FinOps for Data Center: Applying the FinOps Framework

For one or more Data Centers, costs may be spread across multiple bills, cost centers, and stakeholders, with no centralized system to consolidate and present the information coherently.

Consider evaluating whether a single Data Center Infrastructure Management (DCIM) tool is in place; if not, this may be a worthwhile starting point. Conduct an audit to confirm that all cost elements are captured—including software licenses and ancillary charges.

Allocation

Without a complete and unified view of all billing sources, building a comprehensive allocation model can be challenging. However, as visibility improves, an allocation model can evolve alongside it.

As new cost elements are identified, begin by classifying each as either application-specific or shared. Introduce tagging or metadata practices to help associate costs with the appropriate business unit or cost center.

Reporting and Analytics

In many cases, there may be limited or no application- or product-specific reporting on Data Center costs shared among Finance, Leadership, or Product Owners.

Depending on the level of available resources, organizations might begin with basic dashboards or move toward automated reporting. Include visibility into application-specific, shared, and unclassified costs, and ensure reporting reaches a manageable, engaged group of stakeholders.

• Are costs being refreshed at an appropriate frequency?
• Are both usage and cost metrics being captured adequately?
• Is the granularity of captured data sufficient to support meaningful analysis?
• Is this data accessible to the systems that support unified allocation, reporting, and analytics across FinOps Scopes?

Allocation
For practitioners entering the walk stage of maturity, establishing a regular cadence for allocation and improving transparency are important steps. However, it is equally important to avoid basing business decisions or behaviors solely on allocations. Instead, allocations should be treated as one of several data points that support informed decision-making.

Reporting and Analytics
As Data Ingestion and Allocation practices mature, the development of associated reporting and analytics tooling can be scheduled accordingly.
Consider the following:

• Are dashboards, routine reports, and automated alerts sufficient to enable timely, data-driven decisions about Data Center services?
• Are the appropriate stakeholders engaged in the development and lifecycle management of these tools as part of a broader communication strategy for Data Center cost and usage?

In cloud scenarios, practitioners typically receive standardized billing and usage data covering all interactions with the cloud service provider (CSP) through a single API. In contrast, Data Center reporting must aggregate and normalize information from multiple disparate systems, each with inconsistent formats, granularity, and update frequencies.

Potential data sources may include:

• Power monitoring systems (e.g., UPS, PDUs, DCIM tools)
• Hardware asset management databases
• Facilities management systems (HVAC, cooling)
• Capacity planning tools
• Server utilization metrics from hypervisors
• Network and storage monitoring systems
• ITSM platforms (CMDB, Service Desk, Capacity Planning)
• Tools and dashboards from Systems Integrators or Managed Service Providers
• Labour rate cards for SI/MSP/project resources
• Contract management systems (SI, BPO, managed services, contract labor)
• HR systems
• Software licensing and consumption data

The granularity of available data can vary significantly across these systems, making normalization a complex task. While the FOCUS™ standard may serve as a useful reference, it currently requires some level of mapping/ customization to accommodate Data Center–specific metrics.

Data silos can further complicate ingestion efforts. Each Facility Operator (FO) may introduce unique variables based on regional regulations, procurement practices, environmental standards, and waste management protocols. These factors must be accounted for when designing ingestion pipelines.

Time-series data collection is also more fragmented: some metrics (e.g., power, temperature) may be available in real time, while others (e.g., facility costs, depreciation) may only update monthly. Organizations must establish processes to align these temporally inconsistent datasets.

Finally, cost structures in Data Centers often include significant fixed components—such as depreciation and facility leases—that must be amortized appropriately to enable meaningful comparisons with variable cloud costs during analysis and reporting.

Type and nature of costs to be allocated

• Examples of Fixed Costs:
  • Physical hardware
  • Perpetual software licenses and maintenance
  • Facilities (real estate, physical security, cooling)
  • Costs are more easily quantifiable and predictable over their individual lifecycle
    • Rent/leasing charges set via contract
    • Asset depreciation schedules created on date of purchase
• Examples of Variable Cost:
  • Facilities Variable costs (power, cooling)
  • Projects/Project labor
  • Consumption based managed services from 3rd party suppliers
  • Subscription licenses

Definition of the entity to be “charged”

Practitioners may already be familiar with shifting allocation targets as projects begin and end or as products transition from development to production. However, when supporting broader Data Center services, additional factors may come into focus, such as:

