Why Analytics Value Needs a New Language

If you lead analytics in a large enterprise, you’ve likely faced a familiar—and frustrating—question: “What’s the ROI?”

It’s a reasonable ask. But too often, the conversation ends there, where it is reduced to a spreadsheet that fails to capture how analytics actually delivers value inside a complex organization.

Over the years, IIA has helped hundreds of analytics leaders reframe that question. Through client work and community discussions, we’ve seen firsthand how traditional financial models fall short in capturing the full return of analytics and AI investments.

That’s especially true for leaders trying to mature their organizations beyond project-based delivery.

In recent months, we’ve published two in-depth resource hubs to support that evolution:

• Our resource hub on demand-driven data strategies tackles the upstream alignment and leadership challenges behind effective data investments.
• And the hub on federated analytics addresses how enterprise teams organize, prioritize, and scale analytics in decentralized environments.

Now, we turn our focus to the third leg of the stool: how to measure and communicate analytics value in a way that reflects the reality D&A leaders face.

This blog introduces a new framing for that task: Returned Business Value (RBV). Unlike traditional ROI models, RBV accounts for how analytics value unfolds—across business functions, over time, and under uncertainty. It’s a model grounded in practical experience, built for organizations navigating transformation, and designed to support the conversations that matter most: not just what was delivered, but what was realized.

ROI vs. RBV: Why the Math Doesn’t Work Anymore

Most analytics leaders aren’t new to ROI discussions. But too often, the ROI models used inside their organizations were built for a different kind of investment, such as new machinery, call center outsourcing, or ERP platforms.

These are systems with clear financial inputs and predictable operational outputs.

Analytics doesn’t work that way.

When it comes to AI models, predictive insights, or enterprise data platforms, the return is rarely immediate, direct, or attributable to a single cost center. Even when the impact is real—reduced attrition, smarter routing, faster pricing—the path to value is murky.

That disconnect shows up in three ways:

1. Analytics is probabilistic, not deterministic.
   Traditional ROI assumes a clean, causal line from input to outcome. But analytics merely enables decisions. It doesn’t guarantee that decision-makers will change their behavior, or that the organization is equipped to act.
2. Delivery gets confused with success.
   Too many teams are measured by outputs: dashboards launched, models deployed, data pipelines built. But none of that matters if it doesn’t lead to action. Traditional ROI ends at delivery. RBV starts where delivery leaves off.
3. Organizational friction is invisible.
   Even the best model will fail if there’s no sponsor, no shared KPI definition, or no one accountable for acting on the insight. That friction is what IIA calls “absorption risk,” and it’s the main reason value fails to take hold.

We’ve seen this pattern across years of client engagements. And we’ve built tools to help shift the conversation. Starting with a new equation.

What Makes Up Returned Business Value?

Every enterprise claims to value data. But when it comes time to measure the value of analytics, most organizations fall back on legacy thinking. They expect a clean number. A crisp return. A short path from insight to income. The reality is far messier.

That’s why RBV doesn’t offer a single ROI-style metric. It offers a structured way to think about value in context—anchored to strategy, adjusted for risk, and built to reflect how analytics really works in complex organizations.

RBV is built from three interlocking components:

1. Strategic Value – how well an initiative aligns with enterprise priorities.
2. Commercial Value – the balance of benefit, cost, and ownership.
3. Risk – the friction that prevents value from being realized.

Each component adds dimension to the value story. Together, they allow analytics leaders to reframe conversations with finance, elevate the maturity of their business partners, and surface the bets worth making.

Strategic Value: Focus Over Flash

Think of strategic value as a filter, as opposed to a clean calculation.

It asks whether an analytics initiative supports what the business says matters most, what that comes in the form of customer retention, regulatory compliance, competitive differentiation, or operational resilience. And in environments where time, talent, and budget are finite, this filter becomes the first point of triage.

In IIA’s experience, organizations that consistently score strategic alignment are better at prioritization, faster at decision-making, and more likely to secure executive sponsorship. Why? Because they start by tying analytics initiatives to business relevance.

You don’t need to build a perfect model of strategic value. You need a consistent one. An agreed-upon scoring system that lets analytics leaders and business partners stack-rank projects and make tough tradeoffs when everything feels important.

Scoring strategic fit also helps guide demand generation. If your current queue lacks high-scoring initiatives tied to transformation or compliance, that’s a signal to go looking—often in the functions closest to the action: product, sales, customer experience.

Commercial Value: Benefits, Costs, and the Reality of Follow-Through

This is where most ROI discussions begin and end: what’s the return on our investment? RBV keeps the core of that logic, but broadens the lens.

