An end-to-end corporate data foundation:

Single source of truth across domains

Zentavor corporate data warehouses (DWH/Lakehouse) unify disparate sources into a single source of truth for BI, advanced analytics, and AI. We design the architecture, build ETL/ELT pipelines, data marts and a metric layer and set up data quality and governance—so reports align and new sources are onboarded predictably and fast

DWH / Lakehouse: architecture & pipelines

What you get

From source inventory and data modeling to automated pipelines, marts, and governed metrics. You get a reliable warehouse (DWH/Lakehouse), consistent KPI definitions, data quality control and transparent operations—so analytics and AI run reliably in production

Data marts + metric layer + data quality

5-year ROI (IDC)

112%

reporting preparation time

−30–60%

to onboard a new source to marts

2–6 weeks

average payback period

1.6 years

Key business outcomes:

Describe your data sources and goals—we’ll propose the target DWH/Lakehouse architecture, a migration plan, and a rollout roadmap with measurable impact

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Corporate Data Warehouses

Sources and domains

CRM/ERP, finance, sales, marketing, web/app, logistics, support, external sources

ETL/ELT and orchestration

pipelines, schedules, CDC, change management, incidents, and observability

Data quality

checks, deduplication, reference alignment, reconciliation, alerts, and drift control

ML/GenAI detection

data catalog, lineage, roles & access, audit, compliance, retention policies

Risk scoring and decisioning

aligned KPIs, semantic layer, domain marts, refresh SLAs

BI and self-service

dashboards and marts for business, self-service analytics, quality and refresh alerts

Zentavor designs and delivers corporate data warehouses (DWH/Lakehouse) to consolidate company data into a single platform for management reporting, analytics, and AI

We structure data layers (raw/staging/core/marts), align reference data and KPI definitions, and build domain marts and a semantic metric layer. The platform includes monitoring, lineage, access roles, and governance processes—so data is trusted and changes are controlled

The architecture scales with the business: new sources and domains are onboarded via standards, while quality and SLAs are tracked through metrics and monitoring

What the corporate data warehouse includes

We consolidate CRM, ERP, financial and operational systems into a unified data model, eliminating metric discrepancies and creating aligned KPIs across departments

Fragmented data sources and conflicting KPIs

We implement data source control, change tracking, and update regulations so that every metric has a clear origin and defined freshness level

Lack of data transparency

We automate data collection, transformation, and refresh cycles, eliminating spreadsheet consolidation and reducing dependency on manual work

Manual reporting processes

We design a scalable DWH architecture capable of supporting increasing data volumes, new business domains, and expanding analytical use cases

Data growth without architectural foundation

Audit of data sources and current architecture

We assess existing systems, data marts, and reporting, identifying KPI inconsistencies and structural bottlenecks

Data model design

We develop the target data structure, storage layers, and analytical data marts

ETL / ELT process implementation

We configure regular data ingestion, cleansing, and transformation aligned with required refresh intervals

Data quality control and governance

We establish validation rules, change control procedures, and role-based access management

Performance optimization

We ensure stable DWH and BI performance as data volumes and user numbers grow

Data marts and KPI layer creation

We build structured data marts and unified KPI definitions for management reporting

Dashboards and reporting

We develop BI dashboards with automated refresh and alert mechanisms

Ongoing support and evolution

We support platform scaling, onboarding of new domains, and continuous analytical development

Omnichannel sales and margin
analytics

−30–60%

Retail & E-commerce

Reduction in reporting preparation time through improved data quality, lineage, and aligned KPIs

Banking & Finance

Regulatory and management
reporting

−30–60%

Reduction in reporting preparation time through improved data quality, lineage, and aligned KPIs

Manufacturing & Logistics

Operational efficiency and supply
chain

4–10 weeks

Fast start with an MVP data warehouse: connecting key sources and launching initial dashboards within predictable timelines

Industries and teams that need a single version of data and predictable analytics

Note: impact depends on data maturity, number of sources, reference data quality, and the chosen target architecture

Centralized storage and aligned KPIs across the organization

Single source of truth

Clear data lineage and controlled calculation logic ensure reliable reporting

Transparency and trust in data

DWH designed to support data growth and future AI initiatives

Scalable architecture

Reduced reporting preparation time and improved decision-making accurac

Measurable business impact

Share your objectives—we’ll propose the corporate data warehouse solution and an execution plan

Email: info@zentavor.ai

Describe your current state: data sources, reporting, BI tools, and pain points. We’ll propose the target DWH/Lakehouse architecture, a migration plan, and a rollout plan with measurable impact

Unified data layer

Key domains: finance, sales, marketing, product, operations, support

Data sources: ERP/CRM, accounting, web/app analytics, logistics, support, external data

Key KPIs and conflicting metric definitions across teams

Refresh requirements (hourly/daily) and acceptable data latency

Access model and security: roles, audit, compliance, storage and encryption