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The Latest Innovation in Data Quality - Data Observability

In the rapidly evolving digital era, data has accelerated in its value and importance to powering accurate decision-enablement for businesses across the globe. However, the sheer volume, complexity, and diversity of data types have introduced significant challenges in ensuring its accuracy, availability, and reliability. This is where Data Observability, leveraging AI, has rapidly moved to the forefront as a critical automated accelerator for organizations aiming to maintain high-quality data assets.

Data Observability is defined as an extension of the concept of observability in engineering, which focuses on proactive monitoring, tracking, and triaging issues within a system. It encompasses a set of ai-enabled automation tools and practices designed to help data teams proactively monitor data pipelines, detect and diagnose issues, and ensure data quality across the entire data lifecycle.

By providing comprehensive visibility into the health of data moving through pipelines into curated data assets, Data Observability enables real-time identification, and in some cases, on-the-fly remediation of inaccuracies, inconsistencies, and other anomalies that could impact business outcomes driven by decisions that are enabled by analytics based on curated data assets.

The automation-enabled Data Observability approach offers a proactive approach to data quality management. Traditional data quality management strategies often rely on reactive, and highly human-centric measures, addressing issues only after they have manifested into larger problems, sometimes not identified until a decision maker questions the validity of insights based on underlying data assets. With Data Observability, however, data management teams are empowered with the tools to predict and prevent issues before they escalate, thus maintaining the integrity and reliability of data.

In addition, Data Observability facilitates better collaboration among data engineers, scientists, and analysts by providing a common framework and language for understanding data health. This enhanced communication leads to more efficient problem-solving and decision-making processes, further bolstering an organization's data-driven capabilities.


Top Data Observability Tools (One Man’s Opinion)

Several cutting-edge tools have emerged in the marketplace, each offering unique features to tackle the challenges of Data Observability. Here are some of the top tools currently leading the way:

  1. Monte Carlo is well known for its comprehensive approach to Data Observability, Monte Carlo helps organizations automatically detect and remediate data issues. It offers features like end-to-end data lineage, anomaly detection, and real-time alerts, making it a powerful ally in proactively ensuring data integrity.

  2. DataDog is widely recognized for its cloud monitoring solutions, DataDog has expanded its offerings to include Data Observability. Its capabilities allow for the monitoring of data pipelines, databases, and data lakes, providing insights into performance issues and operational inefficiencies.

  3. Databand provides a proactive Data Observability platform designed to identify issues in data pipelines before they affect downstream processes. It provides detailed observability into data quality, pipeline health, and operational metrics, facilitating quicker issue resolution and overall operational health.

  4. Bigeye focuses on automating data quality monitoring and anomaly detection. It allows data teams to set custom metrics for data quality, receive alerts for potential issues, and track data health over time, improving overall data quality in large and complex data ecosystems.

  5. Acceldata is a multi-dimensional Data Observability platform encompassing data quality, performance, and consumption observability. It is designed to provide data teams with insights into how data is being used, its quality, and the performance of data systems, enabling more informed decision-making and strategic planning.

Concluding Thoughts

As data continues to elevate in importance for organizations, the role of Data Observability in proactively ensuring its reliability and integrity cannot be overstated. With the advent tools like those mentioned above, businesses are better equipped than ever to navigate the complexities of modern data systems while proactively ensuring integrity of the data assets contained therein.

That said, I must offer a word of caution – buyer beware! This lane of technology is fairly new and rapidly evolving. Many of our clients have expressed concerns, and we agree, that Data Observability tools are diverse in their feature set, and relatively expensive. Meaning it is not easy to make an Apples-to-Apples comparison of competing products. So, here is where the old adage of “wisdom is found in a multitude of counselors” rings very true.

At New Era we are leading the way in helping our clients replace confusion with clarity as they chart a course to embrace the opportunity created by these tools, while minimizing the risk of making a bad decision along the way. If you would like to explore how we can help your organization navigate these choppy waters feel free to reach out!

About the author

Mark I Johnson

Mark I Johnson is the Executive Leader for Strategic Data Management and Analytics at New Era Technology, Visionary Co-Founder and Editorial Board Chair of CDO Magazine, and a member of the Organizing Committee for CDO IQ Symposium.


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