Domain 2: Quantify Business Value
| Application | Visual One Intelligence (VisualOne / VSI) |
| Platform version referenced | v6.0.0.1 |
| FinOps Framework domain | Quantify Business Value |
| Capabilities in this section | Planning & Estimating · Forecasting · Budgeting · KPIs & Benchmarking · Unit Economics |
| Source basis | VisualOne reference documentation (FinOps for VisualOne Reference Guide; VSI Screen Reference Guide; VSI Virtualization Reporting User Guide; Visual One Storage Reference) |
2.1 Planning & Estimating
Capability: Estimate and explore potential cost and value of workloads in an organization’s environment for particular scenarios and models.
How VisualOne enables this
VisualOne provides three distinct scenario-modeling surfaces, each aimed at a different planning question: what would a new workload cost, what happens to my cluster if I add hardware, and when do I run out of runway under an assumed growth pattern.
Supporting features
| Planning surface | Implementation |
|---|---|
| Tag Cost Modeling (/reports/finops/tag-cost-modeling) | A dedicated what-if tool and the one editable/CRUD screen in the FinOps menu. A user defines a hypothetical workload against a tag key/value in a Storage, Compute, or Cloud context and receives projected Daily / Monthly / Yearly cost. Storage modal fields: Tag Key (existing), Tag Value (new), Array, Capacity (GiB), $/GiB/Day rate, with a live Estimated Cost Preview computing Daily → Monthly (×30) → Yearly (×365). Compute models carry vCPUs and Memory (GiB) against a cluster; Cloud models carry vCPUs, Memory (GiB), and Storage (GiB). Modeled workloads persist in a grid and can be added or deleted |
| Cluster Modeling (/reports/virtual/cluster/cluster-modeling) | Scenario modeling for future capacity and cost. Project setup captures month to add resource, project name, and notes; inputs accept custom resources and custom host model definitions. Results are presented across tabs for vCPU, memory, disk, VM build, and cost, with a forecast chart plotting used vs. capacity vs. allocated. Documented uses: model the next planned hardware purchase and compare runway change; use the cost tab to translate resource adds into budget impact; support budget justification with forecast outputs |
| Pool / Device Capacity Plan (Date Adjustments dialog) | Custom growth-scenario entry with fields Date, Capacity (GiB), Workload (GiB), Growth (GiB), Notes (observed entries: “05/20/26, Growth 10,000 GiB, custom growth” and “04/20/26, Workload 50,000 GiB, new project”). Multiple adjustment rows can be stacked and saved; results show impact on projected effective capacity, updated utilization trend, future growth slopes, and potential capacity-exhaustion dates. Device Capacity Plan applies the same adjustment framework in a Performance mode using IOPS and Bandwidth instead of GiB, so capacity and performance constraints can be planned in parallel |
| Procurement estimation (TCO comparison) | Side-by-side asset evaluation: Asset A ($100k purchase / $30k annual operating / 100 TiB → $2,500 per TiB 5-year TCO) vs. Asset B ($150k purchase / $20k annual operating / 150 TiB → $1,667 per TiB), demonstrating that normalized TCO supports pre-purchase option comparison rather than only post-hoc reporting |
| Investment case math | Payback Period = Investment Cost ÷ Annual Savings; ROI = (Annual Savings − Annual Support Cost) ÷ Investment Cost × 100%, documented with worked examples for justifying optimization and refresh investments |
| Migration estimation | The platform supports migrations and cross-platform cost comparisons, with Cluster Plans Summary / Cluster Migration Modeling available in the Virtual module where enabled |
| Assumption documentation | Cluster Modeling projects carry name and notes fields, with the documented practice of recording assumptions so a model can be reproduced and reviewed by others |
Personas served
FinOps Practitioner and Engineering (workload and cluster modeling), Finance (budget impact of planned adds, payback/ROI), Leadership (procurement option comparison), Product (cost of a proposed new workload before it is built).
2.2 Forecasting
Capability: Provide or create a model of anticipated future cost and value of systems and services, leveraging statistical methods, historical spend patterns, planned changes, and related metrics.
How VisualOne enables this
Forecasting is a cross-cutting behavior in VisualOne rather than a single report. Several trend charts plot data points beyond the current Collection Date, rendered as a lighter or dashed continuation of the line, model-projected future values sitting alongside historical actuals in the same chart and, in some cases, the same grid.
