ROI Might be a Useless Metric in Communicating Business Value

Summary:

• Costs of data projects are multifaceted and not fully captured by ROI.
• ROI oversimplifies the value of data projects by failing to consider the long-term benefits and unpredictable timing of returns.
• Business value communication requires more than just numbers; it also needs buy-in and alignment.
• To effectively communicate business value, data teams should focus on impact-focused metrics, scenario-based ROI, and time-based value.

In my previous post, I discussed how data teams can communicate the value of their work to business counterparts. It’s crucial for data teams to make their impact clear, but relying solely on return on investment (ROI) to measure that impact could be a major pitfall. While ROI serves as a familiar and convenient shorthand for discussing investments and returns, it fails to capture the nuanced value of data projects—especially when it comes to long-term, intangible benefits, such as a data quality or data engineering project. Here’s why we need to think beyond ROI when talking about the value of data projects.

1. ROI Misses the Nuance of Value

ROI is often an oversimplified way to measure success, especially when applied to data quality or data engineering projects. These types of projects often offer multiple layers of value:

• Short-term, tangible value: This is the most obvious benefit, such as increased efficiency or cost savings.
• Long-term, intangible value: Improved data quality can lay the foundation for innovation, enabling better decision-making, advanced analytics, or even more successful AI initiatives. This value may not be realized for months or even years, making it difficult to quantify immediately.

When focusing on ROI alone, decision-makers risk undervaluing the long-term strategic benefits of data projects, instead prioritizing quick wins that might not drive sustained growth. At the same time, getting some quick wins might help the team get more buy-in and learn from the wins or mistakes. So balancing long-term benefits with short-term quick wins is something data leaders need to consider.

2. Understanding Costs is Also Complicated

A second issue with focusing solely on ROI is that it encourages an oversimplified view of costs. In reality, the costs of a data project are multifaceted:

• Direct costs: hardware, software, and staffing resources
• Indirect costs: training, adoption challenges, and even lost productivity during implementation
• Opportunity costs: impacts on other projects, such as delays or compromises, that result from resources spending time on the current project
• Build vs. Buy: the cost to custom build a solution or purchase an out-of-the-box solution

Good decision-making takes all these factors into account, and a myopic focus on ROI often overlooks these broader cost categories.

3. The Timing of Returns is Unpredictable

Another limitation of ROI is that it fails to account for the time dimension. When will the business see a return - next quarter, next year, or in five years? Some data projects, especially those involving significant infrastructure or data quality overhauls, are long-term commitments. They might require years of investment before the benefits become clear. On the other hand, there could be opportunities for quick wins that show value faster, but are these the right wins for your long-term goals?

Communicating the timeline of benefits helps manage expectations and ensures that teams stay aligned on what the project should deliver at each stage.

4. Business Value is About Buy-in, Not Just Numbers

At its core, communicating business value is about getting everyone aligned and committed. ROI, on its own, doesn’t foster this buy-in. Business stakeholders need to understand not just the cost and projected return, but also the broader strategic value of data projects. It’s not a one-time pitch—it’s an ongoing conversation.

A savvy data leader recognizes that business value communication is dynamic. It’s not about tossing out an ROI figure and moving on. It’s about ensuring that all stakeholders—whether in finance, marketing, operations, or IT—understand what the project aims to achieve and the role they play in its success. This ongoing dialogue also ensures accountability across teams.

Moving Beyond ROI

So, if ROI isn’t enough, what is? A more holistic approach to communicating business value might involve:

• Impact-focused metrics: Focus on how the project will improve decision-making capabilities, speed up operations, or reduce risk.
• Scenario-based ROI: Present a range of possible outcomes rather than a single return figure. This helps capture both best-case and worst-case scenarios.
• Time-based value: Communicate how the project will deliver short-term wins, medium-term milestones, and long-term transformations.

ROI, while useful as a framing tool, is insufficient when it comes to fully communicating the business value of data projects. Data leaders need to understand the broader context of their work—the nuances of value, the complexities of cost, and the unpredictability of returns. More importantly, they need to foster ongoing conversations with business stakeholders to ensure buy-in, accountability, and alignment on long-term goals.

ROI is a start, but it’s just that—a start. To truly communicate the value of data, teams must go deeper, providing a more comprehensive and nuanced view of how data projects contribute to the overall success of the business.

Ready to understand the true value that comes from improving your data? Explore our value calculator to learn more.