ROI of data observability: 5 Essential areas for building a strong business case

Modern data stacks are evolving rapidly, empowering organizations to answer complex questions and deliver insights at an unprecedented scale. Data teams are instrumental in operationalizing these systems to enhance decision-making and drive customer-centric innovations. However, the reliability of this data remains a fundamental challenge—without trustworthy data, even the most advanced systems falter.

Data Observability empowers organizations to harness the full potential of their data by identifying, troubleshooting, and resolving data issues in real time.

Despite its potential, understanding and demonstrating the return on investment (ROI) of data observability can be a complex task. If these questions sound familiar, this article is for you:

• Didn’t we already spend a lot of budget on a warehouse and a BI tool?
• Why don’t we build this internally if you just hired a data engineer?
• How do we measure data quality’s return on investment (ROI)?

This article explores five critical areas where data observability can create measurable value, helping organizations build a solid business case for investment.

1. Reducing the operational costs of In-House solutions

Enterprises often task their data engineering teams with building and maintaining custom data monitoring tools tailored to their unique requirements. While this approach may seem cost-effective initially, it often leads to hidden inefficiencies that escalate over time.

Over time, maintaining and scaling an internal data observability system becomes a resource-intensive effort. Like any operational software, it requires dedicated engineering resources, specifically data engineering and data science expertise, to model and construct anomaly detection models. Additionally, a dedicated quality assurance team and DevOps engineers are required to deploy the solution and ensure its ongoing reliability.

Moreover, these homegrown solutions often lack advanced capabilities, which forces teams into an iterative cycle of addressing edge cases. This reliance on manual processes limits scalability and demands continuous development, testing, and troubleshooting. As a result, engineering teams that should be focused on innovation and core business initiatives find themselves trapped in maintenance cycles, leading to increased overhead and reduced agility.

Measuring the Impact: To quantify ROI, enterprises must consider the following cost drivers:

• Direct engineering costs: The number of engineers needed for development and maintenance multiplied by the annual full-time equivalent (FTE) cost per engineer
• Hidden operational costs: Quality assurance, DevOps support, and infrastructure maintenance required to keep these systems running

Although the salary ranges for these specialized roles may vary, we can compute an average across all team members for the sake of simplification.

Formula: ROI = (total # of engineers to build + # of engineers to maintain) * FTE ($)

2. Reducing indirect infrastructure costs

While direct engineering costs are often the most visible expense of maintaining data quality, the indirect infrastructure costs associated with inefficient data monitoring can be just as significant. Many organizations unknowingly drive up their cloud, compute, and storage expenses due to the lack of a structured approach to data observability.

Without an automated observability solution, teams often rely on heavy queries, redundant scans, and excessive logging to track data health across pipelines. These reactive monitoring efforts increase data warehouse and cloud computing costs, especially in pay-as-you-go environments where every unnecessary query adds up. Due to these cost considerations, many organizations opt-in to validate and monitor only samples, resulting in limited data quality improvements and incomplete results.Similarly, organizations that store large volumes of historical logs for debugging purposes end up overprovisioning storage, further inflating costs.

Specific Data Observability solutions are designed with comprehensive data quality analysis, storage, and hosting capabilities integrated within the platform. This approach eliminates the need to offload these services onto the monitored systems, effectively mitigating the associated expenses. Moreover, this approach offers scalability, enabling the detection of data quality issues across the entirety of the data rather than relying solely on samples.

By implementing an automated data observability platform, organizations can eliminate redundant queries, optimize storage, and proactively detect data issues before they require expensive reprocessing. This leads to leaner, more cost-effective infrastructure operations while maintaining high data reliability.

Measuring the Impact: Break down these costs into:

• Compute and query costs –% of database overage fees related to validation queries.
• Storage overhead –% of extra storage costs for retaining historical data quality metrics.
• Pipeline efficiency –% of increased cloud hosting expenses to support data quality at scale.

Formula: ROI = (annual data warehouse costs * % overage related to data validation queries) + (annual storage costs * % overage in storing historical data quality metrics) + (annual cloud infrastructure costs * % overage in hosting data quality at scale)

In many organizations, infrastructure costs are often consolidated with a single vendor who offers comprehensive services, including data warehousing, storage, and cloud hosting. In such cases, calculating ROI involves multiplying the total infrastructure costs by a percentage (typically between 10% and 20%) to represent the increased impact of data quality monitoring. For instance, if an organization’s annual cloud data warehouse expenses cost $1 million, implementing data quality and observability could yield an indirect impact of 10%, equivalent to $100,000 annually.

