ViitorCloud
Home / What We Do / BI & Analytics Modernization

BI & analytics modernization for metrics the board can trust.

Most analytics programs do not fail because teams lack dashboards. They fail because revenue, risk, cost, customer, and operational metrics mean different things in different rooms. We modernize the data foundation, semantic layer, dashboards, and governance model so leaders stop arguing over definitions and start acting on the numbers.

See the Engagement Shapes
Semantic layer first · Owner-attributed metrics · BI-tool agnostic · ISO 27001 certified
What is BI modernization?

The rebuild of the data, metric, semantic, dashboard, governance, and adoption layers so business reporting becomes consistent, trusted, and usable for decisions.

The semantic layer

One number, every room.

01
Sources
Warehouses, apps, files
02
Data Products
Modeled, tested tables
03
Semantic Layer
Versioned metric logic
04
Metrics Catalog
Owner-attributed definitions
05
Executive Dashboards
One number, every room
Versioned · owner-attributed · BI-tool agnostic

What broken analytics looks like

Fix the layer underneath the dashboard so a metric means one thing in every room.

Dashboards nobody trustsManual reconciliationBoard packs in spreadsheetsMetrics differ across functionsData owners unclear
Investment

Ranges before you spend a meeting on us.

Analytics Architecture Review

From $40K
4 to 6 weeks

Modernization Program

From $200K
2 to 4 months

Analytics-as-a-Service Retainer

From $15K/mo
12 months rolling

Typical first-year investment $380K–$500K. Reconciliation time drops from days to hours within the first quarter. Exact figures are named on the scoping call and written into the SOW.

Where you are today

Modernize the layer, or operate it.

Modernize

Dashboards exist, but nobody trusts them.

Metrics differ across teams, reconciliation is manual, and the board pack still lives in spreadsheets.

Analytics Architecture Review (4–8 wks, from $40K) → Modernization Program (2–4 mo, $200K–$280K)
You get
Production lakehouse on your existing cloud
Rebuilt semantic layer with versioned, owner-attributed metrics
Redesigned dashboards
Embedded enablement
Operate

You modernized, and now need ongoing capacity.

The layer is trusted; keeping it trusted needs standing analytics engineering discipline.

Analytics-as-a-Service Retainer (12 mo rolling, from $15K/mo)
You get
Dedicated analytics engineering capacity
Monthly metric and dashboard delivery
Pipeline reliability monitoring
Quarterly governance review
Typical phases

Review, modernize, hand over.

Phase 01

Analytics Architecture Review

Weeks 1–6 · From $40K

Data estate and metric audit, semantic-layer gap analysis, and a modernization plan with the SOW for the program.

Phase 02

Modernization Program

Weeks 7–18 / $200K–$280K

Lakehouse foundation; semantic layer with versioned metric definitions, owner attribution, and governance; dashboards rebuilt with enablement; handover.

Phase 03

Hypercare & Handover

Weeks 19–20 / Included

Senior engineers on call while executive users adopt the rebuilt dashboards, with the runbook finalized.

Phase 04 (optional)

Analytics-as-a-Service Retainer

Month 6+ · From $15K/mo

Standing analytics engineering capacity: monthly metric and dashboard delivery, reliability monitoring, quarterly governance review.

The pod

Who does the work.

Lead Architect, Analytics & Data (12+ yrs)
Senior Engineer, Lakehouse & Pipelines (Iceberg, Delta Lake, Spark, dbt, Airflow)
Senior Engineer, Semantic Layer & BI (Tableau, Power BI, Looker, Superset)
Platform Engineer
Delivery Lead
Principles & governance

How we build it.

The semantic layer is the work

Dashboards are the visible 10%; the metric logic underneath is what earns trust.

Metrics get named owners

A named steward per metric domain, with a lightweight change process.

Work across BI tools, not against them

Certified vs exploratory metrics; governance applies to decision-grade numbers only.

Open source first where right

Iceberg, Delta Lake, dbt, Airflow; Databricks or Snowflake where you are pre-invested. Lineage and quality run as standing controls with a quarterly governance review.

Tech posture

Open source first where right; your BI tools stay.

Lakehouse / data

Apache IcebergDelta LakeDatabricksSnowflake

Semantic / pipelines

dbtAirflowDagsterAtlanCollibra (where pre-invested)

BI

TableauPower BILookerSupersetMetabase
Expected outcomes (ranges)
Days → hours
reconciliation time within the first quarter
90%+
metric definition coverage with named owners at handover
70–85%
dashboard adoption by executive users within 60 days
By sector

Trusted numbers under regulatory scrutiny.

BFSI

Reconciled risk, capital, and customer metrics with lineage a regulator can follow.

Explore BFSI delivery →

Healthcare

Governed cost, utilization, and quality metrics for payers and health systems.

Explore Healthcare delivery →

Public Sector

Program performance and outcome reporting built for transparency and an audit trail.

Explore Public Sector delivery →
FAQ

What buyers ask about analytics trust

Anything else, email the practice.

Usually not, the semantic layer sits beneath your existing tools.

Across the modern stack, Snowflake, BigQuery, Databricks, dbt, and the major BI tools, chosen on your existing investment.

We target a certified first metric domain within weeks, then expand domain by domain.

You do, a named steward per metric plus a lightweight change process.

Yes, governance applies to certified, decision-grade metrics only.

Related services
Enterprise AI CopilotsExplore →AI Workflow AutomationExplore →Cloud FinOpsExplore →AI Security ReviewExplore →Digital IdentityExplore →

Analytics the business actually trusts, on a semantic layer your team owns.

Bring the number two rooms disagree on. A senior practitioner will map the layer underneath it.

Email the Practice