Do we need a data platform before starting AI work?

Not always, but AI outcomes depend on data quality and access. We frequently deliver a focused data foundation in parallel with the first AI use cases.

How do you handle data privacy requirements?

Privacy and residency requirements are captured at design stage and enforced through architecture, access control, masking, and retention rules.

Decisions supported by trusted data

Data and Analytics

Turn enterprise data into actionable insight using analytics, dashboards, data platforms, and intelligent decision-support systems.

Overview

How we approach it

Most organisations hold more data than they can use. Dariyo builds the platforms, models, and governance needed to make enterprise data reliable, accessible, and decision-ready.

We deliver analytics that reach the operational level, where decisions are actually made, rather than reporting that stops at executive summaries.

Typical outcomes

A single, governed source for core enterprise metrics
Faster reporting cycles and reduced manual consolidation
Analytics embedded in operational decision-making
A data foundation ready for AI initiatives

Capabilities

What this practice covers

Capability areas can be engaged individually or combined into a single delivery programme.

Data strategy and governance

Data ownership, quality standards, catalogues, lineage, privacy controls, and governance operating models.

Data platform engineering

Warehouses, lakehouses, pipelines, and integration layers designed for scale, security, and cost control.

Business intelligence

Executive and operational dashboards, self-service reporting, and metric definitions agreed across the business.

Advanced analytics

Segmentation, forecasting, anomaly detection, and scenario modelling applied to specific business questions.

Decision support

Command centre views, alerting, and embedded analytics that place insight inside operational workflows.

Data quality and integration

Master data alignment, validation rules, reconciliation, and integration with core enterprise systems.

Service catalogue

Services in this practice

A single engagement can combine advisory, delivery, and managed operations.

Data maturity assessment
Data strategy and governance
Data warehouse and lakehouse design
Data pipelines and integration
Master and reference data management
Business intelligence and dashboards
Operational and executive reporting
Advanced and predictive analytics
Geospatial analytics
Data quality management
Analytics enablement and training
Managed data platform services

Consulting approach

A delivery method that carries initiatives through to operation

We combine strategic consulting with hands-on engineering to move initiatives from concept to measurable operational results.

Discover

Understand business objectives, operating context, constraints, and the outcomes that define success.

Assess

Review current systems, data, security posture, processes, and capability to establish a factual baseline.

Design

Define target architecture, solution requirements, security controls, delivery sequence, and business case.

Implement

Build, integrate, test, and deploy in controlled phases with governance and knowledge transfer throughout.

Operate

Run, monitor, and support the environment against agreed service levels, with clear escalation paths.

Optimize

Measure outcomes, tune performance and cost, and extend capability as requirements evolve.

Each stage produces documented outputs — assessments, architectures, backlogs, runbooks — so decisions and knowledge stay with your organisation.

FAQ

Data and Analytics questions

Practical answers about how engagements are scoped, delivered, and supported.

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