FinOps for Data Center

Data Ingestion for Data Centers requires a fundamentally different approach than for public cloud environments, primarily due to the nature of on-premises infrastructure.

Data Center reporting must aggregate and normalize information from multiple disparate systems, each with inconsistent formats, and data silos can further complicate ingestion efforts.

Additionally, cost structures in Data Centers often include significant fixed components – such as depreciation and facility leases – that must be amortized appropriately.

Examples of data source include:

• Power monitoring systems
• Hardware asset management databases
• Capacity planning tools
• Server utilization metrics from hypervisors
• Network and storage monitoring systems

Within a Data Center, the method for allocating costs back to internal consumers often varies based on the type and nature of the cost or service. Practitioners will likely encounter a combination or variation of fixed allocation, pure pass-thru consumption, subscription, and defined recovery units.

Historically siloed cost centers must be bridged, as end-to-end Data Center costs often span IT, facilities management, and geographically distributed operations.

Additionally, there is often a stronger emphasis on allocating costs related to fixed assets and the foundational components of platforms, rather than on consumption-based service units.

FinOps Practitioners require continuous access to Data Center specific metrics, including:

• PUE (Power Usage Effectiveness)
• Data center utilization (rack space, power capacity)
• Server utilization by workload
• Cooling efficiency
• ITSM metrics e.g. incident problem change management
• Service provider metrics e.g. SLA performance, service level credits
• Project cost reporting
• License utilization metrics

Temporal reporting presents challenges as Data Center costs operate on multiple timescales – including capital expenses follow depreciation schedules, operational expenses may be monthly or quarterly, and capacity planning operates on annual or multi-year horizons.

Anomaly management in Data Centers tends to focus on the consumption, utilization, and availability of the individual infrastructure components that make up a system or support an end-to-end business process.

Data Center anomalies for FinOps Practitioner consideration include:

• Unexpected invoices from shadow IT
• Continued unplanned expense after business divestiture of data center assets
• Power consumption spikes,
• Cooling inefficiencies
• Supply chain disruption (Geopolitical, pandemic)
• Software license over/underallocation

Estimating solutions and services within a Data Center typically requires input from multiple disciplines and data sources, including Facilities, Hardware Engineers, Data Center Technicians, Service Management, and Commercial/Contract Management teams.

Planning horizons are fundamentally different, typically requiring months or years of lead time combined with the reality that adding capacity will physically take longer.

Further adding to the complexity, some of these functions may be performed by third-party providers, requiring additional coordination and data integration.

Similar to Planning & Estimating, Forecasting in the Data Center requires input from additional collaborators and the integration of data with different temporal characteristics compared to cloud-native services.

Methodological differences include:

• Reliance on longer historical trending due to the relative stability of Data Center environments
• Need to forecast both demand (workload growth) and supply (infrastructure capacity & SI ability to deliver)
• Requirements to balance both under-utilization risks (stranded capacity) and over-utilization risks (service disruption)

When forecasting for services delivered from a Data Center, practitioners should consider space, power, and cooling constraints that create “hard limits” on scaling, along with hardware refresh cycles and depreciation schedules that impact the financial representation of costs.

In the context of Data Center budgeting, you would typically coordinate capital budgets for infrastructure acquisition, account for depreciation of existing assets, plan for operational expenses (power, cooling, staff), and budget for maintenance and support contracts, including licensing.

Budget cycles are typically aligned with capital planning processes, infrastructure refresh schedules, facilities maintenance schedules, and support contract renewals.

It’s worth noting that Budgeting, together with Planning & Estimating and Forecasting can be seen to overlap as parts of traditional Capacity Planning.

Benchmarking for Data Centers requires industry-standard metrics and KPI including:

• Power Usage Effectiveness (PUE); a ratio of total facility power to IT equipment power
• Data Center Infrastructure Efficiency (DCiE)
• Space utilization efficiency
  (kW/rack or kW/square foot)
• Cost per kW of power capacity
• Total cost of ownership per rack

Benchmarking normalization techniques must account for regional differences in power and real estate costs, facility age, different redundancy levels (Tier I-IV), and

varying maintenance and staffing model practices.

To enable Unit Economics in the Data Center, it is essential to calculate the Total Cost of Ownership (TCO) for each service or product and link it to a relevant business metric – such as per user, per transaction, per VM-hour. Resource efficiency metrics – such as cost per GB stored or cost per virtual CPU – could also provide valuable insights.

