Our Data Mindset
Data Mesh Architecture
We address the fundamental challenge of data ownership and governance by embracing a Data Mesh paradigm. In this approach, we treat data not as a byproduct but as a core product with intrinsic value. Each data product is owned and managed by the teams closest to the business, who take responsibility for quality and access, while we provide the tools and guidance to help deliver high-quality data products reliably.
| Concept | What We Do | What You Get |
|---|---|---|
| Data-as-a-Product | We transform scattered source information into domain-specific, standardised data assets (models, semantic layers, reports, dashboards, etc.) intentionally packaged with metadata, documentation, and defined interfaces. | You work from shared, federated definitions, ensuring every user references the same validated models for better alignment, clearer reporting, and more reliable cross-functional analysis. |
| Decentralised Ownership & Self-Service | We equip domain teams with robust, self-service infrastructure and automated BI toolkits, offering expert consulting and continuous technical guidance on enterprise standards and platform best practices. | You have the flexibility to build your own data products. This eliminates centralised bottlenecks, enabling you to validate and deploy your BI solutions with unprecedented speed and efficiency. |
| Federated Governance | We implement a common data contract and computational governance layer that establishes policies on data usage, classification, retention, auditing, and security and equips teams with the infrastructure, code, and templates to apply them in their workflows. | You benefit from consistent governance across your company, combining central oversight with local execution to ensure compliance and interoperability. |
Analytics Engineering
& DataOps
Our transformation layer is engineered using robust software engineering practices, including version control, automated testing, CI/CD pipelines, and modular design, ensuring reliability and maintainability at scale. This setup enables rapid iteration, safe deployments, and consistent delivery of production-ready datasets for downstream consumption.
| Concept | What We Do | What You Get |
|---|---|---|
| Analytics-as-Code | We treat models, metrics, dashboards and pipelines as code, managing them as versioned, trackable assets. Configurations, definitions and metadata are captured in YAML or JSON when possible. | You rest assured that all changes are versioned, auditable, and reversible, keeping analytics assets consistent and traceable while your teams iterate with clear dependencies and documented configurations to deliver trustworthy data products. |
| Modular Modelling | We enforce a modular data architecture by partitioning all transformation logic into explicitly modelling layers (staging, intermediate, mart), using dependency-driven design to establish component relationships for reuse. | You benefit from lower query costs and reduced operational overhead, as every calculation is computed once and applied consistently across your analytics workflows. |
| Cloud-First ELT | We implement an ELT (Extract, Load, Transform) workflow that pushes computation to the cloud data warehouse, embedding DataOps principles with automated monitoring, logging, and error handling. | You gain scalable data processing, automated validation of every load and transform step, and full visibility into pipeline health and failures. |
| Data Quality Automation | We embed data tests within each domain’s data pipelines and treat them as part of the product lifecycle. These checks run automatically through CI/CD, where every code change triggers validation steps that verify data quality before deployment. | You gain confidence knowing that broken reports and corrupted data won’t reach end-users. Data source issues are caught proactively before they become business problems, supporting faster releases and maintaining consistent quality standards across domains. |
Semantic & Metric Layer
We implement an operational abstraction called the Semantic Layer, which maps raw and transformed datasets into a consistent, business-focused view. By standardizing metrics, definitions, and key business concepts, it lets analysts, BI tools, and applications query and combine data across sources without needing to understand the data’s backend complexity, improving reporting accuracy, reducing ambiguity, and enabling faster, informed decisions.
| Concept | What We Do | What You Get |
|---|---|---|
| Metric Governance | We ensure all your core metrics are defined only once as centralised, version-controlled entities that encode business logic, aggregation rules and interdependencies. | You are protected from metric drift by design, as every consumer (report, dashboard, AI agent, etc.) uses the exact same calculation, ensuring consistency across your entire organisation. |
| Tool Agnostic Access | We make all previously defined metrics directly queryable by any downstream BI tool, spreadsheet, or custom application, allowing access via standard SQL or built-in connectors. | You can integrate new BI tools effortlessly, accessing the same validated metrics across platforms, while ensuring consistency in any analytics context. |
| Natural Language Grounding | We enrich metric and model definitions with business-aligned metadata that maps terminology, hierarchies, and relationships in a way AI systems can interpret. | You can leverage AI agents to instantly translate plain-language questions into accurate SQL queries that retrieve governed metrics, delivering insights your teams can trust. |
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Telephone:
+44 7865 139304
Mail:
contact@roipartners.tech
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