Supporting features
| Forecast surface | Implementation |
|---|---|
| Tag Forecasting / Tag Capacity Planning | Dedicated cost-forecast report scoped by Tag Key + Tag Value. Four projected series, Compute Effective Cost, Storage Effective Cost, Cloud Actual Cost, Total Effective Cost, plotted on a horizon that extends approximately one month past the current Collection Date (observed: actuals to 07/21/2026, chart to 20 Aug 2026), with forecast segments rendered dashed/lighter to distinguish them from actuals. The supporting grid (45 records) holds historical and projected daily values side by side, columns: Collection Date, Total Effective Cost, Compute Effective Cost, Storage Effective Cost, Cloud Actual Cost. Forecast values are generated by projection/trend continuation |
| Cost YTD forecasting | Month-over-month cost trend by context (Storage / Virtual / Cloud) with forecast continuation past the collection date, supporting forecasting of annual costs based on current run rate |
| Savings forecasting | Optimization Recommendations plots an Estimated Savings (Monthly) series across Nov 2025 – Jul 2026 with monthly data points, projecting the trajectory of the opportunity pool ($28,216 Feb peak → $1,602 Jun → $5,138 Jul) |
| Depreciation forecasting | Amortization Report projects Asset Depreciation Value (Monthly) forward on a ten-year horizon (Mar 2017 – Feb 2027), with per-device straight-line decline to $0, making future asset-value and refresh timing explicit |
| Capacity runway forecasting | Weeks Left to Capacity (estimated runway until a constraint is reached), Build Capacity (how many additional VMs fit given CPU/memory/disk headroom, may be negative), and Capacity Constraint / Limiting Factor (the resource that limits growth first, often memory). Cluster Trends charts the VM build capacity trajectory and identifies the inflection point where build capacity crossed zero |
| Storage capacity forecasting | Pool Capacity Plan combination chart overlays historical series with a projected forecast line and adjustment-impact visualization, with start-date and forecast-duration controls, identifying capacity-exhaustion dates |
| Documented forecast methods | Four methods with worked examples: Simple run-rate (YTD Cost × 365 ÷ Days Elapsed); Monthly average (YTD ÷ Months Elapsed × 12); Growth-based compounding (Growth Rate = (Current − Previous) ÷ Previous; Projected = Current × (1 + Growth Rate)^Remaining Periods); Seasonal adjustment (Adjusted Forecast = Run Rate × Seasonal Factor, where Seasonal Factor = same-period historical average ÷ annual average) |
| Method selection guidance | The reference annotates the limits of naive extrapolation, for example flagging that a short-elapsed-period run-rate projection produces a distorted annual figure, and recommends seasonal adjustment where historical data supports it |
| Historical depth | Forecast quality is underpinned by retained collection-date snapshots; observed trend horizons span multiple years, with zoom presets including 1y and All |
Personas served
FinOps Practitioner (run-rate and growth forecasting by tag), Finance (annual projection vs. budget, depreciation schedule), Engineering (capacity runway and constraint timing), Leadership (year-end cost projection).
2.3 Budgeting
Capability: Setting limits, monitoring, and managing technology spending aligned with business objectives, to ensure accountability and predictable financial outcomes.
How VisualOne enables this
The Cost Per Sector Report is the budgeting instrument: it holds a budget figure and an actual cost figure per infrastructure sector and renders the comparison three ways, with variance computed in both absolute and percentage terms.
