- Cloud
- Perspectives
- Data & AI Value
Data & AI Value
Data isn’t for reporting—it’s for improving decisions and operations. This perspective focuses on real-time visibility and optimisation use cases that reduce downtime, energy waste and logistics cost.
Real-time operations visibility
Edge → cloud
Platform-first
Approach for scale
KPI-led
Use-case design. OEE, energy, service level
Pilot → rollout
Model across sites/lines
Data quality
Security & operating model
Operational value from data & AI
Transform operational data into measurable business outcomes through real-time visibility and predictive intelligence
Reduce unplanned downtime with predictive signals
Lower energy cost with anomaly detection and baselines
Improve planning and distribution decisions
Enable near real-time action instead of late reporting
Scale from one pilot to multiple sites confidently
Prove ROI with KPIs and measurement cadence
What you get
Comprehensive deliverables from data inventory through pilot execution and scale roadmap
Data & AI value deliverables
- Data/source inventory
- PLC/SCADA/sensors/ERP/MES/CMMS, etc
- Real-time event model
- Taxonomy (shared definitions)
- KPI baseline
- OEE, downtime, energy per unit, OTIF, etc
- Use-case portfolio
- Impact × effort × data readiness
- Pilot scope
- Site/line/equipment + success criteria
- Platform architecture
- Edge → cloud ingestion, storage, analytics
- Operational dashboards
- And alerting approach
- Scale roadmap
- Sites, governance, operations
- CSI backlog
- Continuous improvement + next use cases
How it works
Structured approach from discovery through pilot delivery and multi-site scale
Discover
Target KPIs, current pain points, available data
Design
Platform blueprint + event model + quality/security
Pilot
Measured delivery for selected use cases
Operate
Pipelines, alerts, SOP/response loop
Scale
Expand across sites/lines and use-case portfolio
Align
Joint operating model, governance cadence, KPI ownership
Data & AI service catalogue
Comprehensive services for industrial data platforms and operational intelligence
SRV-021
Industrial IoT Data & Real-Time Operations Platform
An industrial IoT data and monitoring platform service that collects real-time data from field devices and sensors, normalises and enriches this data to make it usable for dashboards, alarms and analytical models; providing operational visibility.
Field Discovery & Protocol Analysis
Edge Architecture Design
Ingestion & Streaming Pipeline Design
Data Model & Metadata Standardisation
SRV-018
Predictive Maintenance & Production Intelligence
A predictive maintenance and production analytics service that uses machine and production line data to identify faults and performance anomalies in advance, reduces unplanned downtime, and transforms maintenance processes into data-driven operations.
Discovery & Asset Mapping
Data Integration & Modelling
Modelling & Prediction
Validation & Tuning
SRV-019
Energy & Sustainability Intelligence
A service that monitors energy consumption and environmental indicators at facility, line and equipment level, identifying wastage points, inefficient operating models and improvement opportunities; delivering analytics reporting and optimisation aligned with sustainability goals.
Discovery & Measurement Mapping
Target Model & KPI Design
IoT & Data Integration
Analysis & Optimisation
SRV-020
Supply Chain & Distribution Optimisation
A supply chain analytics service that analyses supply, inventory, order and distribution data to provide optimisation across inventory levels, warehouse locations and distribution routes; aiming to improve service levels whilst reducing costs.
Discovery & Data Mapping
Target Model & KPI Definition
Data Model & Pipeline Design
Inventory Optimisation & Demand Forecasting
RELATED SERVICE
SRV-010: AI & Data Assessment
An assessment service that produces an actionable AI and data transformation plan by analysing current data assets, data architecture and business objectives to identify suitable use case scenarios, requirements and roadmap for artificial intelligence and analytics.
