What is data observability in a cloud environment?

Data observability involves the continuous monitoring, management and maintenance of data to ensure quality, availability and reliability. It enables organizations to gauge the quality, completeness, accuracy and usefulness of their data within their data ecosystem and resolve issues as they arise.

In cloud computing, data observability uses tools to aggregate, correlate and analyze a steady flow of performance data generated by various applications, hardware, and network components. Data observability in the cloud helps businesses create better customer experiences, enhances network security, improves data quality and helps technicians spot problems before they occur. As a result, businesses benefit from reduced downtime, lower costs, and improved customer satisfaction.

Data observability focuses on gaining insights into the behavior, quality, and performance of data in real-time, allowing for proactive identification and resolution of issues. On the other hand, data monitoring involves tracking and analyzing data metrics and events to ensure they meet predefined thresholds and standards, often in a reactive manner.

Data observability is a natural evolution of Application Performance Monitoring (APM) and Network Performance Management (NPM). It builds on monitoring and differs from traditional monitoring because it's designed to explain the "unknown unknowns" — the detection of unexpected problems. Traditional monitoring systems were effective at identifying and mitigating these common failure scenarios in data management and quality. However, as the demand for systems capable of managing larger and continuous data streams grew, traditional monitoring methods became insufficient for providing timely, real-time feedback.

Data observability is designed to create actionable outputs in unexpected situations where systems are changing and adapting on the fly. It accomplishes this by providing tools to explore and debug. Data observability can help detect problems or outages ahead of time to mitigate the risk of significant downtime that could result in lost revenue or compromise brand credibility.

There are five pillars of data observability, according to Monte Carlo CEO Barr Moses and other experts, that are fundamental to establishing effective practices within an organization. These pillars are:

1. Freshness: Freshness refers to how up to date data tables are and how often they are updated. Ensuring freshness is important for informed decision making as it directly impacts the accuracy and relevance of the data being used. Having outdated or stale data can lead to misguided decisions based on obsolete information, potentially resulting in missed opportunities or incorrect assessments.
2. Distribution: Data distribution refers to how data is spread out or distributed across various locations, systems or storage mediums within an organization's data infrastructure. This includes tracking data movement, replication and synchronization processes to ensure data consistency, availability and reliability. By monitoring data distribution, organizations can identify potential bottlenecks, latency issues or data replication problems that may impact data accessibility, performance or reliability. 
3. Volume: Volume refers to the quantity or extent of data contained within tables or datasets. It measures the completeness of data tables and evaluates the robustness of data sources within an organization's data ecosystem.This enables teams to monitor capacity and cost to scale efficiently. By understanding the volume of data, organizations can monitor resource utilization, anticipate capacity requirements and ensure that infrastructure resources are adequately provisioned to handle current and future data loads.
4. Schema: Schema refers to the structural framework that defines the organization, structure and relationships of data within a database or data storage system. It is essentially the organization of your data. Organized data is easier for users to understand and interpret, enhancing data usability and reliability. With a clear schema, users can quickly locate and leverage data for analysis and decision-making, improving overall efficiency and effectiveness. Frequent changes to the schema indicates broken data. Data observability monitors who makes these changes and how these changes are made
5. Lineage: Data lineage refers to the end-to-end journey or lifecycle of data as it moves through various stages of collection, processing, transformation and consumption within an organization's data ecosystem. Data lineage determines which upstream sources and downstream users were affected. It collects information about the data (or metadata) associated with specific data tables, providing information on governance and business and technical guidelines. Lineage is a single source of truth for every consumer.

Data observability improves data performance and reliability, enhances security and governance, optimizes cost for increased ROI, and helps to prevent pipeline breakage.

These benefits are the result of organizations understanding the harmful impact that bad data can have on business outcomes. Business leaders are increasingly aware that bad data affects the bottom line. Gartner estimates that bad data costs businesses $12.9 million a year. The sooner a business can implement data observability practices, the sooner the effects of bad data are mitigated.