Supporting features
| Budgeting element | Implementation |
|---|---|
| Budget-to-actual reporting | Cost Per Sector Report carries per-sector Budget Amount and Cost Amount across Storage, Virtual, and Cloud, with a selectable cost metric |
| Three-panel comparison | Budget per Sector donut (budget allocation share), Cost per Sector donut (actual spend share, observed distribution Storage 54.8%, Compute/Virtual 38.2%, Cloud remainder and growing), and a Budget vs. Cost horizontal grouped bar (orange = budget, green = actual) where green > orange reads as over budget at a glance |
| Variance grid | Columns: Sector, Budgeted amount, Actual cost, Variance amount, Variance percentage, Capacity, Utilization percentage |
| Variance math | Variance = Actual − Budget (positive = over budget); Variance % = (Actual − Budget) ÷ Budget × 100%. Worked example: $125,000 actual vs. $100,000 budget = $25,000 / 25% over |
| Variance trending | Tracking variance across consecutive months (−5% → 0% → +5% → +10%) identifies a deteriorating trend before year-end, rather than treating each month in isolation |
| Budget management workflow | Monthly cycle: establish budget targets by sector/department → run Cost Per Sector monthly → calculate variance → investigate significant variances (>10%) → adjust budget or operations → reforecast for the remainder of the year. Stated outcome: budget compliance and proactive cost management |
| YTD budget tracking | Cost YTD Report supports year-to-date budget tracking and run-rate comparison against annual budget, feeding the “on track vs. variance” determination and year-end projection |
| Budget accountability by owner | Chargeback Report and Tag Summary allocate the same cost base to departments and cost centers, so sector budgets can be devolved to owning organizations; the governance workflow includes reviewing variance with cost center managers and escalating out-of-policy spending |
| Forward budget planning | Cluster Modeling’s cost tab translates planned resource additions into budget impact and supports budget justification; Financial Administration Report supports capital budget forecasting and planning and operational budget development; Amortization Report gives forward depreciation load by year |
| Budget inputs | Operating Cost settings (power, floor space, personnel, purchase price, maintenance per device/host) provide the controllable cost assumptions budgets are built from, editable inline or by CSV export → edit → re-upload |
| Annual planning cadence | The implementation roadmap includes annual budget planning and quarterly optimization reviews as an ongoing practice |
Personas served
Finance (budget setting, variance analysis, reforecast), FinOps Practitioner (monthly variance cycle), Leadership (sector-level financial status), Engineering (understanding the budget consequence of capacity adds).
2.4 KPIs & Benchmarking
Capability: Evaluate resource optimization and value between parts of the organization or against industry peers, to inform decision-making and align FinOps with business objectives.
How VisualOne enables this
VisualOne ships a standardized 0-100 scoring model applied uniformly across heterogeneous assets, which is what makes benchmarking possible across otherwise non-comparable storage arrays and compute hosts.
Supporting features
| KPI / benchmark element | Implementation |
|---|---|
| Cost Efficiency Dashboard | Scores every asset (storage arrays and VMware hosts alike) on cost-to-performance/cost-to-capacity, on a 0-100 scale with a four-tier classification: Optimal (>75), Efficient (>50-75), Underutilized (>25-50), Wasteful (≤25) |
| Portfolio-level KPI | An Overall Efficiency gauge rolls all scored assets into a single number with a status pill (observed: 14/100 across 14 scored assets) |
| Peer ranking | Top Performers and Low Value panels rank the top and bottom five assets by score; the Matrix view groups every asset into its tier band with live counts and per-asset score + monthly cost (observed: 2 assets Underutilized at 25.09/100 · $1,345/mo; 12 assets Wasteful at 0.09-22.89/100) |
| Cross-class comparison | Because the same scoring scale spans storage and compute, class-level comparison is possible, observed storage arrays scoring 0.09-1.07 against VMware hosts at 13.45-25.09 |
| Capacity KPIs | Capacity Score (0-100 indicator of balance and utilization health), Weeks Left to Capacity, Build Capacity, Capacity Constraint / Limiting Factor, Utilization % (Used ÷ Capacity) |
| Financial efficiency KPIs | $ Per TiB Total (annual), $ Per TiB Daily, Total Cost per GiB per Day, Price per GiB, Cost of Asset per Day, all normalized so differently sized assets compare fairly; higher values indicate less efficient assets, and larger capacity generally yields lower per-unit cost |
| Spend KPIs | Tag Cost Inventory KPI tiles: Total Infrastructure Spend (current month), Tags Analyzed count, Average Cost Per Tag, and Top Spender with its monthly figure |
| Organizational benchmarking | Chargeback and Tag Summary allow department-to-department and BU-to-BU cost comparison on a common basis; Cost Per Sector allows sector-to-sector variance comparison |
| Optimization KPIs | Savings Realization % = Actual Savings ÷ Projected Savings × 100% (worked example: $40,000 realized against $50,000 projected = 80%); ROI %; Payback Period |
| Performance KPIs | IOPS, Latency, throughput, SPM growth, with dual-axis trending for SLA verification |
| Longitudinal comparison | Scoring re-runs on each collection and Collection Date snapshots are retained, so KPI movement is comparable over time; the Delta Report quantifies change between any two periods |
Personas served
FinOps Practitioner (efficiency scoring and realization tracking), Engineering (capacity score, limiting factor, utilization), Finance (per-unit cost efficiency), Leadership (single overall efficiency KPI and ranked outliers).
