What Is Data Observability and Why You Need It?


Experts estimate that the world is generating 2.5 quintillion exabytes of data every day. That information resides in multiple systems, including legacy on-premises systems, cloud applications, and hybrid environments. However, amid this challenge lies a significant opportunity for organizations to harness the power of data for informed decision-making and strategic insights.

Challenges Faced by Organizations: Insights from the Precisely Data Trends Survey

The Precisely Data Trends Survey brings to light the prevailing challenges faced by organizations in the data landscape. With over two-thirds reporting negative effects due to disparate data and nearly half of newly created data records containing critical errors, the need for a robust data management strategy becomes evident. Moreover, the Harvard Business Review underscores the severity of the situation, revealing that a staggering 84% of CEOs harbor doubts about the integrity of the data upon which crucial business decisions are based.

Enter Data Observability: A Pillar of Data Stewardship

Facing these challenges, the idea of data observability becomes crucial in managing data effectively Although it’s been a known factor in software engineering, using it in data operations (DataOps) is relatively new. This method actively watches over and ensures the quality and availability of enterprise data as it moves through different systems, offering a proactive solution to the complexities of modern data management.

Understanding Data Observability: Going Beyond Traditional Monitoring

Unlike traditional monitoring approaches, data observability provides deeper insights into the dynamics of the data, offering a comprehensive understanding of what is happening and why. It involves not only collecting information about the data’s location, content, and users but also proactively and continuously monitoring, analyzing historical trends, and employing real-time machine-learning intelligence to detect anomalies and outliers.

Core Components of Data Observability: A Three-Pillar Approach

  • Discovery, Profiling, and Monitoring: This pillar involves gathering information about the data’s location, content, and user interactions, followed by vigilant and continuous monitoring.
  • Analysis: The processing of information about enterprise data, delving into historical trends, and employing AI/ML for intelligent analysis.
  • Visualization and Alerting: Providing users with dashboards for real-time data activity visualization, issuing proactive alerts for outliers, and offering additional context for informed decision-making.

Key Attributes Monitored by Data Observability: Ensuring Data Health

Data observability focuses on key attributes to ensure the health of enterprise data. Distribution tests determine whether values fall within acceptable ranges, volume checks watch for unexpected record numbers, and schema analysis helps understand changes in data organization.

Data Observability in Action: Early Detection and Proactive Issue Resolution

Data observability becomes a proactive strategy for identifying potential data issues early in the data lifecycle, preventing downstream complications, and eliminating the need for reactive problem-solving. In a landscape where manual identification and correction of data quality issues are impractical due to the sheer volume of data, data observability emerges as a scalable solution.

Distinguishing Data Observability from Data Quality: Anomaly Detection and Confidence in Data

Distinguishing data observability from data quality, the former focuses on anomaly detection and understanding the reasons behind unexpected changes. It serves to answer the crucial question: “Is my data ready to be used?” Different stakeholders, from operations managers relying on downstream analytics to data scientists building machine learning models, benefit from the confidence in data provided by data observability.

Real-World Scenarios: From Development Changes to Regional Anomalies

Consider a scenario where a development team makes changes to a core operational system. Altering key data columns, unbeknownst to them, may impact downstream applications. A data observability tool in this instance acts as an early warning system, identifying changes and alerting users to take corrective action.

Similarly, envision a sudden and unexpected drop-off in orders from a specific region. A data observability tool quickly identifies this anomaly, alerts key users, and aids in investigating the root cause. In this way, teams can proactively resolve issues, ensuring the timely processing of critical business operations.

Data Observability vs. Traditional Monitoring: A Paradigm Shift

Expanding beyond the notion of data monitoring, data observability draws parallels with continuous diagnostic tools in the medical field. Just as these tools provide a constant stream of data to understand a patient’s health and guide appropriate action, data observability, armed with advanced technology and machine learning intelligence, continuously detects patterns and anomalies, enabling proactive issue resolution.

The Paramount Importance of Data Observability: Trust in Data for Strategic Decisions

The paramount importance of data observability lies in its role in ensuring trust in data for accurate reporting and analytics. By detecting anomalies and alerting the appropriate users, data observability empowers organizations to be proactive rather than reactive, addressing data issues that have the potential to disrupt business operations and create costly downstream problems.

Additional Benefits: Reducing Costs, Minimizing Time to Resolution, and Supporting Transformation Initiatives

Reducing costs, minimizing time to resolution for operational issues, and supporting successful transformation initiatives are additional benefits offered by data observability. In an era where digital transformation is a top priority for many businesses, the need for a comprehensive understanding of what is happening to data becomes crucial.

Integrated Solutions and the Future of Data Observability: The Precisely Approach

As organizations look to embrace data observability, integrated solutions that encompass an inclusive data catalog become essential. An integrated data catalog provides a single searchable inventory of data assets, allowing technical users to explore and understand their data effectively. It facilitates visualization of relationships among various data sets and offers collaboration tools for certification, auditing, and tracking data across its lifecycle.

Conclusion: Data Observability as a Paradigm Shift in Data Management

In essence, data observability is not just a tool or a strategy; it is a paradigm shift in how organizations approach and ensure the reliability of their data. As part of the broader Precisely Data Integrity Suite, data observability emerges as a key player in delivering accurate, consistent, and contextual data to businesses whenever and wherever it’s needed. With its focus on understanding overall data health, reducing risks in analytics, and proactively solving problems at their root, a robust data observability strategy becomes imperative for organizations navigating the complex landscape of modern data management.

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