Data Observability Explained

Modern companies need to monitor data across many tools and applications, but few have the visibility necessary to see how those tools and applications connect. Data observability can help companies understand, monitor, and manage their data across the full tech stack. In this article, you’ll learn what data observability is, the differences between data observability, monitoring, and data reliability, and what information you can track with data observability. By the end of this article, you’ll discover how to implement data observability and find the right data observability tools for your organization.

What is Data Observability?

Data observability entails the capacity to comprehend, diagnose, and oversee the well-being of data across diverse IT tools throughout its lifecycle. Utilizing a data observability platform, organizations can uncover, assess, and address real-time data issues by leveraging telemetry data, including logs, metrics, and traces.

Unlike conventional monitoring approaches, observability offers a more comprehensive perspective, enabling organizations to enhance security measures through the monitoring of data movement across various applications, servers, and tools. This advanced approach to data observability empowers companies to streamline the monitoring of business data and effectively manage the internal health of their IT systems by scrutinizing outputs in real time.

Benefits of Data Observability

For organizations managing numerous disparate tools, observability monitoring emerges as a valuable asset for IT teams seeking insights into system performance and overall health. Simplifying root cause analysis stands out as a primary advantage of data observability. By providing end-to-end visibility and monitoring across complex IT architectures, teams can swiftly pinpoint bottlenecks and address data issues, regardless of their origin.

Data monitoring typically involves tracking pre-defined metrics to assess system health, requiring prior knowledge of specific issues and metrics to monitor. In contrast, data observability empowers teams to actively debug and triage their systems by monitoring a broad spectrum of outputs. This expanded insight into how data interacts with different tools and moves within the IT infrastructure enables teams to identify unforeseen issues or improvements, resulting in a faster mean time to detection (MTTD) and mean time to resolution (MTTR).

The accessibility of pre-stored telemetry data facilitates automated security management. Data observability not only allows real-time issue identification but also automates parts of the triage process for instant detection of health issues or data downtime.

With a comprehensive 360-degree view of organizational data, companies can uphold high data quality, reinforce data integrity, and ensure data consistency across the entire data pipeline. Ultimately, this facilitates adherence to service-level agreements (SLAs) and empowers organizations to leverage high-quality data for effective analysis and decision-making.

Challenges of Data Observability

While an effective data monitoring system can revolutionize how organizations handle and uphold their data, the implementation of data observability presents challenges for certain organizations, contingent on their existing IT architecture.

Even top-notch observability tools may face limitations without comprehensive insight into the entire data pipeline, encompassing software, servers, databases, and applications. As data observability cannot operate in isolation, it becomes imperative to eradicate data silos and seamlessly integrate all systems, both internal and external, into the data observability software. The hurdle often lies in obtaining the necessary buy-in to incorporate every system and tool into the observability solution.

Furthermore, even with the integration of all internal and external data sources into the observability platform, disparate data models can create complications for the observability system. Considering that organizations typically manage an average of 400 data sources, variations in data standards across these sources are unsurprising.

Leading observability tools prioritize the standardization of telemetry data and logging guidelines to facilitate effective correlation of information. However, depending on the diversity of data sources and custom data pipelines, achieving data standardization may demand additional manual efforts with certain data observability tools. Additionally, storage costs associated with specific tools, influenced by data storage methods and organizational retention policies, may pose scalability challenges.

Data Observability Framework

To actualize agile DataOps within your organization, initiating a robust data observability framework is crucial. This effective framework serves as the cornerstone for fostering a secure, data-driven culture and formulating an observability strategy that champions impeccable data quality, consistency, and reliability.

At its essence, a data observability framework revolves around five pillars designed to ensure elevated data quality:

  • Freshness: Monitoring the frequency of data updates to eradicate stale data.
  • Distribution: Documenting anticipated data values to discern instances of data unreliability.
  • Volume: Tracking the expected number of values to verify data completeness.
  • Schema: Observing changes in data tables and data organization to pinpoint issues with data integrity.
  • Lineage: Collecting metadata and mapping upstream data sources along with downstream ingestors to troubleshoot breaks, identifying teams with access to the data.

Crafting a framework that encompasses these five pillars empowers organizations to effectively manage their metrics, traces, and logs. This comprehensive approach ensures an enhancement in data quality while maintaining end-to-end visibility within the data monitoring system.