Data observability improves performance monitoring and troubleshooting in several ways. It discovers and addresses the unexpected issues. Observability uncovers them, links the relationship to production issues and identifies the root causes to help fix the problem.

With observability, effective monitoring is baked into the first stages of software development. This enables teams to identify and repair issues in any new code before it erodes the customer experience. It's also possible to use tools to scale observability automatically and enable information gathering from the start-up to the shutdown of any system.

Data observability also improves resource optimization for warehouse provisioning and scheduling by boosting efficiency and performance while reducing costs. It does this by analyzing usage trends and peaks, which enables organizations to better forecast resource needs and make adjustments before reaching a critical storage situation.

Administrators can enact load-balancing strategies that distribute the workload evenly among accessible resources. All these measures provide an essential benefit for businesses using data observability: reduced costs due to fewer resources and storage space.

Data observability helps organizations detect security threats and maintain data privacy and governance standards through several measures. Data observability tools constantly monitor sudden spikes in data retrieval or access from unusual locations. The tools alert security teams when these patterns deviate from the norm, as they suggest unauthorized access or data breaches. These observability tools also ensure compliance with their governance and privacy standards. They track and log every data transaction and modification, creating exhaustive audit trails and verifiable records that show the organization is following data handling standards.

Observability tools apply privacy rules by automatically hiding sensitive data when unauthorized users or systems attempt to access it. Data observability helps organizations define who has access to essential data based on their access level. It gives organizations a complete view of their data environment by integrating security insights from numerous platforms and systems. This holistic approach is critical to recognizing complex security threats throughout their data ecosystem.

As noted above, bad data can directly affect an organization's revenue if data teams have trouble seeing where data is being used and how they're being charged. Significant cost overruns and misallocation of charges are a genuine concern. Labor costs involved with fixing the problem and the operational cost of poor data quality can result in significant revenue loss. 

Effective data observability solutions provide granular visibility into overall data investments. This allows organizations to feel confident they are using their data effectively. 

A smart way to maximize ROI is using a Snowflake data warehouse. Businesses can store and analyze large volumes of data in a secure, scalable and user-friendly environment. Capital One's Slingshot enables data observability by providing enhanced Snowflake data visibility and insights. By monitoring spend at a more granular level with greater predictability, businesses can improve performance and reduce waste.

Pipeline breakage, the interruption or failure of data flow within an organization's data pipelines, can have detrimental effects on business operations, decision-making and overall data reliability. These breaks can occur due to various factors such as data inconsistencies, schema changes, resource limitations or unexpected system failures. 

However, data observability plays a crucial role in preventing pipeline breakage by providing visibility and monitoring across the data lifecycle. By leveraging tools and practices associated with data observability, organizations can proactively detect anomalies, monitor data quality, track data lineage and ensure adherence to data schema standards. This enables early identification of potential issues or bottlenecks within data pipelines, allowing for prompt remediation actions to be taken before they escalate into full-blown pipeline breaks. 

Ultimately, data observability helps organizations maintain the reliability, integrity and continuous operation of their data pipelines, supporting informed decision-making and driving business success.

Organizations need to build and maintain a successful data observability strategy to gain awareness of the health of their systems and enable effective use of their data. 

The following best practices can help organizations create an effective data observability strategy:

• Identify goals: Determine your primary business and technical objectives that an observability pipeline can support. Include decisions about crucial metrics and events to track and log.

• Data aggregation and storage: Find effective data storage solutions that can scale and deal with ingestion rates and query loads — centralized data from various sources, such as applications or databases — to expedite analysis.

• Build a pipeline: Pipeline design is vital. It should effectively process, filter and collect data from various locations in a system and forward it to your storage facility and analytical tools.

• Dashboards: Create dashboards to provide updates on key metrics and trends. They must be customizable so different shareholders can view the data that's relevant to their roles.

