Unlocking data potential: The power of merging data catalogs and observability

In today’s fast-paced digital world, which is constantly reshaped by the advancements in Artificial Intelligence (AI) and Machine Learning (ML), picture the life of a data professional who navigates through these complex and ever-evolving challenges.

Data engineers frequently encounter challenges in managing data flow and processing, primarily due to a lack of clear information about the data’s origin, structure, and lineage. This ambiguity hampers their ability to construct and maintain data pipelines effectively. On the other hand, data analysts often grapple with ensuring data accuracy and consistency. Their challenges also include comprehending the context of data sets and verifying the currency and relevance of the data they utilize for analysis. Data scientists, meanwhile, face obstacles in identifying meaningful patterns and trends. Irregularities or gaps in historical data often complicate their work, and they may struggle to replicate experiments or comprehend the impact of changes in data sources on their models.

Data Catalog and Observability tools mark a transformative phase in data management. These tools bring structured visibility and real-time monitoring to the entire data lifecycle. They offer clear mapping, lineage tracking, and quality insights, directly addressing the fundamental challenges faced by data engineers, analysts, scientists, and other data professionals. This shift ushers in an era of clarity and actionable intelligence, replacing potential disarray with systematic and informed understanding.

What is a data catalog?

Imagine a supermarket with rows of produce, each item offering different flavors and nutrients. Finding the specific fruit or vegetable you need can be challenging, especially if you’re unfamiliar with its appearance or name. This is where labels and signs in the supermarket come in handy, guiding you to the right section and providing information about each item.

A data catalog serves a similar purpose for organizations with vast data. Just as labels in a supermarket help you navigate through different aisles and identify various products, a data catalog helps navigate large volumes of data. It is a centralized metadata repository providing detailed information about the organization’s data assets. Think of it as the labeling system that organizes and categorizes your data, making it easily searchable and accessible.

A data catalog goes beyond simply listing data sources. It enriches the data with descriptive details, such as its origin, format, lineage, quality metrics, and usage patterns. This wealth of information makes it easier for data professionals to discover, understand, and trust the data they need. This way, everyone can be more productive and make better decisions because they have the correct information.

What is data observability?

Now, think about our supermarket again. You wouldn’t pick a can of milk that is expired? Data observability is like a vigilant store assistant who continuously inspects the produce. They ensure that each item is fresh (taking note of expiry dates) and correctly sourced, highlighting its origin, whether locally grown or genetically modified.  

For businesses, data observability is about fully understanding the state of data in the system. It’s like a health check, detecting and diagnosing issues with data quality, pipeline failures, schema changes, and other anomalies in real-time. It keeps track of data health throughout its lifecycle, ensuring accuracy, consistency, and reliability.

Convergence of data catalogs & data observability

Returning to the analogy, when you combine the informative labels and signs (data catalog) with the attentive store assistant (data observability), you achieve an exemplary supermarket experience. You can swiftly locate the correct item, fully informed of its source, whether it’s local or genetically modified, and its freshness. This ensures that your selection meets your expectations – fresh, ripe, and accurately represented, with no surprises in quality or origin.

Data observability often has limited visibility in the business context of data assets, while data catalogs may not have real-time insights into data quality and performance issues. Moreover, data producers and consumers may need to switch between multiple tools to get a holistic view of their data, which can be time-consuming and inefficient.

Combining these two makes sure that businesses can find their information quickly and trust that the data is good. That way, when they have to do important stuff, like figuring out what new products to make or what customers to target, they make those choices based on the best and most accurate info they have. It’s like having a super-smart supermarket that’s automatically organized and reliable!

Data catalogs and observability serve complementary functions—one is about organizing and understanding data assets, and the other is about monitoring and ensuring data quality and pipeline health. There’s a natural synergy between them, as a data catalog enriched with observability insights provides a more comprehensive understanding of an organization’s data assets and reliability.

Who benefits from this union?

The convergence of data catalog and observability can lead to the democratization of data across an organization, where each persona benefits from the insights (See Table 1). Here are some of the benefits of a unified approach:

• Enhanced Data Governance and Remediation: For roles like Data Engineers and Data Stewards, the convergence of Data Catalog and Observability tools facilitates quicker resolution of data issues and better data governance by linking real-time monitoring with comprehensive data knowledge.

• Streamlined DataOps and MLOps: Roles such as Data Analysts, ML Engineers, MLOps Engineers, and Data Scientists benefit from streamlined operations and improved workflow efficiency, as they can quickly identify and resolve data issues, ensuring high performance of models and analysis.

• Integrated Insights for Strategic Planning: For Data Architects, IT Operations Managers, and ML Product Managers, this convergence aligns data strategy with actual data health, enabling more informed decisions and strategies, particularly regarding data infrastructure, compliance, and product development.

Wrapping up

Data catalogs and data observability tools are like the nervous and immune systems of an organization’s data body. They work together to keep the data flowing to the right places, keep it healthy, and react quickly if something goes wrong. This coordination is critical to smooth and effective DataOps and MLOps processes, helping organizations to deploy and maintain data and ML-driven solutions with confidence and agility.

A comprehensive data catalog becomes indispensable as we navigate an era of rapidly expanding data and AI. It is a detailed map for organizations to understand and utilize their data resources effectively. However, the dynamic nature of data landscapes demands more than static mapping; data observability steps in as a real-time navigator, constantly updating and ensuring the accuracy and safety of these data pathways.

The fusion of data cataloging and observability creates a dynamic and trustworthy blueprint for data landscapes, transforming raw data into actionable insights and empowering informed decision-making. This integration is crucial for harnessing the present-day potential of data and sets the stage for future advancements. It paves the way for autonomous, self-correcting data ecosystems that can proactively manage discrepancies, streamline compliance, and unlock new growth opportunities. This convergence is key to shaping the future of data-driven innovation and possibilities.

About author

Ankur Gupta brings a wealth of experience in product marketing, product management, and business analytics. He most recently served as the Product Marketing Director at Collibra, a data governance and catalog leader. Before that, Ankur held key positions at Talend, Reltio, Krux, and Yahoo. Ankur earned his MBA from Cornell University and engineering degrees from the Indian Institute of Technology Delhi. He currently resides in San Jose with his wife and two young children.

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