• Mergers, Acquisitions and Divestments – the creation, amalgamation, and removal of business unit entities
• Changing business conditions and priorities through various phases of the business cycle
  • Expansion period – funds distributed to individual business units, given more accountability and agility to “grow the business” through strategic projects
  • Contraction period – centralisation of budgets, de-duplication of roles, projects folded under run and maintain
• Shifts in accountability for costs of implementation versus ongoing support

Methodology of recovery

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 the following approaches:

Fixed Allocation:

• Simplest and most common form
• Roll-up all costs to single figure, divide by some simple metric
  • Business unit, department, geographic location
• No consideration/ accountability for actual consumption behavior
  • Low users of a service pay same price as high users of a service
• Example: Fixed overhead charge for security services
  • Security Operations Center provides significant benefit to the organization as a whole

Pure Pass-thru/Consumption:

• All costs are visible, allocated and accounted in the period they are accrued
• “True” end use cost is defined and transparent to all consumers
• Requires granular, timely consumption-based reporting of all cost elements
  • Challenging where there is no centralized, consolidated cost and usage management platform
  • Requires amalgamation of reporting from multiple element managers and systems (facilities, DevOps/Engineering, ITSM, ITAM, SAM, Finance, HR)
• Creates variability in total cost to end users (both rate and consumption)
  • Similar pros and cons to Cloud re forecasting, budgeting and Pay-As-You-Go (PAYG)
  • Efficiencies gained by the internal provider are visible and passed through
  • Example: A server may still be functionally fit for purpose at the end of its official service life and depreciation period. In theory this results in a cheaper support cost for the same functionality. To mitigate the risk of out of warranty equipment, higher failure rates, etc. the internal provider may still offer to support the equipment, but only for non-critical systems
• Creates risk for internal providers
  • Maintaining large fixed cost investments with unpredictable “revenue” stream to recover against
  • Example: Significant investment in on-site data warehouse storage with an expected useful life (and therefore cost recovery period) of 5 years is made redundant through industry innovations or changing data consumption patterns
  • Example: Fixed Data Center costs during and after a Cloud migration. Stranded costs of physical equipment, potential contract termination charges for facilities leases are critical considerations for such activities.

Subscription:

• Largely how systems integrators, outsourcers and managed service providers operate
• Limited visibility into the individual cost elements that make up the service
• Creates rate predictability for end users
• Potentially creates a “margin” that someone in the organization must monitor:
  • Potential to incentivize behavior and reward Product innovation (Profit Center versus Cost Center)
  • Efficiencies accrue to the internal provider, not the consumer
  • Internal provider may price in risk of “revenue” variability
  • Internal consumers may benefit from increased innovation and cost predictability, at the expense of cost transparency and efficiency
• Actual Cost-of-Good-Sold (COGS) needs to be tracked separately for audit purposes. “What you charge” does not equal “What it costs”
• Example: Enterprise Reporting and Analytics Service
  • Costs incorporate hardware, OS, storage, platform licenses, backup, monitoring, engineering
  • Internal provider may recover costs using a monthly fee per user, or a per-report charge using historical data to forecast future usage
  • Any efficiencies accrue to the provider (e.g. vendor negotiations, efficiencies through DevOps, report automation, AI integration)
  • Consumers get cost predictability and a “guaranteed” level of service

Definition of Recovery Units

This activity involves creating cost units analogous to those practitioners may be familiar with in cloud environments. The actual recovery unit will vary depending on the nature and type of cost—for example:

• Whether the item is provided “as is” or with services wrapped around it
• Whether the costs are fixed or variable
• Whether the costs are predictable
• Whether the service is consumed by individuals, teams, or departments as a whole
• Allocation method used.

