Home Advanced Analytics
Advanced Analytics

Stop reporting the past. Start predicting it.

Consulting, warehousing, migration, and machine learning — the full path from scattered source systems to models that tell you what happens next.

Book a Data Assessment See all six services
6 servicesONE CONTINUOUS PIPELINE
Zero downtimeMIGRATION TARGET
ExplainableMODELS YOU CAN DEFEND
Unsupervised · k-means
3 segments, not 30 guesses
customer-segmentation · v3 LOADING
ROWS0
FEATURES18
SILHOUETTE—
Where You Are

Four questions data can answer. Most companies stop at two.

Reporting tells you what happened. Advanced analytics is the work of getting to the last two columns — and it needs the first two to be solid first.

DESCRIPTIVE

What happened?

Dashboards and reports. Most organisations have this, even if the numbers don't always agree.

01
DIAGNOSTIC

Why did it happen?

Drill-down, segmentation, and correlation. Needs clean, joined-up data to be trustworthy.

02
PREDICTIVE

What will happen?

Forecasting, churn and demand models. This is where the return on a data warehouse starts showing.

03
PRESCRIPTIVE

What should we do?

Optimisation and recommendation — the model doesn't just warn you, it proposes the action.

04
Where To Start

Every engagement opens with an honest scorecard.

Six dimensions, scored against where you actually need to be. It usually shows the platform isn't the problem.

QUALITY GOVERNANCE SKILLS TOOLING ARCHITECTURE ADOPTION
WHERE YOU ARE WHERE YOU NEED TO BE
Data qualityDuplicates, gaps, and fields nobody trusts
4.5
GAP 4.0
GovernanceWho owns a metric, and who may see it
3.0
GAP 5.5
SkillsIn-house capability to run it without us
5.5
GAP 3.0
ToolingPlatform, licences and pipelines already owned
6.0
GAP 2.5
ArchitectureHow data moves, and what breaks when it doesn't
4.0
GAP 4.5
AdoptionWhether anyone acts on what gets produced
3.5
GAP 5.0

ILLUSTRATIVE SCORES — THE SHAPE OF YOUR CHART IS THE FIRST THING WE PRODUCE

Consulting Data Analytics ETL Migration Data Migration Data Warehousing Machine Learning
01CONSULTING

Expert advice to harness the full potential of your data.

Before anyone builds anything, someone has to say honestly what you've got, what it's worth, and what's actually stopping you. That's this.

  • Data maturity assessmentAn honest scorecard across quality, governance, skills, tooling, and architecture — with the gaps ranked.
  • Use-case prioritisationWe size the value and the effort of each idea, so you start with the one that pays for the next one.
  • Target architecture & roadmapA staged plan with costs — not a 90-slide strategy deck that sits in a shared drive.
  • Governance & ownership modelWho owns which dataset, who approves definitions, and how quality gets measured after we leave.
Data Maturity AssessmentSCORE 100
Data quality42
Governance31
Architecture58
Tooling74
Skills66
Priority gap — governance. Fix definitions before building models on them.
02DATA ANALYTICS

Meaningful insights for data-driven decision making.

Analysis that answers a question someone actually asked, and arrives before the decision has already been made.

  • Customer & cohort analyticsRetention curves, lifetime value, and segmentation that shows which customers are actually worth chasing.
  • Operational & financial analysisMargin leakage, cycle times, utilisation — where the money and the hours are quietly going.
  • Statistical rigourSignificance testing and confidence intervals, so a 3% movement isn't presented as a trend.
  • Self-service enablementCurated datasets and training so your analysts can answer the next question without us.
Cohort Retention7 PERIODS
03ETL MIGRATION

Move, transform, and load data with minimal disruption.

Rebuilding the plumbing while the business keeps running on it. Old jobs stay alive until the new ones prove they produce identical results.

  • Legacy pipeline modernisationSSIS, stored procedures, and hand-run scripts rebuilt on Azure Data Factory, Fabric, or dbt.
  • Parallel run & reconciliationBoth pipelines run side by side until outputs match row for row. Only then does the old one get switched off.
  • Incremental & CDC loadingChange-data-capture instead of full reloads — refresh windows go from hours to minutes.
  • Monitoring & alertingFailures page someone at 3am instead of being discovered in a 10am meeting.
Pipeline RunINCREMENTAL · CDC
ExtractD365 · SQL · FLAT FILES
1.2M
TransformCLEANSE · JOIN · CONFORM
1.2M
LoadWAREHOUSE · GOLD LAYER
1.2M
ReconcileROW COUNTS MATCH
✓
The Unglamorous Part

Not all of your data survives contact with reality.

