Historically, data engineers have often prioritized building data pipelines over comprehensive monitoring and alerting. Delivering projects on time and within budget often took precedence over long-term data health. Data engineers often missed subtle signs such as frequent, unexplained data spikes, gradual performance degradation or inconsistent data quality. These issues were seen as isolated incidents, not systemic ones. Better data observability unveils the bigger picture. It reveals hidden bottlenecks, optimizes resource allocation, identifies data lineage gaps and ultimately transforms firefighting into prevention.

Until recently, there were few dedicated data observability tools available. Data engineers often resorted to building custom monitoring solutions, which were time-consuming and resource-intensive. While this approach was sufficient in simpler environments, the increasing complexity of modern data architectures and the growing reliance on data-driven decisions have made data observability an indispensable component of the data engineering toolkit.

It’s important to note that this situation is changing rapidly. Gartner® estimates that “by 2026, 50% of enterprises implementing distributed data architectures will have adopted data observability tools to improve visibility over the state of the data landscape, up from less than 20% in 2024”.

As data becomes increasingly critical to business success, the importance of data observability is gaining recognition. With the emergence of specialized tools and a growing awareness of the costs of poor data quality, data engineers are now prioritizing data observability as a core component of their roles.

Hidden dangers in your data pipeline

There are several signs that can tell if your data team needs a data observability tool:

• High incidence of incorrect, inconsistent or missing data can be attributed to data quality issues. Even if you can spot the issue, it becomes a challenge to identify the origin of the data quality problem. Often, data teams must follow a manual process to help ensure data accuracy.
• Recurring breakdowns in data processing workflows with long downtime might be another signal. This points to data pipeline reliability issues when the data is unavailable for extended periods, resulting in a lack of confidence among stakeholders and downstream users.
• Data teams face challenges in understanding data relationships and dependencies.
• Heavy reliance on manual checks and alerts, along with the inability to address issues before they impact downstream systems, can signal that you need to consider observability tools.
• Difficulty managing intricate data processing workflows with multiple stages and diverse data sources can complicate the whole data integration process.
• Difficulty managing the data lifecycle according to compliance standards and adhering to data privacy and security regulations can be another signal.

If you’re experiencing any of these issues, a data observability tool can significantly improve your data engineering processes and the overall quality of your data. By providing visibility into data pipelines, detecting anomalies and enabling proactive issue resolution, these tools can help you build more reliable and efficient data systems.

Ignoring the signals that indicate a need for data observability can lead to a cascade of negative consequences for an organization. While quantifying these losses precisely can be challenging due to the intangible nature of some impacts, we can identify key areas of potential loss

There might be financial loss as erroneous data can lead to incorrect business decisions, missed opportunities or customer churn. Oftentimes, businesses ignore the reputational loss where inaccurate or unreliable data can damage customer confidence in the organization’s products or services. The intangible impacts on reputation and customer trust are difficult to quantify but can have long-term consequences.

Prioritize observability so bad data doesn’t derail your projects

Data observability empowers data engineers to transform their role from mere data movers to data stewards. You are not just focusing on the technical aspects of moving data from various sources into a centralized repository, but taking a broader, more strategic approach. With observability, you can optimize pipeline performance, understand dependencies and lineage, and streamline impact management. All these benefits help ensure better governance, efficient resource utilization and cost reduction.

With data observability, data quality becomes a measurable metric that’s easy to act upon and improve. You can proactively identify potential issues within your datasets and data pipelines before they become problems. This approach creates a healthy and efficient data landscape.

As data complexity grows, observability becomes indispensable, enabling engineers to build robust, reliable and trustworthy data foundations, ultimately accelerating time-to-value for the entire organization. By investing in data observability, you can mitigate these risks and achieve a higher return on investment (ROI) on your data and AI initiatives.

In essence, data observability empowers data engineers to build and maintain robust, reliable and high-quality data pipelines that deliver value to the business.