Variability
Considered explicitly in data design, quality, governance and operating decisions.
IT Services
Big Data
Data engineering and analytics foundations that make high-volume, high-variety information useful, trustworthy and governable.
Service overview
Engineer data for reliable decisions
We help teams build ingestion, transformation, storage and analytics capabilities around clear ownership and quality expectations. Architecture is driven by latency, scale, governance, lineage and the decisions or workflows the data needs to support.
- Data engineering and integration pipelines
- Warehouse and lakehouse architecture
- Data quality and governance
- Analytics and business intelligence enablement
Big Data dimensions
The operating characteristics that shape the data architecture
Scale alone does not define a data platform. Variability, trust, security, change rate, usability and business value all influence architecture and operating decisions.
Veracity
Considered explicitly in data design, quality, governance and operating decisions.
Vulnerability
Considered explicitly in data design, quality, governance and operating decisions.
Volatility
Considered explicitly in data design, quality, governance and operating decisions.
Visualisation
Considered explicitly in data design, quality, governance and operating decisions.
Value
Considered explicitly in data design, quality, governance and operating decisions.
HOW WE HELP
How RC approaches Big Data
Explore the specialist capabilities within this service area. Each capability has its own page covering context, scope, delivery approach, controls, expected outcomes and the wider service environment around the work.
Build Data and Analytics Strategy
Align data investment with measurable business questions, priority domains and accountable ownership.
We establish priority use cases, source systems, data domains, governance, platform direction and a staged roadmap that can be delivered incrementally. The strategy distinguishes foundational work from business-facing outcomes so investment sequencing is visible. Ownership, quality expectations and decision rights are defined alongside technology choices.
Read MoreAdvanced Analytics
Build analytical capability on trustworthy, well-defined data with measures that users can interpret.
We define datasets, metrics, analytical methods, quality thresholds, processing needs and delivery patterns for advanced workloads. Outputs are designed around decisions or operational actions rather than analysis for its own sake. Where models are introduced, evaluation and monitoring are included in the delivery design.
Read MoreInnovation with Machine Learning
Prepare governed data and operational workflows for machine-learning use cases that can move beyond experimentation.
We address feature or data pipelines, evaluation, reproducibility, model interfaces, deployment and monitoring alongside the intended business workflow. The work also covers ownership of model changes, fallback behaviour and the evidence needed to decide whether the use case is performing well enough for continued operation.
Read MorePerformance Management
Create consistent measures and reporting that show operational performance against clearly owned definitions.
We define KPI ownership, calculation rules, source lineage, refresh expectations, thresholds and dashboard consumption patterns. The objective is to reduce conflicting metrics and make it easier for management to understand what changed, why it changed and who is responsible for action.
Read MoreBusiness Intelligence
Deliver governed reporting and self-service analytics that users can trust without creating multiple versions of truth.
We structure semantic models, dashboards, access controls, certification and self-service boundaries around agreed business definitions. Performance, refresh cadence and user experience are considered alongside data modelling so BI remains usable at scale. Governance is designed to enable exploration while protecting critical metrics.
Read MoreData Warehousing
Design analytical storage for clarity, performance, history and maintainability across reporting and data products.
We define modelling, ingestion, transformation, orchestration, history management, workload isolation, security and performance according to analytical requirements. The warehouse is treated as an operated platform with monitoring, testing and change management rather than as a one-time data repository.
Read MoreData Science Application
Operationalise data-science outputs inside real products, processes and decision workflows.
We design APIs, batch or streaming integration, versioning, monitoring, data contracts, fallback behaviour and support ownership so data-science work becomes maintainable software. The surrounding user or process workflow is included because a technically accurate model does not create value unless its output can be consumed safely and consistently.
Read MoreTalk to us about Big Data
Share the business objective, current environment, known constraints and target timeline. We will use that context to identify the most relevant capability and delivery path.