Data Analytics ROI: Measuring and Maximizing Your Data for Optimal Impact

As data becomes more central to business strategy, organizations are under growing pressure to prove their analytics efforts are worth the investment. That’s where your return on investment (ROI) in data analytics comes in as a way to measure the tangible and intangible value created by your data products, platforms, and teams.

But determining the ROI in the analytics world isn’t always easy to pin down. It requires more than just a formula—it takes thoughtful evaluation of impact, cost, adoption, and organizational maturity.

What is data analytics ROI?

At its core, data analytics ROI refers to the measurable return an organization gets from its investment in data analytics—including tools, platforms, talent, and time. It’s about determining whether analytics efforts actually generate business value, which can come in many forms: cost savings through operational efficiencies, increased revenue from smarter targeting, faster decision-making, or reduced risk due to better forecasting. A high data analytics ROI means that your analytics initiatives are translating into real-world outcomes that move the business forward.

Data analytics ROI helps shift the mindset from simply “doing analytics” to evaluating how effectively analytics are being used to solve problems and achieve goals. Organizations will need to ask not just what data they have but also what they’re doing with it—and whether that effort is worth the investment. Measuring ROI supports strategic prioritization, helping teams identify what’s working, what’s underperforming, and where to focus resources to maximize impact.

In this guide, we’ll break down what data analytics ROI is, how to calculate it, the challenges that come with it, and strategies you can use to measure and maximize the value of your data.

How to calculate data analytics ROI

To calculate the ROI of your data analytics, you’ll need to compare the business value created by your analytics efforts against the total cost of delivering those insights. While the concept is straightforward (return divided by investment), putting it into practice requires a clear understanding of what “return” means in a data context.

That’s where the essential roles of key components, like data product value, data downtime, and data investment, come into play. Each represents a different factor in the analytics equation, helping you quantify both the impact and the cost of your data initiatives.

Organizations can use the following equation to calculate data analytics ROI:

Data ROI = (Data product value – data downtime) / data investment

Data product value

Data product value refers to the measurable outcomes generated by the dashboards, reports, models, or tools built by your analytics teams. These “products” help users make better decisions, automate processes, or uncover new business opportunities.

Take for example, a predictive sales model that improves conversion rates by 10 percent. Or a dashboard that helps reduce inventory waste by $200,000 every year. Both examples illustrate how data contributes directly to real value.

To measure this, track the business impact attributed to each analytics deliverable, such as revenue growth, cost savings, or time saved, and link those outcomes to the tools or insights that enabled them.

Data downtime

Data downtime represents the periods when analytics tools or data products are unavailable, inaccurate, or untrusted—essentially when they fail to deliver value. This includes broken dashboards, delayed data refreshes, missing reports, or inconsistent metrics that prevent users from making timely or confident decisions.

For example, imagine if your finance dashboard went down right as your closing the books for the quarter. The time lost and the risk incurred can be substantial.

By reducing data downtime, you increase your ROI by ensuring that analytics products are consistently delivering reliable value to the business. You can quantify data downtime by tracking incidents, calculating the cost of delayed decisions, or estimating lost productivity due to unavailable or untrusted data.

Data investment

Data investment includes all the resources required to create, maintain, and scale your analytics capabilities. This can span everything from software and licensing costs (like BI platforms and cloud storage) to staffing (think data analysts, engineers, and scientists), training, governance, and the necessary infrastructure.

For instance, imagine if your organization spends $500,000 annually on analytics tools and salaries. That figure now becomes your baseline investment.

To accurately calculate the ROI of your data analytics, compare the total business value you’re generating, like increased revenue or cost savings, to your overall investment in analytics. A higher return relative to this investment means your analytics program is creating meaningful value.

Challenges to calculating data analytics ROI

Calculating data analytics ROI can be surprisingly complex, even for organizations that are confident in their data capabilities. One major challenge is attribution—linking a specific business outcome directly to a data product or insight. While dashboards, models, and reports often inform decisions, they’re rarely the only factor influencing a result, which makes isolating the impact of analytics difficult.

Another common issue is measuring intangible value, such as improved decision-making, reduced risk, or increased agility. These benefits are real, but they’re harder to quantify in financial terms, especially when analytics enable outcomes indirectly or over a long time horizon.

There’s also the challenge of incomplete or inconsistent tracking. Many organizations don’t have clear documentation of who used a data product, how they used it, or what changed because of it—making retroactive ROI calculations feel like guesswork. In addition, misaligned timeframes between investment (e.g., infrastructure or team costs) and value realization can skew ROI metrics. You might invest heavily in a new platform this year, but see the full return over the next two.

Finally, a lack of standardization across teams or departments often leads to inconsistent measurement, with each group defining value in different ways. This makes it harder to compare or consolidate ROI across the organization.

Overcoming these challenges requires not just better tools, but also clear frameworks, cross-team alignment, and a shared understanding of how data drives value.

Other ways to calculate ROI

While the classic formula for calculating data analytics ROI is a strong baseline, it’s not the only way to assess the impact of your data efforts. In more nuanced or advanced data environments, organizations often use alternative models that reflect different aspects of how data creates value.

These alternative approaches give organizations more flexibility in how they measure the value of their data efforts—whether by progress over time, feature-level wins, adoption success, or real-world actions inspired by insights.

Here are four meaningful ways to calculate or frame data analytics ROI beyond the basic equation, especially as analytics initiatives become more mature and embedded in business operations:

Data maturity ROI

This approach focuses on the return your organization gains as it climbs the data maturity curve—from basic reporting to advanced predictive and prescriptive analytics. As you invest in better governance, quality, and access, you open the door to more advanced capabilities and higher-value outcomes.