It starts with the basics:

• Benefit – Includes cost savings, revenue gains, KPI lift, or productivity improvements. But it also accounts for technical benefit: reusable code, data asset cleanup, or the scaffolding that accelerates future projects.
• Cost – Goes beyond acquisition and implementation. It includes integration, support, training, enhancement, and even retirement or decommissioning at end-of-life.

In other words, RBV doesn’t stop at first-order costs. It accounts for the full lifecycle of analytics delivery, including the hidden burden placed on IT or the disruption that comes with change.

And crucially, RBV demands a clear service model: Who owns the outcome?

It’s not enough to build a model that performs. The business must agree to track impact, reinforce behavior, and claim results in terms that matter to leadership. Without that co-ownership, value fades.

The result is a more durable measure of commercial value—one that reflects what’s built, what’s used, and what’s reinforced.

Risk: The Multiplier That Changes Everything

RBV makes one thing clear: risk is not a footnote. It’s a multiplier.

The more friction, uncertainty, or immaturity in the environment, the lower the likelihood that even great analytics work will deliver return. That’s why RBV adjusts commercial value by a risk factor between 0 and 1:

RBV = Strategic Value + (Benefit – Cost) × (1 – Risk)

With this equation in mind, IIA recommends building a practical risk register that covers:

• Human capital – Are the skills in place to deploy, explain, and scale the solution?
• Technology – Will it integrate into the current environment? Is the vendor stable?
• Organizational – Are roles clear? Sponsors engaged? Metrics agreed upon?

The benefit of this approach is that each risk is scored and mitigated before value is promised.

In advanced analytics, where project failure often stems from business-side inaction, this step is critical. It protects both delivery and credibility.

RBV also highlights a subtle but widespread source of risk: absorption failure. You can build the dashboard. But if no one changes behavior, the return doesn’t exist.

Accounting for that reality shifts the conversation. It places more weight on business partner readiness and builds the case for embedding change management into analytics delivery.

When Value Is a Bet and Not a Guarantee

Ultimately, RBV reflects the nature of analytics work: it’s often a bet. A calculated decision to invest time and capital in an initiative with uncertain—but potentially transformative—returns.

Unlike traditional ROI, which assumes fixed costs and predictable gains, RBV embraces nuance:

• Some value is technical.
• Some value is future-facing.
• Some value depends on business readiness.

And that’s what makes the framework so valuable.

It doesn’t hide the complexity of analytics behind a single number. It surfaces it, structures it, and makes it actionable.

What’s Next: From Concept to Capability

If this first look at RBV has struck a chord, you’re not alone. Across dozens of engagements, we’ve seen analytics leaders wrestle with the same challenge: how to move from showcasing technical delivery to communicating sustained business impact.

The Returned Business Value framework offers a path forward—one that doesn’t ask analytics teams to contort themselves into outdated ROI math, but instead invites a more honest, strategic, and credible view of how value is created and sustained across the enterprise.

This blog has just scratched the surface. In the coming weeks, we’ll go deeper into the practical application of RBV across several high-impact areas:

• Portfolio Management for Analytics: How to shift from delivering outputs to managing a portfolio of bets—and how RBV supports more strategic prioritization and tradeoff decisions.
• Operationalizing the Value Realization Cycle: A step-by-step look at how leading teams embed measurement and accountability into each stage of the analytics lifecycle—from planning through sustained impact.
• Building a Co-Owned Service and Accountability Model: What it really takes to condition business units to own the outcomes analytics enables, and how to formalize shared responsibility without stalling delivery.
• Diagnosing and Reducing Value Friction: How to identify the organizational blockers—like unclear ownership, weak measurement culture, or data literacy gaps—that drag down ROI, and what to do about them.
• Evolving Your Metrics to Match Maturity: A practical guide to shifting your value metrics from usage stats and adoption rates to alignment, readiness, and business-owned results.

Each topic will draw directly from field-tested practices, real client engagements, and the voices of experienced analytics leaders who’ve put these ideas to work.

And if you're ready to take the next step now, join us for a live conversation that builds on this framework. Register now for our upcoming webinar: Beyond the Math: Rethinking ROI in Enterprise Analytics.

In this session Amit Mohindra, IIA Expert and CEO of People Analytics Success, shares how he reframes analytics ROI to reflect not just measurable outcomes, but the relationships that sustain them.

Because in the end, value isn’t what you model. It’s what you enable—and what the business owns.

Stay tuned.