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Nov 2, 2023
Data Observability 101

Considering the ROI of data observability initiatives

Post by
Benedetta Cittadin

The implications are broader than you might think

In today’s digital age, data quality is of paramount importance for business to make informed decisions, deliver personalized experiences to customers, and drive growth. However, maintaining data quality can be challenging, and it is often difficult to measure the return on investment (ROI) of data quality initiatives. This blog explores the significance of data observability, how it can mitigate the risks of bad data, and ways to measure its ROI. By understanding the impact of bad data and implementing effective strategies, organizations can maximize the benefits of their data quality initiatives. 

How bad is bad data?

Bad data can have damaging consequences, leading to incorrect decisions, financial losses, legal implications, and reduced team productivity. Poor data quality often results in strategic decisions such as misguided investments that harm a company’s performance. The cost of data quality issues varies across organizations, with the average cost estimated at  13 million dollars annually according to a 2021 Gartner report

For example, in 2022, Unity Software reported a loss of $110 million in revenue and $4.2 billion in its market cap. “Consequences of ingesting bad data from a large customer”, the company stated. Similarly, bad data caused Equifax, a publicly-traded credit reporting agency, to send lenders inaccurate credit scores on millions of customers.

The impact of data quality issues

Data quality issues can result in various problems, including loss of trust in data, reduced team productivity and morale, non-compliance with regulations, and diminished quality of decision-making. Siloed data within departments or business units makes it challenging to gain a holistic view of the organization’s data landscape. This can lead to ineffective decision-making, hinder data culture, and jeopardize compliance with regulations like GDPR and HIPAA. Moreover, data teams can become frustrated by spending excessive time troubleshooting data issues, negatively impacting their job satisfaction and potentially leading to employee churn. 

Achieving data quality through data observability

Data observability is a solution to proactively monitor and maintain the health of data throughout its lifecycle. By implementing logging, tracing and monitoring techniques, organizations gain visibility into data streams, quickly identify and troubleshoot data quality issues, and prevent disruptions to analytics dashboards. Data literacy, involving sourcing, interpreting, and communicating data, is essential for decision-makers to translate data into business value effectively. Cultivating a data-driven culture and investing in the right tools are crucial steps towards achieving data quality through data observability. 

Measuring the ROI of data observability

Measuring the ROI of data observability helps business leaders understand the value and benefits associated with investing in this practice. Several quantifiable metrics can serve as a starting point for evaluating the cost of bad data, including the rate of occurrence or number of incidents per year, time to detection, and time to resolution. These metrics provide insights into the frequency and efficiency of identifying and resolving data quality issues:

  • Rate of occurrence or number of incidents per year: measuring the frequency of data incidents helps to understand the likelihood or probability of such incidents happening within a specific time period
  • Time to detection: represents the duration it takes for the data engineering team to identify a data quality issue
  • Time to resolution: the average time spent from becoming aware of a data incident to resolving it. The resolution time is influenced by the criticality of the incident and the complexity of the data platform

Additionally, non-quantifiable metrics such as effective decision-making, preservation of data trust, regulations compliance, and satisfaction of the data team, contribute to the overall value of data observability:

  • Effective decision-making: data observability enables the identification and timely resolution of issues that impact business decisions. By monitoring data in near-real-time, problems can be found early and corrected, leading to improved decision-making, This, in turn, brings benefits such as increased profits, a stronger competitive edge, and higher customer satisfaction
  • Preservation of data trust: trust is easily lost and hard to regain. Accurate and up-to-date data is crucial for successful data-driven decision-making. Organizations facing data quality issues risk losing their team’s trust in data usage, jeopardizing a strong data culture, and leading to negative long-term consequences
  • Regulations compliance: implementing data observability helps address data governance challenges. With dynamic data flows and growing data teams, it becomes challenging to maintain up-to-date data documentation. Lack of monitoring and timely troubleshooting of data quality issues can result in compliance problems with regulations like GDPR and HIPAA
  • Satisfaction of the data team: data teams face constant pressure to deliver high-quality, reliable data products that help foster a data-driven culture. Spending significant time troubleshooting data issues instead of focusing on value-creating initiatives can lead to missed opportunities and employee dissatisfaction. Considering the difficulty and cost of hiring skilled data professionals, it’s crucial to ensure their time is spent on meaningful work rather than constant debugging. Read more on the importance of ensuring happiness in the data team in this blog

By addressing these aspects, data observability not only provides quantitative benefits but also safeguards against data issues, promotes trust, ensures compliance, and supports a productive and satisfied data team.


Data quality issues significantly impact businesses, leading to wasted resources and missed opportunities. Investing in data observability is essential to prevent and mitigate the risks associated with bad data. By leveraging quantifiable metrics and considering non-quantifiable factors, organizations can measure the ROI of data observability and demonstrate its value to decision-makers. Ensuring data trust, promoting effective domain decision-making, complying with regulations, and fostering a satisfied data team are all critical aspects of maximizing the benefits of data quality initiatives. Embracing data observability is a strategic investment that safeguards the accuracy, reliability, and utilization of data in today’s data-driven world.

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