Infonomics Progressions

There are 720 combinations of the six Infonomics metrics, some of which we want to avoid and others which we want to build a business around and more than enough to join up everyone when talking about the value of data.

This post looks at how the priority order of the metrics change with professional orientation, illustrating that different interpretations can be had of the same data in simultaneous, varied and non-rivalrous ways.

Some sequences are much more natural (and role-aligned) than others, but no single progression is universally correct. Below I’ll:

1. show the main logical progressions,
2. map them to roles/functions, and
3. call out which sequences are generally preferable.

Natural analytical progressions

These progressions respect a conceptual dependency: that we understand quality and business relevance before we can credibly claim performance, economics or marketability.

A. Inside‑out causal progression (data → decisions → performance → money)

This is the cleanest causal chain from quality to cash flow and is analytically strong across most data value conversations.

1. IVI → BVI → PVI → EVI
   • IVI: Is the data intrinsically any good?
   • BVI: Is it embedded in important business processes?
   • PVI: Does it measurably move KPIs?
   • EVI: What is the bottom‑line contribution?

B. Cost‑aware progression (adding cost and market layers)

Building on the inside-out causal progression, we address economics, cost and marketing:

2. IVI → BVI → PVI → EVI → CVI
   • Adds: Is the value justified by the cost to acquire, process, manage and replace?
   • Supports ROI, capital allocation and keep vs kill decisions.
3. IVI → BVI → PVI → EVI → CVI → MVI
   • Adds: Could we also monetise externally, and what would the market pay (MVI)?
   • This is a full Infonomics lifecycle sequence: from quality to internal value, to cost, to external market value.

C. Market‑first progression (starting from external opportunity)

Two marketing focused progressions looks at what can be marketed from what we have on hand.

4. IVI → MVI → EVI/CVI
   • Start from: Is this data good and scarce enough that a market would pay for it?
   • Then: What internal and external economics does that support (EVI) given the costs (CVI)?
5. IVI → BVI → MVI
   • Data already has internal business relevance (BVI).
   • Question: Is there a derivative/aggregated form that is marketable (MVI)?

D. Cost‑risk progression (risk and resilience lens)

Two finance and risk-oriented progressions, looking at replacement value, overall contribution and total cost of mitigations.

6. IVI → CVI → EVI
   • Focused on: What would it cost to lose/replace this data (CVI), and how does that compare to its economic contribution (EVI)?
   • Natural for risk, audit, business continuity.
7. IVI → CVI → EVI → MVI
   • Adds: If we treat data as an asset, is it worth investing more to protect it (CVI) because of its internal (EVI) and external (MVI) value?

Role and function alignment

These progressions are associated with particular professions, organisational role or function. These roles might sit closest to data quality, architecture, experimentation, and pipeline economics.

Chief Data Officer / Data Governance / Analytics leadership

• IVI → BVI → PVI → EVI
  • CDO/CAO own quality, relevance, and experimental proof that data drives KPIs, then collaborate with Finance on EVI.
• IVI → BVI → PVI → CVI
  • When arguing for or against data platform spend: Here is what this data does, and here is what it costs.

Chief Finance Officer / Finance

• EVI → CVI → EVI/CVI ratio (and then MVI)
  • Finance often starts from P&L and cash flows:
    • What is the economic contribution (EVI)?
    • What are the capitalised and expensed costs (CVI)?
    • Is there external monetisation (MVI)?
  • Under the hood, they will rely on CDO/analytics teams having run IVI/BVI/PVI first.

Differences Between the CDO and CFO Frame

So, we get a split workflow where one (the CDO) precedes the other, which is why thinking about a data asset eventually necessitates a finance term.

• CDO side: IVI → BVI → PVI
• CFO side: EVI → CVI → capital allocation, with MVI when exploring data products.

Product / Growth / Marketing / Commercial

These roles care about use‑case fit and uplift, with economics as the final step. and a natural sequence is:

• BVI → PVI → EVI
  • Start from: Which data matters to this product/channel (BVI)?
  • Test impact on KPIs (PVI), then translate into revenue/margin (EVI).
  • For data products: BVI (for customers) → MVI → EVI (for us).

Risk / Compliance / Audit

Two progressions aimed at the cost of compliance, risk and loss; and looking at the cost-effectiveness of the data infrastructure supporting business activities.

• IVI → CVI → EVI (risk‐weighted)
  • Focused on data loss, regulatory exposure, and the value at risk if data is wrong or lost.
• Occasionally IVI → BVI → CVI
  • “This data underpins our risk models (BVI); what does it cost to manage at the required quality (IVI, CVI)?”

Strategy / Corporate Development / Investors

The portfolio level question is the same as the Backbone progression (less a specific cost analysis); and the strongly market led orientation of the second progression is focused on specific market conditions.

• IVI → BVI → PVI → EVI → MVI
  • For portfolio‑level questions (what is the strategic value of our data assets relative to peers?) and data‑heavy M&A.
• Or market‑first: IVI → MVI → EVI
  • For data businesses where external monetisation dominates.

Are some progressions preferable?

There is a methodologically preferable progression to use as the analytical backbone. For evaluating a specific data asset in a disciplined way, IVI → BVI → PVI → EVI → CVI → MVI is the most complete and conceptually coherent sequence.

Why this backbone is strong:

• IVI before everything: If the data is bad, nothing downstream is robust.
• BVI before PVI: Prove relevance and fit before we claim performance lift.
• PVI before EVI: Use empirical lift to ground economic value (EVI) rather than story‑telling.
• EVI before MVI: External market value is usually anchored in demonstrated internal/business value and use cases.
• CVI alongside EVI: EVI alone is incomplete; EVI–CVI or EVI/CVI drives any rational investment decision.