Examples may include:

• Per User Charge
  • Where most or all costs can be easily attributed to the actions of an individual user
• Per Physical Item
  • Network switch
    • Cost build up may only include switch, monitoring software, maintenance
  • Rack
    • Rack, cabling, floor space per SQM, power/cooling
• Per Managed Item
  • Managed Physical Server
    • Physical costs include HW, OS license, rack allocation
    • Service costs include DevOps/Engineering, realtime incident monitoring, backup
    • Rate based on choice of HW config (small, medium, large)
• Per logical item
  • Per database table
    • Cost build-up incorporates HW, OS, DevOps/engineering, DB platform license, DBA
• Per Activity
  • Per report
    • Cost buildup incorporates platform, reporting and analytics license, report templates
    • Use historical data to predict the total number of reports likely to be generated in a period, divide costs by this number plus risk

Additional Considerations

• Historically siloed cost centers must be bridged, as end-to-end Data Center costs often span IT, facilities management, and geographically distributed operations.
• Practitioners may need to evaluate the best method for identifying directly attributable costs in the absence of a centralized, consolidated cost and usage platform.
• 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.
• Clear ownership attribution—commonly achieved in Cloud through account structures and tagging—may be difficult due to fragmented or inconsistent data across systems.
• Multiple stakeholder groups are typically involved, including both the consumers of raw infrastructure that supports virtualization and those consuming the resulting VMs or containers.

As with reporting in the public cloud, practitioners require continuous access to Data Center–specific metrics, which are outlined below:

• 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 utilisation metrics

Temporal reporting presents challenges as Data Center costs operate on multiple timescales:

• Capital expenses follow depreciation schedules
• Operational expenses may be monthly or quarterly
• Capacity planning operates on annual or multi-year horizons

Comparative reporting between Data Center and cloud costs requires the use of normalization techniques to enable meaningful “apples-to-apples” comparisons. These techniques help account for differences in cost structures, depreciation models, and operational approaches.

FOCUS™ for Data Center Costs and Usage Mapping may also assist practitioners in reducing complexity in reporting and analytics—particularly where FOCUS™ has already been adopted for Public Cloud.

In contrast, Data Center environments often lack this abstraction layer for near real-time monitoring of costs. 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.

Practitioners may be asked to contribute holistically to scenarios that directly impact cost, such as:

• Financial anomalies
  • Sudden or unanticipated changes to vendor unit rates
  • Unexpected invoices or spend (e.g. shadow IT)
  • Continued unplanned expense after business divestiture, which can be caused by poor or missing cost allocations.
• Operational/Facilities anomalies
  • Power consumption spikes,
  • Cooling inefficiencies
• External Events
  • Weather
  • Supply chain disruption (Geopolitical, pandemic)
• Capacity anomalies
  • Unexpected exhaustion of physical capacity
  • Software license over/underallocation
• Performance anomalies
  • Unexpected latency or throughput issues
  • Application response times

Detection methodologies must account for:

• Longer baseline establishment periods due to the relative stability of Data Center environments
• Seasonal patterns in power and cooling that may appear anomalous but are expected
• Maintenance windows that create planned “anomalies” that should be excluded

Response frameworks require cross-functional coordination:

• Facilities teams for power/cooling anomalies
• IT operations for utilization or performance anomalies
• Finance teams for cost anomalies
• Security teams for potential security-related anomalies

In public cloud, practitioners can often define and estimate nearly all components of a cloud-native system using the CSP’s built-in cost and estimation tools. In contrast, estimating solutions and services within a Data Center typically requires input from multiple disciplines and data sources, including:

• Facilities
• Engineering
• Database
• Product Teams
• Cloud Teams
• Service management
• Commercial/Contract Management teams

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

Cost estimation for physical infrastructure could incorporate:

• Up-front capital expenditures for infrastructure
• Installation and commissioning costs
• Ongoing facilities costs (real estate, power, cooling)
• Operations staffing costs
• Hardware refresh cycles and technology obsolescence
• Current value based on depreciation schedules
• End-of-life decommissioning and disposal costs
• Licensing costs (with or without maintenance and support)

Estimation of SI and Managed Services requires:

• Defining of business outcomes expected over a fixed term, and the services required to meet those outcomes
• Defining resource units that reflect the consumption patterns of the service
• Onboarding of vendors into existing service management and vendor management frameworks
• Deployment of the service

Planning horizons are fundamentally different:

• Cloud planning can be performed in days or weeks
• Data center planning requires months or years of lead time
• Smaller increments of capacity cannot be added as easily as in cloud

When forecasting for services delivered from a Data Center, practitioners should consider:

• Multiple forecasting time frames dependent on scope
  • Facilities forecasts typically greater than 10 years due to capital planning requirements
  • Medium term for high capacity or specialised hardware, software and services
  • Short term for commodity hardware, software and services
  • Multiple forecasting models (quarterly operational, annual budgeting, multi-year strategic)
• Long-lead time hardware and software procurement cycles (public tender processes, negotiations)
• Space, power, and cooling constraints that create “hard limits” on scaling
• Regular contract renewals with differing anniversary dates, renegotiations and tender activities
• Step-function capacity increases (new racks, rows, or facilities)
• Hardware refresh cycles
• Infrastructure maintenance and end-of-life planning
• Depreciation schedules that impact the financial representation of costs

Methodological differences include:

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

Forecasting accuracy expectations differ:

• Short-term forecasts (1-3 months) should achieve higher accuracy than cloud
• Long-term forecasts (3+ years) inherently have greater uncertainty due to technology changes

• Coordinate capital budgets for infrastructure acquisition
• Account for depreciation of existing assets
• Plan for operational expenses (power, cooling, staff)
• Budget for maintenance and support contracts, including licensing

Budget cycles are typically aligned with:

• Capital planning processes
• Infrastructure refresh schedules
• Facilities maintenance schedules
• Support contract renewals

Budget variance management is complicated by:

• Fixed cost nature of many Data Center expenses
• Limited ability to scale down costs when demand decreases
• Step-function increases when capacity thresholds are reached

Holdback strategies differ significantly:

• Capital budgets typically require larger holdbacks due to procurement uncertainties
• Operational budgets may have smaller holdbacks for power cost fluctuations or emergency maintenance

Budgeting, together with Planning & Estimating and Forecasting can be seen to overlap as parts of traditional Capacity Planning.

Industry-standard benchmarks include:

• Power Usage Effectiveness (PUE) – 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

Internal benchmarking approaches differ:

• Comparison between facilities within the organization
• Historical trending of key metrics for the same facility
• Comparison of similar workloads across different environments

External benchmarking sources include:

• Uptime Institute annual surveys
• Data Center Dynamics industry reports
• 451 Research Data Center economics reports
• Gartner IT key metrics data

Normalization techniques must account for:

• Regional differences in power and real estate costs
• Facility age and design differences
• Different redundancy levels (Tier I-IV)
• Varying maintenance practices and staffing models

Scenarios of Value in Benchmarking for Data Center Scope

Multi-Site Data Center Efficiency Benchmarking

Useful to implement a comprehensive benchmarking initiative focused on Power Usage Effectiveness (PUE) as the primary efficiency metric across all facilities to compare dissimilar Data Centers across continents, with varying ages (2-15 years), sizes (1,000-5,000 racks), and redundancy levels (Tier II-IV). The FinOps team’s benchmarking approach transforms what could have been arbitrary cost-cutting into strategic efficiency investments backed by data-driven business cases.

Hybrid Cloud vs. Data Center TCO Benchmarking

When CTO & CFO debates whether to expand the existing Data Center or migrate workloads to the public cloud. The board requires a comprehensive TCO comparison to guide this strategic decision. The FinOps team’s benchmarking enables nuanced decision-making beyond simplistic “cloud vs. Data Center” debates, optimizing both cost and performance. TCO Benchmarking should also consider the value of Resilience, and the costs of Disaster Recovery and Data Security.

Industry Peer Benchmarking for Regulatory Compliance

A regional bank operates two Data Centers supporting critical financial systems. Banking regulators question whether the bank’s IT infrastructure costs are excessive compared to industry peers. The bank must demonstrate cost efficiency while maintaining regulatory compliance for system availability and data sovereignty. The FinOps team can help conduct comprehensive peer benchmarking using industry data while accounting for regulatory constraints unique to financial services.

Sustainability Benchmarking for ESG Reporting

For those companies that have commits to carbon neutrality for a defined year in the future (2030). The sustainability team needs to benchmark Data Center carbon efficiency against industry leaders and develop improvement roadmaps that balance environmental goals with cost considerations. The FinOps team develops comprehensive sustainability benchmarking that connects environmental metrics with financial implications.

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. Unused or underutilized resources should also be factored in, as they contribute to higher unit costs for the services or products delivered.

A distinct challenge in Data Center unit economics is the treatment of fixed versus variable costs. Many Data Center costs—such as infrastructure and facilities—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. Allocating these fixed overheads to usage-based metrics requires thoughtful and consistent methodology.

Time-based considerations are also critical. Organizations must account for the time value of infrastructure investments, align depreciation schedules with business value realization, and adjust forecasts to reflect the evolving nature of technology throughout the asset lifecycle.