A real profile from a mid-market client's customer table. This is the work nobody demos, and the reason most projects run late.

2,417,880RAW ROWS EXTRACTED
1,958,482AFTER DEDUPLICATION
459,398 EXACT & FUZZY DUPLICATES
1,547,201AFTER VALIDATION
411,281 MISSING KEYS OR BAD FORMATS
1,262,940AFTER ENRICHMENT & MATCHING
284,261 UNMATCHABLE TO A MASTER RECORD
1,159,382TRUSTWORTHY · MODEL-READY
103,558 FAILED BUSINESS RULES
48%Of the original extract was usable without intervention. That number is normal, not alarming.
Fix upstreamEvery rule we write is also handed back as a source-system fix, so the number climbs each quarter.
Visible, not hiddenRejected rows go to a quarantine table with the reason attached — never silently dropped.
04DATA MIGRATION

Risk-free migration strategies for archiving and migration needs.

Moving twenty years of history into a new system without losing a record, breaking an audit trail, or taking the business offline for a weekend that becomes a fortnight.

  • Profiling before you commitWe find the duplicates, orphans, and bad dates while they're still cheap to fix — not during cutover.
  • Rehearsed cutoverTrial migrations run repeatedly until the timings and the fallback plan are boring.
  • Reconciliation you can show an auditorRow counts, control totals, and sample-level checks documented for every table.
  • Archiving & retentionLegacy history kept queryable at low cost, with retention rules that satisfy compliance.
Migration Run · Trial 3DRY RUN
SOURCE
4,182,904legacy AX 2012
TARGET
4,182,904D365 F&O
Row count reconciliationMATCH
Financial control totalsMATCH
Referential integrity0 ORPHANS
05DATA WAREHOUSING

Manage, store, and access large volumes of data.

One place where the data lands, gets cleaned, and becomes something every downstream report and model can rely on — designed to stay fast as volume grows.

  • Layered architectureRaw, cleansed, and business-ready layers so you can always trace a number back to where it came from.
  • Dimensional modellingStar schemas and slowly changing dimensions — so history stays correct when a customer changes region.
  • Built on your cloudAzure Synapse, Microsoft Fabric, Snowflake, or Databricks — chosen on your workload, not our preference.
  • Cost control by designPartitioning, tiered storage, and right-sized compute, because a warehouse that surprises finance gets shut down.
Warehouse ArchitectureMEDALLION
Bronze — raw landingSource data, untouched, replayable
4.1M rows
Silver — cleansed & conformedDeduplicated, typed, joined to master data
3.8M rows
Gold — business readyStar schema, certified metrics, serving BI & ML
412 tables
Semantic layerOne definition per metric, reused everywhere
86 measures
06MACHINE LEARNING

Improve performance with machine learning algorithms.

Models aimed at one decision that repeats often enough to be worth automating — and measured against what you'd have done without them.

  • Forecasting & demand planningTime-series models for inventory, cash flow, and capacity that beat the spreadsheet baseline.
  • Churn, propensity & scoringRanked lists your sales and service teams can act on, delivered into the CRM they already use.
  • Anomaly & quality detectionCatching the fraudulent claim, the failing machine, or the mispriced order before a human would.
  • MLOps & explainabilityVersioned models, drift monitoring, retraining schedules — and an answer when someone asks why it decided that.
demand-forecast · trainingEPOCH 40/40
TRAIN ACCURACY VALIDATION
MAPE6.4%
VS BASELINE−38%
DRIFTSTABLE
Explainability

"Why did it flag this customer?"

A model nobody can question is a model nobody will act on. Every prediction we ship comes with the reasons behind it.

Account #C-40218 · Nexova RetailCHURN MODEL v3.2 · SCORED 06 AUG
HIGH RISK · REVIEW
Support tickets7 open, 3 breached SLA
+21%
Order frequencyDown 40% vs their own baseline
+18%
Days since contact94 days, no touchpoint
+13%
Invoice disputes2 raised this quarter
+9%
Tenure6 years — long-standing account
−10%
Contract term18 months remaining
−4%
◀ REDUCES RISK BASELINE 12% INCREASES RISK ▶
Churn probabilityBaseline 12% → final
69%
This is what lands in the CRM Not a bare score. The account owner sees the four things driving it and the two holding it back, so the retention call writes itself — and if they disagree with the model, they can say exactly why.
Next Step

Start with an assessment, not a platform purchase.

Two weeks, a scored view of your data estate, and a ranked list of what to do first. You'll know whether the rest is worth doing.

Book a Data Assessment Talk to Sales