Data Services

When Do You Need Data Services?

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.

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Aleksandras Voitechovskis

Head of Data Solutions

[email protected]

Robertas Skardžius

Cloud & Data Business Manager

[email protected]

Why Choose Data Services?

Modern & scalable data platform

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

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.

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.

Operational support

We provide ongoing data platform support, including data quality management and BI support services. Analytics optimisation ensures stable performance and continuous improvement.

Industries for Data Services

Banking & finance

Banking data analytics

Regulatory compliance analytics

Financial analytics

Risk analytics

Manufacturing

Manufacturing data analytics

Data storage & processing

Production analytics

Workforce analytics

Wholesale / Retail

Retail data analytics

Supply chain analytics

Procurement analytics

Pricing analytics

Logistics

Logistics data analytics

Warehouse management analytics

Fleet analytics

Route analytics

Insurance

Insurance data analytics solutions

Underwriting, claims & risk analytics

Fraud detection & operational analytics

Regulatory-compliant BI & reporting

Case Studies

FAQ

How do you ensure compliance and data security?

Security and compliance are built into every stage of delivery. We follow industry best practices for access control, data protection, monitoring, and governance to keep your data secure and compliant with relevant regulations.

What does a typical data project look like?

Most projects start with analysis and design, followed by data engineering, platform implementation, and testing. Once the platform is live, we provide ongoing support to ensure performance, data quality, and continuous improvement.

How can a new data platform help me start using AI and machine learning?

A modern data platform provides clean, well-structured, and accessible data, which is essential for AI and ML use cases. It makes it easier to prepare data, train models, and deploy AI-driven insights across the business.