Data & AI journey pathways
Strategic progression from readiness through pilot delivery and multi-site scale
- Industrial IoT Data & Real-Time Operations Platform (SRV-021)
- Predictive Maintenance & Production Intelligence (SRV-018)
- Energy & Sustainability Intelligence (SRV-019)
- AI & Data Assessment (SRV-010 )
- Industrial IoT Data & Real-Time Operations Platform (SRV-021)
- SRV-018/019/020
- Supply Chain & Distribution Optimisation (SRV-020)
- Revenue Forecasting & Dynamic Pricing Optimisation (SRV-022)
Data & AI resources
Download templates, worksheets, and frameworks for operational intelligence programs
OEE loss tree template
Downtime classification guide
Energy baseline worksheet
Use-case prioritisation matrix
Pilot scope canvas + KPI sheet
Data source inventory template
Frequently asked questions
Common questions about industrial data platforms and operational intelligence
Do we need a platform first, or can we start with a use case?
Both approaches are valid. Platform-first (SRV-021) works best when planning multiple use cases across sites—it establishes scalable infrastructure, shared data model, and governance from the start. Use-case-first (SRV-018/019/020) is appropriate for proving value quickly with a specific pain point, then evolving to platform later. Consider platform-first if you have 3+ use cases planned, multiple sites, or need enterprise governance. Choose use-case-first for rapid ROI demonstration or when organizational buy-in requires proof points.
What minimum data sources are required?
Requirements vary by use case. Predictive maintenance needs equipment sensors (vibration, temperature, pressure) plus maintenance history from CMMS. Energy optimisation requires utility meters, production counters, and process parameters. Supply chain optimisation needs ERP/WMS data, shipment tracking, and demand signals. Most pilots start with 3-5 core data sources. During discovery (SRV-010), we map available sources against use-case requirements and identify gaps. Often existing data is sufficient—the challenge is access and integration, not availability.
When should we choose real-time vs batch?
Choose real-time (streaming) when decisions require immediate action: equipment failure prevention, quality defect detection, safety alerts, or production line optimisation. Real-time enables sub-minute response with automated alerts and workflows. Use batch processing for historical analysis, reporting, trend analysis, and optimisation that doesn’t require immediate response. Many solutions use hybrid: real-time for operational dashboards and alerts, batch for deep analytics and model training. Cost and complexity increase with real-time, so align architecture to decision latency requirements.
How do you define pilot success criteria?
Success criteria must be measurable, time-bound, and tied to business outcomes. For predictive maintenance: reduce unplanned downtime by X%, increase MTBF by Y%. For energy: reduce consumption per unit by Z%, identify N waste sources. Establish baseline metrics before pilot, define target improvement, and set measurement cadence (weekly/monthly). Include technical metrics (data quality, model accuracy, alert precision) and operational metrics (user adoption, response time). Typical pilot duration is 8-12 weeks with weekly progress reviews and final ROI validation.
How do we measure ROI (OEE/energy/OTIF)?
ROI measurement requires baseline establishment, intervention tracking, and outcome attribution. For OEE: measure availability, performance, and quality losses before and after predictive maintenance implementation. For energy: compare consumption per unit across similar production runs. For OTIF: track on-time and in-full delivery rates with and without optimisation. Use control groups when possible (optimised vs non-optimised lines/routes). Calculate hard savings (reduced downtime cost, energy bills, expedited shipping) and soft benefits (improved customer satisfaction, reduced manual effort). Typical payback period is 6-18 months for operational intelligence investments.
How do you handle security and access in OT/IT environments?
Security requires layered approach respecting OT/IT boundaries. Edge data collection uses read-only connections to OT systems, minimising production impact. Data flows one-way from OT to IT through DMZ/data diode. Cloud platform uses role-based access control (RBAC), encryption at rest and in transit, and audit logging. For highly sensitive environments, we deploy on-premises or hybrid architectures. Integration with existing identity providers (Active Directory, SSO) ensures consistent access management. Regular security reviews, penetration testing, and compliance validation (SOC 2, ISO 27001) are standard practise.
Turn operational data into measurable outcomes
In 15 minutes, we’ll identify data sources and the highest-ROI first pilot