Subsequently, organizations need a standardized data platform to draw data from and a standardized library delineating data quality. Mere data extraction from multiple sources is insufficient; organizations require a robust infrastructure to generate consistent, standardized data. This infrastructure should accommodate data retrieval from APIs, support data lake observability, and facilitate routine queries to data warehouses.

While a data observability platform integrates these components, they must seamlessly align with the overall data management strategy to instill the right culture around data sharing. The observability infrastructure equips teams with a comprehensive view of end-to-end data across the organization, expediting the detection and resolution of data issues. Therefore, setting expectations that observability provides insights into system health organization-wide is pivotal to prepare teams for collaborative efforts. Otherwise, there may be resistance to consolidating data into a centralized repository or working collectively to achieve optimal observability.

What Can You Track with Data Observability?

While the five pillars—freshness, distribution, volume, schema, and lineage—serve as fundamental elements for monitoring dataset health and performance, the world of possibilities with observability monitoring extends far beyond these metrics.

A noteworthy advantage offered by data observability platforms is the robust monitoring of data pipelines. By scrutinizing execution metadata and tracking delays in custom data pipelines, organizations can avert data downtime and sustain consistent operational health across their IT systems. Additional metrics such as duration, pipeline states, and retries provide deeper insights into the vitality and performance of data pipelines, ensuring continuous observability tailored to the organization’s requirements.

Delving further, column-level profiling and row-level validation contribute enhanced visibility into data performance across the entire system. Incorporating anomaly detection and business rule enforcement enables the identification of issues before they impact data quality. A statistics summary provides nuanced insights into the five pillars constituting the data observability framework.

Collectively, these observability metrics furnish a comprehensive understanding of the overall health of the system, potential incidents affecting specific elements, and the overarching quality of the data.

Data Observability vs. Data Quality

When delving into the comparison of data observability versus data quality, it’s paramount to acknowledge that effective observability hinges on robust data quality. The two concepts are intricately intertwined, with data quality serving as a linchpin for successful observability. Nevertheless, a meticulously crafted data observability framework, coupled with a supportive platform, can catalyze improvements in an organization’s data quality over the long term.

Challenges in testing data quality arise when confined to application observability, software observability, or product observability in isolation. Full-stack observability tools emerge as instrumental solutions, empowering organizations to elevate data quality across their entire IT infrastructure by elucidating how diverse systems leverage shared data. The imperative role of standardizing data becomes evident, underlining why standardization constitutes a foundational element within the data observability framework.

Despite the significance of standardization, a considerable proportion of data analysts—roughly one-third—report allocating over 40% of their time to standardizing data for analysis. Additionally, 57% of organizations grapple with the formidable challenge of transforming their data. This underscores the essential need for support in enhancing data quality. In this context, data observability aligns with DataOps principles by establishing standards to ensure data completeness, efficient delivery, and consistency. Acting as a proactive ally, data observability furnishes the context necessary for organizations to identify data errors, address pipeline issues, and pinpoint the sources of inconsistencies, fortifying data quality progressively.

Develop an Observability Strategy for Your Organization

Embarking on the journey of implementing data observability requires more than just selecting the right platform; it necessitates a comprehensive strategy that permeates the entire organizational fabric. While choosing a fitting data observability platform to amalgamate data across the entire tech stack is a pivotal starting point, the true essence of implementation extends beyond the tools employed. To ensure a seamless integration of data observability into workflows, organizations should lay the groundwork with a well-defined observability strategy.

Initiate this process by formulating a robust data observability strategy and framework, leveraging the insights provided in the “Data Observability Framework” section. Foremost among the priorities is instilling a culture of data-driven collaboration within the team. Contemplate how the adoption of a new observability tool and the convergence of data across teams may reshape collaborative dynamics among disparate units.

Proceed by establishing a standardization library that delineates the characteristics of exemplary telemetry data. This library becomes a cornerstone for team members to standardize metrics, logs, and traces seamlessly across diverse data sources such as lakes and warehouses, integrating them effortlessly into the chosen data observability tool. Concurrently defining data standards provides an opportune moment to institute governance rules, encompassing aspects like retention requirements and proactive data testing methods for identifying and purging flawed data.

