source environment with executive + operational layers. Anything claiming "two days" is either a templated dashboard or a sales pitch.
Do we need Power BI Premium / Fabric capacity?
Usually no for SMBs — Power BI Pro covers most use cases up to ~50 viewers. Premium / Fabric makes sense once you need Direct Lake, paginated reports at scale, or large semantic models (>1GB).
Our data is messy. Do we need to clean it up before you start?
No. The cleanup is the work. We expect messy — that's what data quality, lineage, and governance steps in our methodology are for. The only prerequisite is that someone on your team can answer "what does this column mean?" when we ask.
What if our data is on Snowflake / Databricks / BigQuery, not Microsoft?
Great. We work there. Power BI sits comfortably on Snowflake and Databricks; Looker / Looker Studio handles BigQuery. The methodology doesn't change.
Will my team be able to maintain this after you leave?
That's the point. Stage 05 (Enablement) is built into Stage 04 delivery — documentation, Loom walkthroughs, hands-on training, and a 30-day post-handoff support window. The model is named, governed, and editable by your designated owner.
How does this compare to hiring an in-house BI person?
An in-house senior BI lead in Canada runs ~$120-160K all-in. Our typical Stage 04 engagement is in the $15-60K range depending on scope, plus optional retainer for ongoing support at fractional rates. The math favors fractional until you have a permanent need for ~30+ hours/week of BI work.
Ready to make your data speak?
30-minute fit call. We'll review your current state, name the gaps, and tell you what we'd do first — whether it's with us or someone else.