Implementing unit economics in Data Centers can deliver the following benefits:

Within a Single Data Center:

• Cost Visibility: Precisely calculates cost per unit by allocating both fixed and variable costs to business outcomes.
• Optimization: Highlights inefficiencies and enables targeted improvements in resource utilization and operational efficiency.

Across Multiple Data Centers:

• Resource allocation using unit economics as a guide: Facilitates comparison between sites, guiding efficient workload placement and investment.
• Performance Benchmarking: Using Unit Economics to support benchmarking of unit costs and complementing with metrics such as Power Usage Effectiveness (PUE)-a standard measure of Data Center energy efficiency-across locations to identify best practices.

Across Data Center and Public Cloud (Hybrid):

• Cost Optimization: Provides granular cost insights across environments, enabling optimal workload allocation—Data Centers for steady loads; cloud resources for elasticity and innovation.
• Business Value Alignment: Connects costs directly to business outcomes, justifying hybrid strategies, supporting accurate forecasting, and informing strategic decisions on capacity and migration.
• Apples-to-Apples Comparison: By applying unit economics, organizations can compare costs and value consistently across Data Centers and Public Cloud, ensuring true apples-to-apples evaluation of different capabilities

Architecting for Cloud in the Data Center context focuses on:

Design principles:

• Emphasis on standardization and modularity to enable efficient operations
• Focus on virtualization and abstraction layers to maximize resource utilization
• Designing for known capacity constraints rather than unlimited resources

Key architectural considerations include:

• Balancing specialized hardware performance benefits against standardization advantages
• Implementing resilient designs within limited availability zones
• Creating appropriate isolation boundaries between tenants or applications
• Designing for technology refresh without application disruption

Infrastructure choices must account for:

• Long-term support requirements and vendor roadmaps
• Total cost of ownership over the expected lifecycle
• Compatibility with both legacy systems and cloud-native applications

Operational architecture requires:

• Automation frameworks to reduce manual management
• Monitoring and observability implementation for cost visibility
• Capacity management processes to optimize utilization

However, two considerations require specific attention, as they differ significantly from cloud-based optimization:

Sizing and Utilization: Unlike the perception of “infinite” scalability in cloud environments, 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.

Power Management Strategies: From a purely cost-driven perspective, the incentive to schedule or shut down workloads in a fully owned and managed Data Center may appear limited, since the infrastructure costs are largely fixed. However, from a cost and sustainability standpoint, there is growing value in adopting power management strategies. Reducing unnecessary workload activity can help lower energy consumption costs and carbon emissions—also a key consideration as environmental impact becomes a higher organizational priority.

Example Considerations – Near Real-time Failover during Disaster Recovery

For a critical system that cannot be offline for more than a few minutes, capacity must be deployable and accessible at an alternate geographical location within the business-defined RTO/RPO metrics. The metrics and performance requirements during a DR event will dictate the scale of the secondary site installation, which could range from a smaller footprint installed temporarily in a Public Cloud, to the extreme scenario of 100% duplication of all physical capacity (LAN, SAN Security and Compute) installed in a second DC (assuming the same performance, response time and capacity is required at the failover site).

In the extreme case, consideration also must also be given as to how this excess capacity can be utilized during BAU (e.g. hosting the dev/test/QA environment for the system, burst capacity for other virtual systems etc), on the understanding that such systems could be turned off at any time to make way for the failover of Production.

Considerations for Typical Workload Optimization

• A ground-up approach to optimization is required, covering elements not typically in scope for the Practitioner in the Cloud:
  • Floorspace/real-estate
  • Physical Core/Edge network switches and Security devices
  • Physical servers, SAN, backup
• Critical systems being hosted on dedicated hardware, and needing to be sized for peak workload processing
• Maximizing utilization of fixed capacity resources while providing headroom for short to medium term growth
• Right-sizing virtual machines to physical hardware
• Scheduling batch workloads to optimize resource usage

Key optimization techniques include:

• Workload consolidation to increase device utilization
• Workload placement
• Balance power and cooling requirements per rack
• Balance performance requirements across multiple farms
• Application performance tuning to reduce resource requirements
• Capacity reclamation from idle or underutilized systems

Measurement methodologies must account for:

• Hypervisor overhead and management resource requirements
• Infrastructure reserved for high availability and failover
• Performance impact of increased resource sharing
• Power and cooling considerations beyond pure compute capacity