Because TCO is rarely available by default in Data Center environments, it must be derived. This involves annualizing capital expenditures (e.g., hardware, facilities) and incorporating all relevant operational expenditures (e.g., power, maintenance, labor) for the specific service.

A distinct challenge in Data Center unit economics is the treatment of fixed versus variable costs. Many Data Center costs are fixed regardless of utilization. As a result, unit costs generally decrease as utilization increases, unlike in many cloud environments where costs tend to scale more directly with consumption.

When determining which hosting environment to deploy to – whether Data Center, private cloud, public cloud – enterprises should establish a workload placement strategy.

While cost is an important factor, it is one of several considerations in the decision-making process. The Architecture  function typically defines additional criteria to inform a decision model.

The FinOps practitioner plays a key role by contributing cost analyses for each hosting option under consideration, supporting a more informed and balanced hosting strategy.

Key Architecting & Workload Placement considerations for constructing Scopes within the data center include:

• Balancing specialized hardware performance benefits against standardization advantages
• Creating appropriate isolation boundaries between tenants or applications
• Designing physical infrastructure for technology refresh with minimal or no application disruption

Usage Optimization in the Data Center requires principles consistent with those applied in cloud environments – a defined optimization strategy that balances cost, quality, performance, and sustainability.

However, two considerations require specific attention:

• Data Centers operate within fixed physical constraints. Facilities, servers, network switches, and SANs must be provisioned to meet both current and anticipated peak capacity and performance demands. Scaling is limited to the infrastructure physically installed on the Data Center floor, making accurate sizing and efficient utilization critical components of any optimization strategy.
• From a sustainability standpoint, there is growing value in adopting power management strategies. Reducing unnecessary workload activity can help lower energy consumption costs and carbon emissions.

This Capability is primarily led by the Engineering Core Persona, with input from IT Financial Management (ITFM) Allied Persona.

Data Center cost optimization focuses on negotiating bulk hardware purchase agreements, optimizing power contracts, leveraging volume licensing discounts, and

managing facilities contracts.

FinOps Practitioners should incorporate the following considerations when developing a Rate Optimization strategy:

• Power procurement optimization (time-of-use rates, demand response programs)
• Hardware standardization to improve procurement leverage
• Vendor consolidation to increase purchasing power
• Support and maintenance contract optimization

Data Center environments often involve managing a wide range of licensing models including:

• Instance-based licensing models for physical server, OS, cluster, per CPU core, per GB RAM
• Role based licensing models such read-only, report developer, app developer, administrator
• OEM licenses bundled with hardware

FinOps Practitioners should incorporate the following considerations when developing a licensing strategy:

• Software audit preparation and management
• License inventory reconciliation processes
• Documentation of license entitlements and deployments
• Management of license transfers during server decommissioning

Data Center sustainability requires direct measurement of power consumption and energy sources, water usage monitoring for cooling systems, E-waste management for hardware disposal, and supply chain sustainability assessment.

The Configuration Management Database (CMDB) often serves as a source of hardware inventory data that can be used to build an internal emissions model.

Key sustainability metrics include:

• Carbon emissions (Scope 1, 2, and relevant Scope 3)
• Power Usage Effectiveness (PUE)
• Water Usage Effectiveness (WUE)
• E-waste recycling rates and circular economy metrics

FinOps for Data Center requires awareness of organizational dependencies with facilities management teams, coordination with capital planning committees, and close collaboration with procurement and vendor management.

FinOps Practitioners should consider adapting their processes for longer planning and optimization cycles aligned with capital investments, asset lifecycle management into financial practices, and incorporating fixed assets into showback/chargeback models. Focus on data integration before optimization along with an emphasis on governance policies for major capital investments.

Traditional roles involved in Data Center management and governance should be up-skilled in FinOps when applying FinOps to Data Center. Additionally, FinOps practitioners will need to be up-skilled in the specialized knowledge related to Data Centers to inform considerations required for how to best apply FinOps concepts.

Policy development considerations include infrastructure standardization requirements, hardware provisioning approval processes, capacity management guidelines, and hardware lifecycle and refresh policies.

Governance guidelines should bridge traditional IT governance boards, multiple vendors, capital expenditure approval committees, and sustainability considerations.

