Example case studies

What changes when the work ships

Illustrative scenarios — one per service practice — showing the problem, the build, and the outcome each engagement is designed to achieve. These are examples, not actual client engagements.

Business Intelligence Consulting · example scenario

Regional professional services firm, 240 staff

Challenge: Board packs took eleven days to assemble each month and three departments reported utilization differently, so the partners argued about the numbers instead of the business.
Solution: A four-week advisory engagement produced a signed KPI dictionary, retired 38 redundant reports, and defined a Power BI semantic model as the single source of truth for utilization and realization.
Targeted result: Board pack assembly dropped from eleven days to two, and the partner group now reviews one utilization figure everyone agrees on.

11 → 2 days

Board pack cycle

Reports retired

Agreed definition

How we deliver business intelligence consulting

Power BI Dashboard Development · example scenario

Multi-site dental group, 9 practices

Challenge: Each practice manager built their own production spreadsheet on Friday afternoons. Consolidating them took the operations director eight hours a week and the numbers still didn't tie to the practice management system.
Solution: We modelled the practice management database into a Power BI semantic model, wrote production, case-acceptance, and chair-utilization measures once, and published a mobile-friendly dashboard with row-level security so each manager sees their own site plus the group benchmark.
Targeted result: Manual reporting time fell from eight hours a week to roughly twenty minutes of review, and managers now see yesterday's production before their morning huddle.

95%

Less reporting time

Daily

KPI visibility

Sites on one model

How we deliver power bi dashboard development

Excel Dashboard Development · example scenario

Specialty contractor, $18M annual revenue

Challenge: The controller spent most of Monday rebuilding a job-cost workbook from four exports, and errors in the paste steps had twice caused jobs to be reported profitable when they weren't.
Solution: Power Query connections to the ERP and payroll exports, a proper job-cost data model, and a one-page dashboard with variance flags and a drill sheet per job.
Targeted result: Monday's rebuild became a refresh button, and the two-week lag on job margin visibility disappeared.

6 hrs → 5 min

Weekly rebuild

Manual paste steps

2 wks → 1 day

Margin visibility lag

How we deliver excel dashboard development

Tableau Dashboard Development · example scenario

Wealth management firm, $1.2B AUM

Challenge: Advisors pulled their own extracts, so quarterly client reviews sometimes showed AUM figures that didn't match the operations team's numbers by six figures.
Solution: One certified Tableau data source over the custodial feed, with incremental extracts, entitlement-based row-level security, and a redesigned advisor book-of-business workbook.
Targeted result: Every advisor review now runs off the same certified numbers, and extract refresh time dropped from over three hours to under fifteen minutes.

3 hrs → 14 min

Extract refresh

Certified source

Workbooks retired

How we deliver tableau dashboard development

Alteryx Workflow Automation · example scenario

Regional insurance carrier

Challenge: Two analysts spent the first four days of every month reconciling premium data across the policy system, the billing platform, and broker statements — entirely in Excel.
Solution: An Alteryx workflow that ingests all three sources, applies the matching rules the analysts had been running by hand, and produces a matched file plus a coded exception report for human review.
Targeted result: The reconciliation now runs overnight on the first of the month, and the analysts review exceptions instead of building the file.

4 days → 40 min

Monthly reconciliation

99.4%

Auto-match rate

Analysts freed up

How we deliver alteryx workflow automation

Python Automation · example scenario

Direct-to-consumer brand, 4 sales channels

Challenge: Someone downloaded order exports from four marketplaces every morning, pasted them into a master file, and reconciled fees by hand. If they were sick, the numbers stopped.
Solution: A Python service that pulls each marketplace API on a schedule, normalises fees and refunds into a single order model, writes to the warehouse, and alerts on schema changes or missing days.
Targeted result: The morning routine disappeared entirely and channel profitability is now available before the team logs on.

90 min/day

Manual work removed

Channels unified

6am

Data ready daily

How we deliver python automation

SQL Development · example scenario

Third-party logistics provider

Challenge: The operations dashboard timed out every afternoon once daily shipment volume passed a threshold, and the vendor's suggested fix was a larger server.
Solution: Workload profiling traced 80% of the cost to two views built on scalar functions. We rewrote them as set-based inline table functions, added three covering indexes, and updated statistics maintenance.
Targeted result: The dashboard now returns in under a second at peak volume on the same hardware, and the planned server upgrade was cancelled.

42s → 0.6s

Peak query time

Extra hardware

-71%

CPU at peak

How we deliver sql development

ETL Development · example scenario

Multi-brand retailer, 60 stores

Challenge: Nightly loads from the POS, e-commerce platform, and inventory system frequently failed, and the merchandising team had learned to check row counts by hand each morning before trusting the report.
Solution: Rebuilt the ingestion in Azure Data Factory with incremental watermarks, added a dbt-tested curated layer with freshness checks, and quarantined malformed POS records instead of failing the batch.
Targeted result: The morning manual check was retired after two months of clean runs, and merchandising now has store-level data by 5am.

