Data analytics services are for organisations that want to turn data into clear, actionable insights. We modernise legacy data platforms to solve fragmented data, slow reporting, and scalability limits, with AI-driven business intelligence built in.
A scalable data infrastructure built around a centralised data platform and lakehouse architecture. This supports long-term growth, performance, and flexible analytics needs.
Unified data foundation
A single, centralised data platform with consistent and trusted data across the organisation. Teams work with the same reliable information, without silos or duplicates.
Faster business insights
Faster analytics delivery with real-time data and interactive dashboards. Decision-makers get the insights they need without long reporting cycles.
AI-driven self-service analytics
Business users can explore data independently using semantic data models and Power BI. AI-driven analytics make insights more accessible across the organisation.
Our Data Expertise Areas
Data warehouse / lakehouse
We design and modernise data warehouse and lakehouse platforms for scalable, cloud-based analytics. Solutions support secure data storage, performance, and long-term growth.
Data integration & engineering
We build reliable data pipelines to connect, transform, and manage data from multiple sources. This ensures consistent data quality and smooth integration across systems.
Business intelligence & reporting
We develop business intelligence solutions for clear, actionable reporting and analytics. Dashboards and semantic data models help teams make data-driven decisions faster.
Advanced analytics & data science
We support advanced analytics and data science use cases, including predictive and descriptive analytics. Data is prepared for AI and machine learning to unlock deeper business insights.
Our Data Services Delivery Processes
01
Analysis & design
We start with data analytics consulting to define a clear data strategy and solution design. This includes data architecture planning aligned with business goals and technical requirements.
02
Engineering & development
We develop data warehouses, data pipelines, and ETL solutions to support reliable data integration. Engineering focuses on performance, scalability, and secure data flows.
03
Operational support
We provide ongoing data platform support, including data quality management and BI support services. Analytics optimisation ensures stable performance and continuous improvement.
What is the difference between Microsoft Fabric and Databricks?
Both platforms support modern data and analytics workloads, but they focus on different use cases and ecosystems. Microsoft Fabric is tightly integrated with the Microsoft stack, while Databricks is built around Apache Spark and advanced analytics.
How long does it take to design and implement a modern data platform?
Timelines depend on your data landscape, requirements, and integrations. Smaller platforms can be delivered in a few months, while larger, enterprise-scale solutions may take longer due to complexity and data migration needs.
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