Services

Data Analytics · Data Warehouse

Data Warehouse & Integration

One warehouse for all your data, not one spreadsheet per department. Manufacturing records are often divided across ERP, MES, WMS, QMS, CRM, portals, files, and spreadsheets. We build governed pipelines and reconciled models so operational analytics use defined ownership and consistent measures instead of manual departmental extracts.

Discuss the Operating ProblemExplore Launchpad

01

The Problem

Manufacturing reports disagree because records, ownership, timing, and definitions are fragmented.

Analytics problems begin when a dashboard is built before records, definitions, timing, lineage, and accountability have been reconciled across the operation.

01

What leaders see

Reports disagree about current conditions.

Teams debate inventory, production, margin, delivery, and quality numbers instead of acting on them.

02

What is actually happening

Measures inherit unresolved record conflicts.

Sources, identifiers, definitions, refresh timing, adjustments, and ownership differ across systems and teams.

03

What gets worse

Faster reporting accelerates the wrong answer.

More dashboards spread inconsistent measures and make exception ownership harder to establish.

02

What Changes

What Data Warehouse & Integration includes.

Manufacturing records are often divided across ERP, MES, WMS, QMS, CRM, portals, files, and spreadsheets. We build governed pipelines and reconciled models so operational analytics use defined ownership and consistent measures instead of manual departmental extracts.

01

Cloud Data Warehouse

Deploy Snowflake, BigQuery, or Azure Synapse as your central analytics warehouse. Schema designed for complex operating data models -- orders, inventory, production, quality, and financials.

02

ERP Data Integration

Automated extraction from current and legacy ERP systems via Python pipelines. Transaction data, master data, and configuration data pulled on schedule or in near-real-time.

03

WMS & MES Integration

Warehouse transactions, production completions, quality records, and shop-floor data integrated alongside ERP data. The warehouse sees the full operational picture.

04

Automated ETL Pipelines

Scheduled and event-driven data pipelines that extract, transform, and load data from source systems. Built-in data quality checks, deduplication, and standardization at every stage.

05

Data Quality Layer

Validation rules, anomaly detection, and data quality scoring applied during ingestion. Bad data is flagged and quarantined -- not loaded into the warehouse to corrupt downstream reports.

06

Semantic Layer & Data Models

Business-friendly data models that define "revenue," "inventory," "on-time delivery," and other metrics once. Every dashboard and report uses the same definitions -- no more conflicting numbers.

03

How It Fits Your Operations

How Data Warehouse & Integration fits your operation.

Operational AnalyticsWhich decision the view supports, which records establish it, and how disagreement or missing data becomes visible.
Governance dependencyMeasures need defined ownership, timing, lineage, reconciliation, and access before dashboards can become a basis for operating decisions.
Operating records to reconcile
demand and orders
materials and inventory
production and capacity
quality
shipments and commitments

What must be defined before engineering begins

  • What the business needs to change and why.
  • Which systems, records, risks, and readiness gaps shape the work.
  • What should be built, how it fits the architecture, and in what order.

Related Services and Planning

What the operating problem may require next.

Follow the dependencies behind this service instead of treating it as an isolated project.

Start With the Operating Problem

Define the smallest sound response and delivery sequence.

Metrotechs determines what the operation actually requires before selecting technology. When a structured assessment is warranted, Launchpad turns evidence into priorities, risks, architecture, and an implementation Roadmap.

Discuss the Operating ProblemExplore Launchpad
Metrotechs designs, builds, integrates, and supports the approved solution.
Launchpad is available when the engagement needs assessment evidence, a Roadmap, and ongoing delivery governance.

04

Delivery sequence

How Metrotechs delivers Data Warehouse & Integration.

Manufacturing records are often divided across ERP, MES, WMS, QMS, CRM, portals, files, and spreadsheets. We build governed pipelines and reconciled models so operational.

01

Source System Inventory

Catalog every data source, document data volumes, update frequencies, and access methods. Map the data flows that need to converge in the warehouse.

02

Schema & Model Design

Design the warehouse schema and semantic models based on your analytics requirements. Define dimensions, facts, and business metric calculations with stakeholder sign-off.

03

Pipeline Development

Build ETL/ELT pipelines for each source system. Implement data quality checks, transformation logic, and incremental refresh strategies.

04

Validation & Go-Live

Validate warehouse data against source systems. Reconcile counts, totals, and key metrics. Go live when data accuracy meets defined thresholds.

05

Monitoring & Maintenance

Deploy pipeline monitoring, data freshness alerts, and quality dashboards. Ongoing maintenance as source systems change or new data sources are added.

05

FAQ

Questions that usually decide the scope.

Straight answers to what operators ask before committing budget to this work.

BigQuery on GCP for most operators in our stack -- integrates cleanly with Python pipelines and ERP data exports. Snowflake is a strong alternative for teams with existing BI investments. We recommend based on your analytics tools and data volume.