2.5 Unit Economics
Capability: Develop and track metrics that show how technology use and management practices impact the value of the organization’s products, services, or activities.
How VisualOne enables this
Unit economics is a structural feature of the platform’s cost model: essentially every cost in VisualOne is stored and reported in normalized per-unit form, not only as an absolute. Capacity is the denominator for normalization, and normalized cost enables fair cost comparison across device sizes.
Supporting features
| Unit metric | Definition / formula |
|---|---|
| $ Per TiB Total | Annual Operating Cost ÷ Total Capacity (TiB), observed representative range $3,000-5,000 per TiB annually |
| $ Per TiB Daily | $ Per TiB Total ÷ 365, observed representative range $8-14 per TiB daily |
| Cost per GiB (annual / daily) | Annual Operating Cost ÷ Usable Capacity (GiB); and that figure ÷ 365 |
| Total Cost per GiB per Day | Cost of Asset per Day ÷ Usable Capacity (GiB), typical range $0.01-1.00; the critical efficiency metric, where higher = less efficient |
| Cost of Asset per Day | Daily Operating Cost + Purchase Cost per Day (capital and operating combined into one all-in unit rate) |
| Purchase Cost per Day | Total Acquisition Cost ÷ Life of Asset (days), capital amortized to a daily unit |
| TCO per GiB / per TiB | TCO ÷ Usable Capacity, where TCO = Purchase Price + (Annual Operating Cost × Years in Service) + Disposal Cost |
| Price per GiB | Configured/derived per-device rate exposed both in Operating Cost settings and as Price Per GB in the FOCUS Export |
| Compute unit rates | Cost of Asset per Day × CPU Cost % and × Memory Cost % (typical 50/50) derive per-vCPU and per-GiB-memory daily rates for VM-level chargeback |
| Modeled unit rate | Tag Cost Modeling takes $/GiB/Day as a direct input and projects Daily / Monthly / Yearly cost from it |
| Per-tag unit cost | Average Cost Per Tag (observed $2,480.94/month across 56 tags), a per-organizational-unit economic metric |
| Per-VM economics | All Units – VMs carries utilization categories and a cost distribution / cost tier donut, giving per-VM cost tiering; VM Summary drills into cost for a single VM |
| Utilization-linked economics | Used Pct, Total Used, and Usable fields in the FOCUS Export tie unit cost to actual consumption rather than provisioned capacity alone |
Worked example carried in the reference
A 100 TiB array purchased at $100,000 over a 1,825-day life, with $2.00/day power, $4.00/day floor space, and $6.67/day personnel, resolves to: Daily Operating Cost $12.67 → Purchase Cost/Day $54.79 → Cost of Asset per Day $67.46 → $0.0659 per GiB per day → $246.23 per TiB annually. This demonstrates the full path from raw cost inputs to a defensible unit rate.
Business-unit linkage
Unit cost becomes business-relevant through the tag layer. Observed tag keys include Company, Function, Operational Category (Production / Dev / Backup / SQL Server / Cloud Native Protection), Owner, BU, and Kubernetes, so a per-GiB or per-VM rate can be aggregated to a per-application, per-function, per-owner, or per-business-unit economic metric, then trended daily via Tag Summary and projected via Tag Forecasting.
Personas served
FinOps Practitioner (unit-rate definition and tracking), Finance (defensible per-unit cost for chargeback and TCO), Engineering (efficiency of a given asset or cluster), Product (cost of the infrastructure supporting a given application or service, via tags).
Section summary
| Capability | Primary supporting surfaces |
|---|---|
| Planning & Estimating | Tag Cost Modeling (CRUD what-if), Cluster Modeling, Capacity Plan Date Adjustments, TCO / ROI / payback comparison |
| Forecasting | Tag Forecasting with actuals and projections in one grid, forecast-past-today as a platform pattern, four documented forecast methods, capacity runway metrics |
| Budgeting | Cost Per Sector budget vs. actual with variance $ and %, monthly variance workflow with >10% threshold, YTD run-rate tracking, forward budget modeling |
| KPIs & Benchmarking | Cost Efficiency 0-100 scoring with four tiers, Overall Efficiency gauge, Top/Low panels, normalized $/TiB and $/GiB/day, Savings Realization % |
| Unit Economics | Per-unit cost stored natively throughout ($/TiB, $/GiB/day, cost of asset/day, TCO/TiB, per-vCPU and per-GiB-memory splits, average cost per tag), with tag-based aggregation to business dimensions |