3. Lowering incident management cases

Incident management in data operations is often a reactive firefighting process, where teams scramble to resolve issues after they have already caused damage. While proactive prevention through data observability is the goal, it is not always feasible to catch every issue before it impacts downstream systems. When data quality issues lead to inaccuracies in business applications or customer-facing platforms, it is not just the data team that gets involved—business stakeholders, operations teams, and even leadership may need to step in to diagnose and fix the issue. These disruptions translate into wasted time, lost revenue, and decreased productivity across the organization, making incident resolution a crucial factor in the ROI calculation of data observability.

Measuring the Impact: Data teams often categorize incident management based on severity levels. For instance, one company classifies its data incidents as follows:

• Average number of incidents per year – Total occurrences of data issues, categorized by severity.
• Time to detect and resolve incidents – The time spent identifying, troubleshooting, and fixing data issues, factoring in all teams involved.
• Personnel and operational costs – The number of employees engaged in resolving incidents and their average hourly cost.

Many organizations classify data incidents based on severity levels:

To simplify, you can group incidents together and calculate the averages across all cost drivers.

• Average # of incidents per year.
• Average time to resolve incidents in hours.
• Average hourly cost to correctly detect and remediate these issues.

Formula: ROI = (Average # of incidents per year) * (Average time to detect and resolve the incident in hours) * (Average hourly cost)

4. Creating trusted data for better decision-making

While the previous ROI factors primarily focused on cost savings, the next two highlight revenue-generating opportunities enabled by data observability. Trustworthy data is the foundation of accurate reporting, effective decision-making, and strategic business growth. However, determining the exact revenue impact of data observability can be complex.

For example, if improved customer data quality leads to higher retention rates, data observability plays a role, but it is not the only contributing factor—team expertise, product enhancements, and other business initiatives also influence the outcome.

To measure ROI, organizations must define the problem scope, establish a baseline cost, and estimate the improvement directly attributed to observability.

Measuring the Impact: To quantify how data observability improves revenue-driving decisions, organizations should assess:

• Problem Statement: Identify how poor data quality affects key business objectives. For example, “Inaccurate data hinders our [business objective, e.g., customer retention].”
• Baseline Value: Estimate the annual financial impact of poor data on the organization. For example, “Inaccurate data results in annual costs of $X for the organization.”
• Addressable Scope: Determine how much of this problem can realistically be improved through data quality initiatives. For example, “We anticipate improving this by Y%, recognizing that some revenue loss is inherent to our business due to factors beyond data quality.”
• Expected Improvement from Data Observability: Isolate the portion of this improvement that can be attributed to an observability solution. For example,“We expect a Z% of the improvement can be attributed to a data observability solution.”

Formula: ROI = Baseline value ($X) * Addressable Scope (Y%) * Expected Improvement (Z%)

It is important to recognize that data observability is one part of a broader data strategy. While it plays a crucial role in improving data reliability and decision-making, other factors—such as process improvements, team expertise, and complementary tools—also contribute to better outcomes.

5. Accelerating time to value of data products

Data products have become increasingly popular, but their success hinges on the quality and reliability of the underlying data. Data observability offers a systematic approach to detecting and addressing data issues promptly, thereby accelerating the time-to-market for these products. By establishing real-time analysis and remediation processes, data observability ensures that data products remain reliable when accessed by consumers.

Measuring the Impact: To calculate the impact on data products, evaluating the time-to-market delay resulting from data quality and consistency issues is essential. Some data observability tools offer a low-code, no-code interface that fosters collaboration between business and technical users. This accelerates the development and testing of data quality, helping you reach revenue goals more rapidly. These tools use machine learning (ML) to assess data quality and identify outliers and anomalies, streamlining a process that would otherwise be time-consuming and reliant on guesswork.

Furthermore, these observability platforms harness historical data trends to detect unexpected data issues in real-time. This real-time monitoring capability empowers product and engineering teams to ensure the continuous health and reliability of data products, contributing to revenue growth.

Formula: ROI = Annual revenue of data products per year * time to market delay due to bad data

Calculate your ROI with Data Observability

Closing thoughts

These five key areas highlight how data observability delivers measurable business value, from reducing operational costs to accelerating revenue generation. While the specific impact may vary across organizations, each factor is crucial in maximizing the return on investment.

When building your business case for data observability, consider both cost-saving efficiencies and revenue-driving opportunities. Collaborate with executive teams to assess all cost drivers, evaluate potential gains, and establish a clear timeline for implementation. Quantifying the return on investment in a structured, data-driven manner will help secure buy-in and ensure long-term success.

Data observability is not just an operational expense. It is a strategic investment. Organizations can transform their data from a potential liability into a powerful competitive advantage by minimizing time spent on troubleshooting, optimizing infrastructure costs, improving data reliability, and accelerating product innovation.

Discover how Telmai can transform your approach to ensuring Data Quality. Try Telmai today!

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