The Problem: Data You Have But Cannot Use
Most businesses in the GCC are not short on data. They have ERP systems generating transaction records, CRMs tracking sales pipelines, operational databases logging events, and spreadsheets capturing everything that fell between the cracks. The problem is not quantity — it is access, consistency, and trust.
When leadership asks "How did we perform last month?", the answer takes days. Someone pulls an Odoo report, someone else exports a spreadsheet, a third person reconciles the numbers manually. By the time the answer arrives, the data is stale and confidence in it is low. Strategic decisions get made on gut feel — not because leadership wants it that way, but because the data infrastructure was never designed to answer that question in real time.
The Solution: A Unified Data Platform on Google Cloud
We design and build data platforms on Google BigQuery that pull from every system your business runs — Odoo ERP, operational databases, third-party APIs, flat files — and consolidate everything into a single, governed data warehouse. From there, Looker dashboards give your business teams live, accurate visibility into the metrics that matter, without needing a data analyst in the room every time a question comes up.
This is not a reporting tool bolted onto an existing system. It is a proper data architecture — designed to grow with your business, add new data sources without rebuilding from scratch, and maintain data quality as a core constraint rather than an afterthought.
What We Build
Unified Data Warehouse on Google BigQuery
BigQuery is Google Cloud's serverless, fully managed analytics database. We design your warehouse schema using a layered data modelling approach — raw staging, transformation, and curated analytical layers — so your data is organised for fast querying, governed access, and long-term maintainability. There is no infrastructure to provision and no capacity to manage. BigQuery scales to petabytes automatically, and you pay only for what you query.
Automated ETL Pipelines
We build extraction, transformation, and load pipelines that move data from your source systems into BigQuery on a defined schedule — hourly, daily, or near real-time. Pipelines are built using Google Cloud's native toolset, including Dataflow, Cloud Composer, and Application Integration, depending on your architecture and latency requirements. Every pipeline is version-controlled, monitored, and alertable. If a pipeline fails or produces unexpected data volumes, your team knows immediately.
Data Quality & Governance Layer
A dashboard built on bad data is worse than no dashboard — it creates false confidence in decisions that are actually wrong. We implement data quality checks at the pipeline level: completeness validation, referential integrity, range checks, null rate monitoring, and data freshness alerts. Data that fails quality gates is quarantined for review and investigation, not silently overwritten. Your teams can trust what they see on every report.
Business Intelligence Dashboards on Looker
Looker is Google Cloud's enterprise BI platform, designed to give business users self-service access to governed data without writing SQL. We design and deliver dashboards for finance, operations, sales, procurement, and HR — calibrated to the KPIs your business actually tracks. Arabic-language labels and reports are fully supported. Dashboards are designed to be understood and maintained by your team, not only by the consultant who built them.
Data Catalog & Lineage Documentation
We document every dataset — what it contains, where it originates, how it is transformed at each stage, and who is responsible for its accuracy. This eliminates the common and costly problem of teams questioning the source of a number in a report. With a searchable data catalog, anyone in the business can find the dataset they need and understand how to use it correctly.
The Delivery Approach
We deliver in four structured phases with a clear milestone and client sign-off at the end of each.
Phase 1 — Discovery & Data Mapping (Weeks 1–2): We inventory your source systems, map data entities and relationships, identify data quality gaps, and design the BigQuery warehouse schema. You approve the architecture before any build work begins.
Phase 2 — Platform Setup & Environment Configuration (Weeks 3–4): We provision the Google Cloud project, configure IAM roles, set up monitoring and alerting, and establish the pipeline infrastructure. The environment is production-grade from the start.
Phase 3 — Pipeline Build & Data Modelling (Weeks 5–9): We build and test each ETL pipeline, implement the transformation models in BigQuery, enforce data quality rules, and validate outputs against source system records. You have visibility into progress throughout.
Phase 4 — Dashboard Delivery & Team Enablement (Weeks 10–12): We build and deliver the agreed dashboards in Looker, run user acceptance testing with your business teams, document everything, and deliver a knowledge transfer session so your team can operate and extend the platform independently.
Source Systems We Connect To
We design extraction approaches for any source system your business runs. Common sources include Odoo ERP (all modules), PostgreSQL and MySQL operational databases, REST and SOAP APIs from third-party SaaS tools, Google Sheets and structured flat files, and on-premise systems accessed via JDBC or SFTP. Extraction is always read-only and non-destructive — we never modify your production systems.
Who This Is For
This solution is designed for businesses in Saudi Arabia, Egypt, and the GCC that are running an ERP or operational system but cannot get fast, reliable answers from their data. Typical clients include companies with 50 to 500 employees where management reporting is done manually in Excel, where leadership wants better visibility into revenue, margins, or operational performance, and where finance or IT teams have recognised that the current reporting process is unsustainable and does not scale.
Business Outcomes You Can Measure
Businesses that implement a unified data platform with QueuesHub consistently see management reporting time reduced from days to minutes, a single source of truth replacing multiple conflicting spreadsheets, finance teams spending significantly less time on reconciliation and more time on analysis, and the ability to onboard new data sources — a new Odoo module, a new business unit, a new third-party system — without rebuilding the platform from scratch. The platform is built to extend, not to be replaced.