In the final phase, carefully select an observability platform tailored to the organization’s needs and assimilate data sources into this unified ecosystem. Building new observability pipelines may be necessary to access metrics, logs, and traces, ensuring end-to-end visibility. As data sources are integrated, infuse relevant governance and data management rules, establishing correlations between tracked metrics and desired business outcomes. The observability platform becomes a dynamic space for issue detection and resolution, offering insights that pave the way for automating security and data management practices. Through this strategic approach, organizations can navigate the intricacies of data observability, fostering efficiency and resilience.

How to Choose the Right Data Observability Tool

Choosing an optimal data observability tool hinges on its ability to seamlessly perform three fundamental functions. Firstly, the tool must adeptly collect, review, sample, and process telemetry data from a diverse array of sources. Secondly, it should provide comprehensive monitoring capabilities across various facets, encompassing networks, infrastructure, servers, databases, cloud applications, and storage. Thirdly, the tool must serve as a centralized repository, facilitating efficient data retention and swift access while delivering robust data visualization.

While these core functionalities lay the foundation, the best-in-class observability tools extend beyond, incorporating advanced features to automate security, governance, and operational practices. Additionally, they offer scalable and cost-effective storage solutions to accommodate the escalating volumes of data, considering the current monthly growth rate of 63%. Evaluating these criteria is pivotal to selecting an observability tool that aligns with the ongoing needs and scalability of your business.

The initial step in choosing the right tool involves a meticulous examination of your existing IT architecture. Seek a tool that seamlessly integrates with each data source, capable of monitoring data at rest without necessitating extraction and tracking data throughout its lifecycle. Emphasize tools that embed Artificial Intelligence for IT Operations (AIOps) and intelligence, complemented by data visualization and analytics, ensuring a harmonious synergy between supporting business goals and addressing IT requirements.

Ultimately, the ideal data observability tool is contingent upon the unique characteristics of your IT architecture and observability engineering prerequisites. Opt for a tool that seamlessly integrates with existing workflows and tools, minimizing the upfront effort required for data standardization, mapping, and alterations to existing data pipelines. This strategic approach ensures a smooth and effective implementation experience tailored to the specific needs of your organization.

What Does the Future of Data Observability Look Like?

As the landscape of data continues to expand, the future of data observability emerges as a pivotal cornerstone for businesses across all scales. The burgeoning recognition of the advantages offered by data-driven decision-making necessitates a profound emphasis on high data quality. In this evolving scenario, the manual monitoring and management of data across diverse sources present significant risks to organizational health and decision-making processes. Consequently, data observability is poised to ascend as the predominant methodology, offering a comprehensive solution to manage vast data volumes, mitigate data silos, and enhance collaboration throughout organizations.

The escalating concern surrounding data security is poised to further propel the adoption of data observability. As privacy regulations amplify penalties for data mismanagement and companies handle increasingly sensitive data, the need for robust tracking and monitoring of data becomes imperative. Data observability serves as a key mechanism to scrutinize data movements, proactively address security gaps, and avert breaches. The integration of Artificial Intelligence for IT Operations (AIOps) with DataOps will gain prominence, especially in scenarios demanding swift responses to breach threats. By diminishing Mean Time to Detection (MTTD) and Mean Time to Resolution (MTTR), organizations can fortify data protection, foster effective cross-team collaboration, and streamline issue resolution processes.

Continual advancements in observability tools are anticipated, encompassing broader support for diverse data sources, increased automation of capabilities such as governance and data standardization, and the provision of real-time insights. These enhancements will empower organizations to navigate the evolving data landscape with agility and resilience, ensuring they remain at the forefront of innovation and strategic decision-making.

Transform Your Organization’s Monitoring Capabilities with Data Observability

Elevate your organization’s monitoring capabilities to new heights through the implementation of data observability. Managing substantial volumes of valuable data on a daily basis demands efficient tools to optimize resource utilization and time allocation. In the face of escalating data volumes, it becomes increasingly crucial for companies to seek solutions that streamline and automate end-to-end data management, catering to analytics, compliance, and security requirements.

For a seamless integration with numerous data observability tools, turn to strongDM. Our Infrastructure Access Platform complements your data observability platform, delivering exceptional monitoring and visibility features that illuminate how users access and interact with your data.

Paul Trance

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