For example, imagine that a company evolves from relying on monthly static reports to using real-time forecasting. If this shift leads to a measurable improvement in the speed or accuracy of decision-making, that incremental improvement represents a return on its data maturity investment.

Measuring data maturity ROI often takes time and is tied to capability benchmarks, like self-service adoption rates or time-to-insight.

Product increment ROI

Rather than calculating ROI at the program level, this model evaluates the return on individual analytics features or deliverables—such as a new dashboard, data model, or KPI metric. This is especially useful for agile teams working in sprints.

For example, if a dashboard designed to monitor supply chain risk helps avoid $100,000 in potential disruption, and it took $10,000 worth of time and resources to build, the product increment ROI is 900 percent.

This method is more granular and helps data teams demonstrate value continuously, instead of waiting for long-term program reviews.

Adoption-based ROI

This approach links return to user engagement and adoption of data tools. If people aren’t using the dashboards, insights, or models created by your analytics team, you’re not getting the full return on your investment. Tracking usage rates, number of active users, and repeat engagement can help tie analytics tools to real business outcomes.

For instance, if a sales team starts using a forecasting dashboard regularly and closes deals 20 percent faster, that change in performance can be partially attributed to increased adoption—and measured accordingly.

Data-driven changes ROI

Here, ROI is measured by the impact of actions that were taken because of data. Instead of measuring the value of a data product directly, you look at the outcomes from decisions or changes made as a result of analytics insights.

For example, if a marketing team shifts budget allocation based on campaign performance data and sees a 25 percent improvement in ROI, the return is tied to the insight that enabled the shift—not just the report itself.

This model connects data analytics directly to behavioral change and business transformation.

Best practices for measuring data analytics ROI

Calculating the ROI of your data analytics efforts isn’t always straightforward—especially when multiple teams, tools, and outcomes are involved.

However, accurately measuring return is essential for understanding the true impact of your analytics investments and making informed decisions about where to focus next.

The key is to take a structured, consistent approach that captures both the value delivered and the full cost of delivery.

Below are several best practices to help you calculate data analytics ROI clearly, confidently, and with credibility.

1. Define clear objectives and success metrics upfront

Before launching a data analytics initiative, establish what success looks like and how it will be measured.

Are you aiming to reduce operational costs, increase revenue, improve forecasting accuracy, or speed up reporting cycles?

Defining these outcomes from the start ensures you’re measuring against the right benchmarks and can tie results directly back to business goals.

2. Attribute business outcomes directly to data products

To calculate ROI, you need to connect specific business results to the analytics products or insights that enabled them.

For example, if a marketing dashboard led to a 15 percent increase in campaign performance, document that link.

This helps separate the impact of analytics from unrelated variables and gives you a defensible basis for your ROI calculations.

3. Quantify both tangible and intangible benefits

While hard metrics like revenue increases or cost savings are ideal, don’t ignore intangible benefits like time saved, improved decision-making speed, or reduced risk.

For instance, if a real-time dashboard replaces weekly manual reports, estimate the hours saved and translate that into labor cost reductions or faster reaction time.

4. Track total costs holistically

Be sure to include all relevant costs in your investment calculation—not just software and licenses.

This includes labor (analysts, engineers, developers), training, infrastructure, and even the cost of maintaining or updating data pipelines.

Underestimating your investment will skew your ROI and may lead to overestimating impact.

5. Account for data downtime and missed opportunities

Factor in times when analytics tools are unavailable, inaccurate, or ignored due to a lack of trust. These are hidden costs that reduce the real value of your investment.

Tracking downtime, user adoption gaps, or rework due to unreliable data helps you measure the “lost” ROI you could recapture with better performance.

6. Use consistent timeframes for comparison

Make sure the period used to measure returns matches the period over which costs were incurred.

For example, if a predictive model generated $200,000 in value over six months, compare that against the six-month cost of building and maintaining it—not a full year of platform expenses.

Misaligned timeframes can distort the true ROI.

How to maximize data analytics ROI

While calculating ROI is critical, maximizing it is where organizations see the real payoff. To get the most value from your analytics investments, it’s not just about the tools you use—it’s about how effectively those tools are embedded into your workflows, strategy, and decision-making culture.

Here are a few key ways to increase the return on your data analytics initiatives and ensure you’re turning insights into impact:

Prioritize analytics projects with high business value

Focus your data team’s efforts on initiatives that directly support revenue generation, cost reduction, or strategic goals. Don’t just build dashboards—build solutions tied to clear outcomes.

Promote data literacy across the organization

The more people who can understand and act on data, the greater the value it creates. Invest in training and tools that make analytics accessible to non-technical users.

Increase adoption through self-service and usability

Make it easy for teams to find, explore, and use data without going through gatekeepers. User-friendly dashboards and intuitive interfaces drive engagement and reduce reliance on analysts for routine questions.

Continuously iterate based on feedback and results

Analytics shouldn’t be static. Regularly update data products based on performance, user feedback, or changing business needs to keep them relevant and impactful.

Get a 455% ROI with Domo

Measuring data analytics ROI isn’t just about proving the value of your tools—it’s about aligning data efforts with real business impact. When you understand what to track, how to track it, and how to tie analytics to outcomes, you can make smarter decisions about where to invest and how to scale.

Remember, data is your most valuable asset. Put it to work with Domo’s data analytics platform. According to a Forrester report, customers can realize a 455 percent ROI by using Domo’s low-code data app platform that goes beyond traditional business intelligence. Read the report here.