99.8%

Load success rate

3.5h → 22 min

Nightly batch

5am

Data ready

How we deliver etl development

Data Engineering · example scenario

Healthcare services group, 3 acquired businesses

Challenge: Three acquisitions meant three EHR-adjacent systems, three finance stacks, and no way to report on the combined group without a two-week manual consolidation.
Solution: A Fabric lakehouse with a bronze-silver-gold model, conformed patient and encounter dimensions across all three sources, orchestration with dependency-aware scheduling, and freshness monitoring per domain.
Targeted result: Group-level reporting became a daily refresh instead of a two-week project, and the fourth acquisition was onboarded to the platform in nine days.

2 wks → daily

Group consolidation

9 days

To onboard a new entity

99.9%

Pipeline SLA

How we deliver data engineering

Data Warehousing · example scenario

Commercial real estate owner-operator, 46 properties

Challenge: Occupancy and NOI reporting was rebuilt from the property management system each quarter, and when a property was reclassified the prior year's figures silently changed, making comparisons unreliable.
Solution: A Snowflake dimensional warehouse with a type 2 property dimension, a monthly financial fact at property-and-account grain, and a Power BI semantic layer on top.
Targeted result: Quarterly reporting now runs from the warehouse in minutes, and prior-period comparisons stay stable because reclassification is versioned rather than overwritten.

5 yrs

Restatable history

3 wks → 1 day

Quarterly pack

Properties conformed

How we deliver data warehousing

Executive Reporting · example scenario

Private-equity-backed services business

Challenge: The finance team spent the final six working days of each month building a 40-page board pack in PowerPoint, and the sponsor still asked for numbers that weren't in it.
Solution: We rebuilt the pack as a Power BI paginated report driven by the warehouse, added variance-to-plan logic, and created a structured commentary form completed by each divisional lead before the cut-off.
Targeted result: The pack now generates on the second working day with commentary already attached, and the sponsor's standing questions are answered by an appendix that refreshes with it.

6 days → 2 hrs

Pack production

Day 2

Board pack ready

40 → 22

Pages, all used

How we deliver executive reporting

AI Automation · example scenario

Commercial insurance brokerage

Challenge: Two staff spent most of their week keying data from carrier loss runs — inconsistent PDFs, fifteen fields each — into the agency management system, with a three-day backlog during renewal season.
Solution: A document extraction service with a labelled evaluation set of 400 historical loss runs, structured output validation, and a review queue where anything under 90% confidence is checked by a human with the source page highlighted.
Targeted result: 81% of documents now clear without human touch at 96% field accuracy, and the renewal-season backlog was eliminated.

96.4%

Field accuracy

81%

Straight-through

Renewal backlog

How we deliver ai automation

API Integration · example scenario

B2B manufacturer with an outside sales team

Challenge: Sales worked in HubSpot, operations in NetSuite, and a coordinator spent roughly ninety minutes a day re-keying won deals into sales orders — with a steady trickle of typos reaching production.
Solution: A one-way sync from HubSpot to NetSuite with a mapped product catalog, idempotent order creation keyed on deal ID, a dead-letter queue for validation failures, and a nightly reconciliation report.
Targeted result: Order entry became automatic within seconds of a deal closing, and transcription errors in production orders stopped.

90 min/day

Re-keying removed

<60s

Deal to sales order

Transcription errors

How we deliver api integration

HTML Dashboard Development · example scenario

Vertical SaaS platform, 300+ business customers

Challenge: Customers asked for analytics inside the product. Embedding a BI tool would have meant per-customer licensing and an iframe that visibly clashed with the product's design.
Solution: A React and D3 analytics module inside the existing application, backed by aggregation endpoints with tenant isolation enforced in the API layer, using the product's own design tokens.
Targeted result: Analytics shipped as a paid tier feature with no per-user licensing, and pages render in about a second at the 95th percentile.

Per-user BI licences

1.1s

P95 page load

68%

Customer adoption

How we deliver html dashboard development

Workflow Optimization · example scenario

Building products manufacturer

Challenge: Quote-to-order averaged 21 days and sales blamed engineering while engineering blamed credit. Nobody had numbers, so the argument repeated at every monthly meeting.
Solution: We mined event logs from the ERP and CRM, reconstructed 47 process variants, and showed that 72% of elapsed time was queue time — most of it waiting on a credit check that could run in parallel rather than in sequence.
Targeted result: Reordering two steps and automating the credit pre-check cut average quote-to-order to nine days without adding headcount.

21 → 9 days

Quote-to-order

72%

Time found in queues

Added headcount

How we deliver workflow optimization

Cloud Analytics · example scenario

Credit union, $2.8B in assets

Challenge: Reporting ran on an ageing on-premise SQL Server that slowed to a crawl during month-end close, and a hardware refresh quote had just landed with a six-figure number attached.
Solution: A sliced migration to Azure with Fabric: landing zone as code, budget alerts from week one, and eight reporting domains moved one at a time in parallel run until every figure tied to the legacy system.
Targeted result: Month-end reporting no longer degrades under load, the hardware refresh was avoided, and monthly platform spend has stayed within the budget set at the start.

0.00%

Variance at cutover

Avoided

Hardware refresh

-12%

Spend vs budget

How we deliver cloud analytics

These are illustrative example scenarios with hypothetical figures, not actual client engagements. References from past work are available on request under NDA.

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