Optimization cycles are typically longer than cloud:

• Hardware refresh cycles (3-5 years) drive major optimization opportunities
• Virtualization platform upgrades enable new optimization capabilities
• Application lifecycle management dictates optimization windows

Other Potential Areas for Optimization

While not necessarily in scope for the Practitioner, an awareness of and participation in the following activities may help surface unforeseen cost impacts on DC infrastructure

• Labor optimization (if self-managed)
  • Automation of tasks (e.g. agentic AI, DevOps)
  • Staff augmentation/contractor pools
  • Offshore/Onshore (e.g. app development, call center analysts)
• Vendor Optimization
  • Commercial/Contract structure, multisourcing versus sole-sourcing
  • Regular alignment of SLAs with business outcomes
  • Contract Management
• Software Optimization
  • Rationalization of multiple apps performing the same function
  • Pros and cons of subscription licensing versus perpetual
  • Build versus buy
  • Application of Architecting for Cloud capabilities if beneficial

Specifically, Data Center environments often involve managing a wide range of licensing models, including but not limited to:

• Various instance-based licensing models
  • Physical server, OS, cluster, per CPU core, per GB RAM
• Role based models
  • Read-only, report developer, app developer, administrator
• Perpetual licenses with ongoing maintenance
  • Asset depreciation over time, rather than expense incurred in-period
• Enterprise agreements with minimum purchase requirements
  • Centrally managed license agreement covering all physical locations
  • For multinationals, often managed globally, with in-country vendor support
  • Vendors have different (and strict) rules for BYOL to Cloud under such agreements
• OEM licenses bundled with hardware

Additional license allocation considerations:

• Mapping software licenses to physical hardware configurations
  • Requires knowledge of the underlying physical asset
• License harvesting from underutilized systems or use of Data Center and public
• Strategic timing of license renewals with hardware refresh cycles
• Evaluating Cloud Mobility options to reduce deployment costs using underutilized licenses. This includes reviewing terms and conditions that allow Bring-Your-Own-License (BYOL) or shelving of these licenses to receive cloud benefits.
• If using BYOL, plan migration increments to ensure sufficient licenses are available to provision dual parallel environments (on-prem and cloud) while migration is in progress. Under certain conditions, some publishers allow a transition period where dual platform use of licenses is permissible.

New challenges with different dimensions to Cloud:

• Varying degrees of capability regarding Software Asset Management
  • SAM is often sacrificed for other priorities, relying on manual processes and the ingenuity/site knowledge of individuals
  • Absence of a consolidated enterprise view of all software assets
  • Manual extracts from multiple vendor and client sources
  • Limited visibility on the proliferation of software deployments
  • Tracking license deployments across all customer environments (Virtual and physical, Cloud and non-Cloud)
• Inability to tag specific software types or instances as is done in Cloud
  • Complicates allocation and showback activities
• Anomaly Management is challenging without a consolidated, accurate, and current baseline view of license deployments
• Compliance
  • Software audit preparation and management
  • License inventory reconciliation processes
  • Documentation of license entitlements and deployments
  • Management of license transfers during server decommissioning

• Negotiating bulk hardware purchase agreements
• Optimizing power contracts and rates
• Leveraging volume licensing discounts
• Managing facilities contracts and real estate costs

Key rate optimization strategies include:

• 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

Financial optimization techniques include:

• Leasing vs. purchasing analysis for infrastructure
• Utility rebate programs for energy efficiency improvements
• Depreciation schedule optimization

Unlike cloud providers who manage sustainability at scale, Data Center sustainability requires:

• Direct measurement of power consumption and energy sources
• Water usage monitoring for cooling systems
• E-waste management for hardware disposal
• Supply chain sustainability assessment

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

Data Considerations:

• The Configuration Management Database (CMDB) often serves as a source of hardware inventory data—such as model and specifications—that can be used to build an internal emissions model.
• The Simple Network Management Protocol (SNMP) is a standard protocol for querying servers to assess workload performance metrics, such as CPU and memory utilization, as well as power consumption.

