# 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 |

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Source: https://visualoneintelligence.com/docs/fof-domain-2-quantify-business-value/