However, in practice we'd:

• Use shorter, role‑specific sub-sequences operationally (e.g. BVI→PVI→EVI for product teams, EVI→CVI for Finance).
• Reserve the full progression for high‑stakes assets (core customer data, key domain datasets) and portfolio reviews.

Backbone overview

Backbone progression: IVI → BVI → PVI → EVI → CVI → MVI. 

This can be presented as a horizontal swim lane with rows for functions and columns for metrics.

1. IVI – Intrinsic Value of Information

Primary owners:

• Chief Data Officer (CDO)
• Data Governance / Stewardship
• Data Engineering

Key activities:

• Profile and score data quality (validity, completeness, integrity).
• Document lineage, controls, and uniqueness.

Outputs passed on:

• IVI scorecard per asset.
• Fit for purpose statement (where this data is safe/unsafe to use).

Main recipients:

• Analytics / Data Science (for modelling).
• Business / Product owners (to understand constraints).

2. BVI – Business Value of Information

Primary owners:

• Business Domain Owners (product, marketing, operations)
• CDO / BI team (facilitation)

Key activities:

• Map data to decisions, processes, and KPIs.
• Identify use‑cases and embed data in workflows and dashboards.

Outputs passed on:

• BVI scorecard (where/how this data matters today).
• Use‑case catalogue linking data to processes and KPIs.

Main recipients:

• Analytics / Data Science (to prioritise experiments).
• Strategy / Portfolio teams (to see strategic importance).

3. PVI – Performance Value of Information

Primary owners:

• Analytics / Data Science
• Experimentation / Optimisation teams
• With Business Owners as sponsors

Key activities:

• Run A/B tests, back‑tests, and pilots using the data.
• Quantify KPI lift attributable to the data.

Outputs passed on:

• PVI scorecard (evidence quality, lift magnitude, robustness).
• Experiment dossiers with KPI effects and confidence levels.

Main recipients:

• Finance / FP&A (to translate into money).
• Product / Operations (to scale successful uses).

4. EVI – Economic Value of Information

Primary owners:

• CFO / Finance / FP&A
• With input from Analytics (PVI) and Business Owners

Key activities:

• Turn KPI lifts into revenue, cost, and risk impacts.
• Attribute value to data assets or bundles.

Outputs passed on:

• EVI scorecard (per asset/use‑case).
• Financial model showing contribution to P&L / capital.

Main recipients:

• Executive team (investment decisions).
• CDO / Product (to prioritise roadmaps).

5. CVI – Cost Value of Information

Primary owners:

• Finance / Controlling
• CDO / Data Platform owners
• Risk / Compliance (for regulatory and protection costs)

Key activities:

• Quantify acquisition, processing, storage, quality, governance, and replacement costs.
• Align with accounting treatment (expense vs capitalise).

Outputs passed on:

• CVI scorecard (1–5 plus monetary figures).
• EVI–CVI ratios and payback metrics.

Main recipients:

• Executive team (keep/kill/scale decisions).
• Risk / Audit (to align protection level with value at risk).

6. MVI – Market Value of Information

Primary owners:

• Chief Data / Digital Product Officer (for data products)
• Strategy / Corporate Development
• Finance (pricing, revenue recognition)
• Legal / Compliance (rights, constraints)

Key activities:

• Assess external demand, competitors, and comparable data prices.
• Design data products and licensing/usage models.

Outputs passed on:

• MVI scorecard (current and potential).
• Data product/monetisation roadmap.

Main recipients:

• Sales / Partnerships (go‑to‑market).
• Executive / Board (strategic positioning of data business).

Role-Based Analytical Progressions

Different functions within an organization interact with data at different stages of its lifecycle, leading to some distinct analytical progressions:

The Chief Data Officer (CDO) / Data Engineering Progression: The Value Creation Path

• Progression: CVI → IVI → BVI → PVI
• Function: This path focuses on foundational data health and operational utility. The CDO first accounts for the budget required to capture and manage the data (Cost Value of Information, CVI). They then engineer the data to ensure high accuracy and completeness (Intrinsic Value of Information, IVI). Next, they align this pure data with specific operational requirements (Business Value of Information, BVI). Finally, they conduct A/B testing to prove the data actually improves key performance indicators (Performance Value of Information, PVI).

The Chief Financial Officer (CFO) Progression: The Capitalisation Path

• Progression: CVI → PVI → EVI
• Function: The CFO focuses on capital allocation, balance sheets, and ROI. They start by establishing the floor replacement cost of the data (CVI) for insurance and SNA 2025 reporting. Under the new FASB ASU 2025-06 framework, the CFO uses the empirical lift (PVI) as the objective evidence required to meet the probable-to-complete threshold, allowing them to shift the data from an operating expense to a capitalized asset (PP&E). Finally, they measure the net return on information to the bottom line (Economic Value of Information, EVI) to justify future capital allocation.

The Strategy / Data Product Manager Progression: The Monetisation Path

• Progression: BVI → PVI → EVI → MVI
• Function: This pathway is driven by the desire to externalize and monetize data. It begins by identifying a highly relevant business problem (BVI) and proving the data solves it internally (PVI). Once the data generates significant internal ROI (EVI), it achieves a Fair Value Unlock, where internal success validates the data's worth to external buyers, allowing the organization to price and license it in the open market (Market Value of Information, MVI).

The Risk & Compliance Progression: The Defence Path

• Progression: IVI → CVI → EVI
• Function: Compliance and audit teams look for systemic liabilities. They assess whether the cost to maintain and secure a dataset (CVI) eclipses its actual economic utility (EVI). If costs (CVI) are high, quality (IVI) is low, and business utility (BVI) is absent, the system is in a Tech Debt Spiral, and the progression triggers a sunset protocol to decommission the asset.