Organizational structure considerations:

• Integration with facilities management teams
• Coordination with capital planning committees
• Alignment with IT operations and infrastructure teams
• Collaboration with procurement and vendor management

Process adaptation considerations:

• Longer planning and optimization cycles aligned with capital investments
• Integration of asset lifecycle management into financial practices
• Development of TCO models that span both infrastructure and operations
• Implementation of showback or chargeback models for fixed assets

Cultural evolution considerations:

• Shifting from capacity-led to demand-driven planning, with FinOps considerations during the product planning and design processes
• Building financial accountability into infrastructure management
• Developing cost optimization mindsets in traditionally utilization-focused teams
• Creating cross-functional collaboration between historically siloed teams

Maturity model differences:

• Greater emphasis on process standardization at earlier maturity levels
• Focus on data integration before optimization
• Emphasis on governance frameworks for major capital investments
• Recognition of the longer timeframes needed for significant changes

• Traditional IT financial management fundamentals
• Capital budgeting and depreciation concepts
• Facilities management cost structures
• Integration with ITAM, ITFM, and DCIM disciplines

Traditional roles involved in Data Center management and governance should be upskilled in FinOps when applying FinOps to Data Center. Additionally, FinOps practitioners will need to be upskilled 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
• Server provisioning approval processes
• Capacity management guidelines
• Hardware lifecycle and refresh policies
• Data retention and custody storage policies. Including audit of data retention.

Governance structures must bridge:

• Traditional IT governance boards
• Multiple vendors in a multi-sourced environment
• Capital expenditure approval committees
• Facilities management oversight
• Environmental and sustainability governance

Enforcement mechanisms differ significantly:

• Greater reliance on process controls versus technical enforcement
• Integration with change management and CMDB systems
• Alignment with procurement and asset management workflows
• Implementation of physical access controls for infrastructure

Policy areas requiring special attention:

• Software and/ or Hardware Asset lifecycle management from procurement to decommissioning
• Capacity reservation and allocation processes
• Power and cooling management guidelines
• Technology standardization and exception management
• Architectural policies and workload placement – involvement of FinOps practitioners

• Need to create “synthetic” internal invoices rather than processing vendor bills
• Requirement to translate capital expenses into operational chargeback models
• Longer amortization periods for infrastructure investments
• Complexity of allocating shared facilities costs

Elements to consider when assessing maturity include:

• Comprehensive Data Center inventory tracking of both physical assets and resource utilization will ensure infrastructure efficiency and financial transparency, enabling more accurate TCO and value decisions to be made.
• Is there a view of all Data Center assets, maintenance contracts, and refresh cycles? Is this combined with similar cloud data for hybrid environments?
• Are facilities management data (power, cooling, space) and IT infrastructure data combined in a timely fashion for capital planning and operational expense analysis?
• Is there integration between DCIM (Data Center Infrastructure Management) systems and financial tools to provide cost visibility?
• Are there KPIs to track and report on efficiency metrics like PUE, server utilization, cost per rack unit, and carbon footprint?

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 optimization is considered holistically—across traditional organizational boundaries.

FinOps Assessment for Data Centers requires a specialized evaluation framework.

Assessment areas specific to Data Centers include:

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

Planning considerations include:

• Physical space, power, and cooling requirements
• Hardware procurement and deployment lead times
• Network capacity and connectivity planning

Cost modeling must account for:

• Initial capital expenditure for hardware
• Installation and configuration labor
• Ongoing operational costs
• Impact on shared infrastructure resources

It requires tight coordination between the FinOps team and Allied Personas—such as ITFM, ITAM, and operations and management functions—each with deep domain expertise. Success depends on orchestrating these roles to drive financial accountability, optimize resource usage, and align Data Center spending with broader business goals.

IT Financial Management (ITFM)

The ITFM Allied Persona serves as a critical bridge between traditional financial management practices and FinOps approaches in Data Centers.

Key ITFM Activities for Data Center FinOps:

• Develop Total Cost of Ownership (TCO) models that capture both capital and operational expenses
• Maintain amortization schedules for physical assets in alignment with accounting policies
• Establish chargeback/showback frameworks suited to fixed, non-elastic infrastructure environments
• Reconcile financial data across systems like DCIM, asset management, and the general ledger
• Support capital planning efforts for infrastructure upgrades, expansions, or lifecycle replacements
• Evaluate business cases for major Data Center initiatives, migrations, or transformations
• Advise on tax treatment of CapEx vs. OpEx to optimize financial outcomes and compliance

ITFM teams should collaborate with FinOps practitioners to create consistent financial reporting that applies FinOps principles to traditional capital-intensive environments while adhering to enterprise accounting standards.