Risk guidelines should take into consideration hardware asset lifecycle management from procurement to decommissioning, capacity reservation and allocation processes, power and cooling management, and hardware exception management.

Invoicing & Chargeback for Data Centers presents fundamental differences compared to cloud environments. FinOps Practitioners need to create “synthetic” internal invoices rather than processing consumption based vendor bills. They are required to translate capital expenses into operational chargeback models, and take into account longer amortization periods for infrastructure investments along with the complexity of allocating shared facilities costs.

IT Asset Management (ITAM) and Facilities Management involvement should be included as part of an organization’s FinOps Maturity Assessment. This helps ensure that existing processes are well understood and that physical infrastructure is considered holistically across traditional organizational boundaries.

FinOps Assessment for Data Centers requires a specialized evaluation areas including:

• Capital planning and investment optimization processes
• Infrastructure standardization and modularity
• Capacity management maturity
• Facilities cost optimization approaches

FinOps for Data Center reflects a more complex and entrenched ecosystem than public cloud. It requires tight coordination between the FinOps team and Allied Personas such as ITFM, ITAM, ITSM, Security, and Sustainability functions. Success depends on orchestrating these roles to drive financial accountability, optimize resource usage, and align Data Center spending with broader business goals.

Optional logical grouping; Identifier for an individual datacenter or in smaller environments the failure domain, power domain, or independent cluster.

Cost allocated. Additional costs could be added in here for Data Center allocation. Generally zero for usage lines, can be the purchase price for purchase lines.

Identifier for the owner of the datacenter estate. Should be used as an index to identify the P&L organization owning a Data Center or set of Data Centers. In small environments will likely match HostProviderName, larger environments may map to different subsidiaries “Acme Asia-Pacific Infrastructure Pty Ltd”, “Acme North America Technology Operations Inc”.

Represents the charge currency of internal pricing (“USD”, “CAD”, “EUR”, …etc).

Billing interval (monthly or quarterly); Should be considered in the amortization period of the Data Center itself.

Classification of charge (“Usage”, “Purchase”); Primarily usage category, as taxes, corrections, credits are less likely in the Data Center

Commonly used to differentiate corrections from regularly incurred charges.

Indicates how often a charge will occur. The Charge Frequency is commonly used to understand recurrence periods (e.g., monthly, yearly), and differentiate between one-time and recurring fees for purchases.

The start/end time period for when the usage occurs (hourly or daily). Likely artificially batched for capital depreciated asset usage.

The volume of a SKU associated with a resource or service used in vCPU-hours, GB-months, GB transferred, etc.

Represents the measurement unit of usage (like “GB”) for a SKU associated with a resource or service.

SKU level charge if including discounts, amortized costs, or treating depreciation as a prepaid purchase. Zero for purchase lines, the cost as calculated by the rate card by usage amount for usage lines.

Organisation or subsidiary that operates, owns, or leases the physical infrastructure.

The cost calculated by multiplying ListUnitPrice and the corresponding PricingQuantity. Generally zero for usage lines, can be retail price for purchase lines.

A SKU specific suggested unit price for a single PricingUnit. Pricing at retail price for the FinOps Practitioner that wants to track retail pricing.

The volume of a given SKU associated with a resource or service used or purchased (vCPU-hours, GB-months, GB transfer), based on the PricingUnit.

SKU level specified measurement unit (GB) for determining unit prices, indicating how the provider rates measured usage and purchase quantities.

The physical data center ID (internal)

The physical data center Name (internal)

SKU index identifier within the physical data center

SKU name identifier within the physical data center

Category of the resource (VM, Volume) or SKU representation level (Cluster)

Linked to layer (“Compute”, “Databases”, “Networking”). Broad classification, derived from the rate card.

Internal name of the offering or tier. Linked to layer (“Compute”, “Databases”, “Networking”).

Entity providing the service “Internal Platform Engineering – Compute Services”, “AI Platform Services Team”. Linked to layer (“Compute”, “Databases”, “Networking”).

Secondary classification of the ServiceCategory for a service based on its core function (“Virtual Machines”, “NoSQL Databases”, “Content Delivery”). Linked to layer (“Compute”, “Databases”, “Networking”).

Used to identify a group of services for an individual data center. Identifier for organisational subdivisions of the estate.

Used to name a group of services in a data center. Identifier for organisational subdivisions of the estate.

Ownership or metadata for allocation/reporting/invoicing/chargeback.