A regional digital-services provider faced a linked commercial and engineering problem: demand was growing while power, cooling, redundancy and vendor constraints limited usable capacity. Marketways brought those conditions into one growth and performance programme. The work connected demand timing with infrastructure headroom, operating efficiency and transition risk.
The engagement objective
Through the initial discovery, Marketways defined the objective: integrate commercial demand with power, cooling, capacity and investment decisions.
How Marketways translated the problem
We began with a practical question: How should compute capacity be placed and cooled under changing demand? The first analysis used rack load, temperature, power, cooling, incidents, contracts and pipeline.
That evidence could not be read in isolation. IT load, ambient conditions, redundancy and contractual headroom interact. Aggregate traffic hides location, application, device and busy-hour constraints.
Average utilisation concealed peak coincidence, redundancy commitments and stranded capacity. We linked demand timing to power, cooling and failover conditions before describing headroom as commercially available.
We did not judge each component by its isolated KPI. We examined how people, assets, decisions and constraints affected one another, then used the evidence to test whether an apparent improvement would strengthen the complete system or merely move cost, pressure or risk elsewhere.
How the engagement developed
The initial work on data-centre power, cooling and capacity exposed dependencies with network demand and capacity forecast, technology and vendor transition assurance. Treating them as separate recommendations would have left the operating trade-offs unresolved.
- Data-centre power, cooling and capacity: Protect service levels while improving energy and capital efficiency.
- Network demand and capacity forecast: Time investment and optimisation around actual service demand.
- Technology and vendor transition assurance: Make transition risk, fallback and sequencing explicit.
Evidence we examined
- Rack load, temperature, power, cooling, incidents, contracts and pipeline.
- Traffic, cells, customers, devices, quality, geography and planned change.
- Architecture, dependencies, contracts, incidents, volumes and migration tests.
Industry conditions we accounted for
- IT load, ambient conditions, redundancy and contractual headroom interact.
- Aggregate traffic hides location, application, device and busy-hour constraints.
- Legacy interfaces and vendor-specific controls may be poorly documented until migration begins.
How our engagement contributed to business impact
We connected every method to a decision and a business measure. The organisation could assess the engagement through operating results as well as model performance.
- Data-centre power, cooling and capacity
- Method: Forecasting, Risk & Optimisation, Data Foundations & Business Intelligence.
- Evidence: Rack load, temperature, power, cooling, incidents, contracts and pipeline.
- Decision supported: Protect service levels while improving energy and capital efficiency.
- Impact measure: Availability, PUE, thermal risk and sellable capacity.
- Network demand and capacity forecast
- Method: Forecasting, Risk & Optimisation, Data Foundations & Business Intelligence.
- Evidence: Traffic, cells, customers, devices, quality, geography and planned change.
- Decision supported: Time investment and optimisation around actual service demand.
- Impact measure: Service quality, utilisation, avoided congestion and capital efficiency.
- Technology and vendor transition assurance
- Method: Process, Workflow & Systems, Research & Evidence Collection.
- Evidence: Architecture, dependencies, contracts, incidents, volumes and migration tests.
- Decision supported: Make transition risk, fallback and sequencing explicit.
- Impact measure: Migration stability, fallback readiness and dependency reduction.
Implementation
The engagement was structured as capacity plan and investment gates. We connected the analysis to the decisions, operating constraints and measures that the organisation would continue to use.
How success was assessed
The overall assessment considered reliable sellable capacity and energy-efficient growth. The supporting measures included:
- Availability, PUE, thermal risk and sellable capacity.
- Service quality, utilisation, avoided congestion and capital efficiency.
- Migration stability, fallback readiness and dependency reduction.
Services and methods used
Services: Operational Performance Diagnostic, Risk & Uncertainty Modelling, Sales Forecasting & Demand Planning, Business Feasibility Study, Business Systems Design & Architecture, Decision Assurance.
Methods: Forecasting, Risk & Optimisation, Data Foundations & Business Intelligence, Process, Workflow & Systems, Research & Evidence Collection.