IT Asset Management (ITAM)

ITAM professionals manage the complete lifecycle of hardware assets, providing critical data that enables accurate cost allocation and optimization.

Key ITAM Activities for Data Center FinOps:

• Maintain a comprehensive asset inventory with purchase price, acquisition date, and ownership details
• Track asset location, configuration, and utilization across facilities and workloads
• Manage maintenance contracts and renewal timelines to avoid service gaps and cost overruns
• Document hardware specifications to support software license compliance and optimization
• Align asset refresh planning with vendor end-of-life schedules and depreciation cycles
• Identify and oversee asset redeployment opportunities to maximize value across business units
• Manage asset disposal processes, ensuring data sanitization, recycling compliance, and audit readiness
• Calculate and report on remaining useful life and depreciation to support capital planning and financial reporting

ITAM teams should integrate their asset lifecycle data with FinOps systems to ensure accurate cost allocation based on physical asset utilization and to identify opportunities for hardware reuse or consolidation.

IT Service Management (ITSM)

ITSM processes govern the delivery and support of IT services, directly impacting operational efficiency and cost optimization.

Key ITSM Activities for Data Center FinOps:

• Embed financial approval workflows into change management to align operational changes with budget impact
• Incorporate cost metrics into SLAs and OLAs to tie service performance to financial accountability
• Define service catalog entries with embedded cost components to support informed consumption decisions
• Enhance configuration management databases (CMDBs) with financial attributes for full cost traceability
• Leverage incident and problem management data to identify and address cost-impacting operational issues
• Support capacity planning through integrated financial modeling of infrastructure usage and growth
• Implement cost-aware request fulfillment processes to drive value-based decision-making at the point of demand
• Drive continuous service improvement (CSI) with cost metrics to guide prioritization and investment

ITSM teams should collaborate with FinOps practitioners to ensure service management processes incorporate financial considerations and that service improvements are assessed based on both operational and financial impact.

Sustainability

Sustainability professionals focus on environmental impact reduction, which increasingly affects Data Center economics through regulations, incentives, and corporate commitments.

Key Sustainability Activities for Data Center FinOps:

• Calculate carbon emissions associated with Data Center operations
• Track and report on power usage effectiveness (PUE) metrics
• Develop water usage efficiency (WUE) monitoring and reporting
• Identify energy efficiency improvement opportunities with ROI calculations
• Manage renewable energy procurement strategies and cost implications
• Evaluate sustainability impact of infrastructure refresh decisions
• Monitor regulatory requirements and potential carbon pricing impacts
• Support e-waste management programs and circular economy initiatives

Sustainability teams should collaborate with FinOps practitioners to integrate environmental metrics into financial modeling, enabling balanced decision-making that considers both financial and environmental impacts.

Other Allied Personas to include for Data Center Scope

Facilities Management

Facilities professionals play a critical role in managing the physical infrastructure that houses Data Center equipment—directly influencing both operational costs and capital planning.

Facilities teams should be integrated with the FinOps practice, providing crucial operational data that affects both capital and operational expenses.

Hardware Engineering

Hardware engineering teams influence key drivers of capital and operational costs through decisions on equipment specifications, standardization, and lifecycle management.

To optimize spend and performance, these teams should work closely with FinOps practitioners to ground technology choices in Total Cost of Ownership (TCO) analysis, ensuring decisions balance technical requirements with long-term financial impact.

Capacity Management

Capacity management specialists play a critical role in optimizing infrastructure utilization and forecasting future demand—directly influencing capital efficiency and operational performance.

As such, capacity management should be recognized as a key Allied Persona within Data Center FinOps, contributing specialized expertise to help balance utilization, performance, and cost—ensuring infrastructure investments are both right-sized and future-ready.

Real Estate Management

For organizations with owned Data Center facilities, real estate management introduces financial considerations—such as property costs, lease structures, and asset depreciation—that are not present in cloud environments.

To fully account for these factors, real estate management should be recognized as an Allied Persona within Data Center FinOps practices, contributing expertise in facility ownership, space planning, and long-term property cost management.

Example Role: FinOps Practitioner

Example Roles: Systems Engineer, DevOps Engineer

Example Role: Finance Analyst

Example Role: Product Owner

Example Role: Vendor Manager

(e.g. Data Center inventory – insuring disposal of legacy equipment, etc.)