• Alerts: Set thresholds for essential metrics and create alerts to notify the appropriate teams when these thresholds are reached. The alerts should provide enough context to help rapidly identify and address problems.

Capital One Slingshot supports data observability best practices with dashboards that provide granular insights into cost, performance and usage of Snowflake, enabling teams to manage its data efficiently. 

Proactive alerts for cost spikes and usage anomalies also empower teams to address issues promptly. And pre-populated warehouse scheduling templates and approval workflows ensure efficient resource allocation while streamlining governance.

Organizations should ensure the pipeline architecture's observability, tool configurations and operational procedures are documented. Train teams on how to use observability tools correctly and effectively, interpret the data they provide and respond quickly to alerts.

Organizations should train teams to handle and retain data in compliance with relevant laws and regulations and ensure good governance of that data. Apply security steps to guard data privacy and integrity across the observability pipeline.

While data observability can significantly enhance an organization's ability to deal with problems quickly and reduce costs, it's not without challenges. Organizations might face difficulties when managing the complexity of data environments, integrating legacy systems, or training teams to implement and maintain best data observability practices.

Complexity of data environments

Some of these challenges are technical issues, such as the complexity of data systems. Present-day data environments usually include cloud services, legacy systems and database platforms. Data teams can struggle to ensure observability while working within such a diverse technological landscape. 

Another challenge can be finding the right tools to manage increased data volumes without sacrificing performance or incurring increased costs. The volume of data ingested can be overwhelming, requiring thoughtful management, and validates the need for mature data observability.

Integrating legacy systems

Another challenge is integrating older systems that aren't designed for observability with newer observability tools. It often requires custom solutions or middleware to work effectively. Cultural and organizational barriers present another challenge. Data observability provides the best results when teams in different units collaborate. Organizational silos often undermine this collaboration, which leads to holes in data observability.

Lack of expertise

Another constant challenge is a lack of expertise in data observability teams. Not everyone has the skills needed to implement and maintain an observability system. It's essential to hire individuals who have the needed expertise or train current staff to acquire those skills.

Data observability is growing fast, but it's still evolving, adapting to the changing landscape of data management.

While Gartner may not yet have a category for data observability, it was mentioned in their 2022 Data Management Hype Circle. Some potential developments in the future of data observability include:

• Increased automation: Expect to see greater automation in data observability processes, including automated anomaly detection, remediation and optimization. Automation will streamline data monitoring and analysis, enabling organizations to identify and address issues more efficiently.

• Integration with machine learning: Machine learning algorithms will play a larger role in data observability, enhancing capabilities such as anomaly detection, predictive analytics and intelligent data governance. Integration with machine learning technologies will enable more proactive and predictive approaches to data management.

• Enhanced focus on data lineage: Data lineage will receive heightened attention, with organizations placing greater emphasis on understanding and tracking the end-to-end journey of data. Comprehensive data lineage capabilities will become essential for ensuring data quality, governance, and regulatory requirements.

• Expansion of stakeholder interest: Data observability will attract interest from a broader range of stakeholders, including business users, data scientists and compliance officers. As data becomes increasingly central to business operations, stakeholders from across the organization will recognize the importance of data observability for informed decision-making and risk management.

• Adoption of modern data architectures: Organizations will continue to adopt modern data architectures, such as cloud-native data platforms and data lakes, to support their data observability initiatives. These architectures provide the scalability, agility and flexibility required to manage and analyze vast volumes of data effectively.

At the heart of the modern data stack, data observability is catalyzing a shift towards self-service analytics and fostering collaborative efforts across integrated teams. By safeguarding against bad data entering into data warehouses, data observability acts as a gatekeeper, ensuring the integrity and reliability of insights derived from data analysis.

Instead of relying on legacy monitoring tools, many modern organizations are exploring enterprise data observability platforms constructed from modern data architectures. With these foundations in place, organizations can extract maximum value from their data assets while fostering a culture of data-driven decision-making.