The Beginner’s Guide to Data Observability

It comes down to trust. Organizations depend on accurate data to keep key operations running smoothly and inform critical business decisions. As data changes and issues arise, the trust and confidence for accurate data can suffer, so taking a proactive approach is important to empower effective business decision-making.
This post will explain the growing practice of Data Observability and how you can use foundational techniques and tools to ensure trustable data throughout your enterprise or IT operation. This approach provides impact as well in the data governance area with policy, business, and technical payoffs as data grows within an organization. It will also discuss key features to look for in Data Observability tools to help you find the right solution for your system.

What Is Data Observability?

One view of Data Observability on a holistic basis is to increase visibility and monitoring across the data workflow — from ingest to transformation to consumption. Both data governance managers and data scientists can observe and monitor the state and quality of their data at all levels. But there is more to consider in improving dynamic trust along the way.

Another deeper view of Data Observability is similar to the DevOps concept of software Observability for processes that prevent data downtime. The key aspects of ensuring better data include:

• Consistent monitoring of data across the workflow for incidents or changes
• Identifying and tracking data using a Mean Time To Detect (MTTD) approach
• Resolving data errors and incidents on a Mean Time To Repair (MTTR) basis

This enables DataOps and DevOps teams to use a continuous integration and development (CI/CD) approach to data and software engineering and provide a Return on Investment (ROI) to the cost of doing so. The payoff has deep impact, and the tools can often automate each step through machine learning to discover and assess data problems that may otherwise remain hidden.

The 5 Pillars of Data Observability

Five principal areas of Data Observability form the framework of advanced data monitoring and mitigation of data downtime that work for both data governance and data engineering teams. Addressing each of these Data Observability pillars is critical to understanding and maintaining the health of enterprise data systems — let us take a look.

• Freshness: Freshness refers to the timeliness of your data — how up to date your data is as well as how often it changes. This pillar is critical for decision-making because stale data loses value over time and its impact on business decisions made using older data sets may not be trusted. Keeping data fresh requires monitoring your system for inconsistencies with your data timeline and routinely updating your data across the enterprise.
• Distribution: Distribution refers to the amount of variance present within your data system, which can reveal important insights into the accuracy of your data. It is a function that includes all your data's values to determine whether your data is within an acceptable range. Extensive variation from these values can indicate the presence of inaccurate or otherwise damaged data, which must be resolved as quickly as possible. Tracking the distribution of your data values helps you find inconsistencies and avoid errors being introduced into your system.
• Volume: Volume refers to the quantity of data in your system, a critical metric for ensuring your data meets expected thresholds and remains within your defined limits. Monitoring volume can also help you determine if it is time to add more storage capacity. The term also refers to the completeness of your data tables, which is helpful for determining the health of your data sources. For example, if you notice fifty thousand rows have suddenly shrunk to five thousand rows, you will know right away that something is wrong in your pipeline.
• Schema: As your organization grows and adds new features to your application database, your data organization strategy — also called your schema — is naturally going to adapt to suit your new needs. However, poorly managed schema changes can lead to data quality issues and downtime. This pillar requires consistently monitoring changes made to your data and performing routine audits to reveal problems within your data ecosystem. For example, you might run periodic data audits every few weeks. Doing so ensures key database schema such as data fields, tables, names, and columns are accurate and up to date.
• Lineage: Lineage combines all four pillars of Data Observability into one comprehensive view. By collecting important metadata that connects back to specific data tables, you can create a network to trace broken data back to its source. So if you come across a broken dataset, you can follow its lineage upstream to determine the original issue. Once you know the root of the problem, you can solve it quickly and efficiently.

The practical implementation of Data Observability is that your organization is running an efficient operation where data can be trusted. In addition, to create an ROI on the cost of the people and time to maintain this trust, the pillars also provide metrics for Data Observability performance, essential to gain an accurate vision of your organization's data at any time.

For example, a full view of data lineage is important for understanding dependencies between pieces of data within your system. Without that lineage, you will have an incomplete picture of how data issues can relate to each other, which can make resolving data quality issues much more difficult. A Data Observability solution incorporates this necessity and ensures data traceability both upstream and downstream.

Beyond the Data Observability Framework

Data Observability goes beyond simply testing and monitoring. It is the framework that makes all of these concepts possible and lets you trust fully in your organization's data.

Beyond the framework of Data Observability, there are pragmatic implementations that comprise the day-to-day operations of an effective data trust team. Let us review a few key areas.

Monitoring

Many people use the terms, “Data Observability” and “monitoring” interchangeably, but the difference revolves around what more you must do beyond monitoring, by alerting the data team on important data quality areas to be addressed — again, all to ensure trust in data.

Monitoring issues alerts based on pre-defined parameters, which represent data in aggregates and averages. Complete visibility into your data assets and attributes is necessary for successfully monitoring the health of your data ecosystem. There are two types of data quality issues:

• Known unknowns: These are issues you can predict because you know what information you are missing.
• Unknown unknowns: These issues are problematic because you do not know what information is missing — as a result, you are unable to predict the potential problems that can arise. For example, a critical application dashboard might stop updating and go unnoticed until someone accesses it and notices the errors.

Before you can successfully establish a monitoring scheme for a data ecosystem, you need to have full visibility into all your data assets, as well as the workflow and business rules that manage them across the pipeline and storage systems. In addition, Data Observability can help you provide visibility into “unknown unknowns,” letting you gain a complete understanding of your data assets and attributes.

Monitoring is also a key feature of Data Observability tools — with a monitoring dashboard, you can get a high-level view of your entire pipeline or data system at a glance. A user-friendly dashboard is a quick and effortless way to provide comprehensive data to anyone in your organization, from your data engineers to your business executives.

Testing

Data engineers utilize routine testing to detect and prevent potential data issues all the way from ingest to downstream consumption. However, with the sheer volume of data modern companies ingest on a daily basis, traditional testing methods are insufficient for identifying a single point of failure.

As with monitoring, unknown unknowns can be problematic for data testing. Utilizing Data Observability at scale can address the gaps caused by unknown unknowns that may be impossible to resolve through other tests. Essentially, Observability of data is more effective than data testing alone because:

• It provides end-to-end coverage of your pipeline.
• It is scalable to your organization's needs.
• It allows you to track lineage, which is helpful for impact analysis.

Data Quality and Reliability

Data reliability and quality are critical for making data-driven business decisions. Reliable data eliminates the guesswork involved in making trustworthy analyses and insights, which is why it is one of the most important characteristics of healthy data.

Bad data causes data downtime, which is incredibly damaging to business operations. However, this simplistic understanding can limit data teams in their ability to evaluate data reliability and quality.

Data health is measured, often as a percentage of how good my data is — 60%, 70%, 80% — and then using strategies to increase the value and trust of the data sets based on countless needs. So, Data Observability can facilitate data quality by enabling data teams to examine the big picture in addition to what is already in their silo.

There are six key dimensions to data quality — beyond this blog — but to indicate they are based upon uniqueness, accuracy, completeness, timeliness, validity, and consistency. Because Data Observability works in conjunction with data quality, it is important that organizations improve every aspect of their data to ensure high quality and reduce data downtime.

Data Governance

Data Observability is critical to establishing a data governance framework, a key part of creating a truly data-driven organization. The four pillars of data governance include:

• Define specific use cases: Identify how data governance impacts your organization through cost, revenue, and risk. This step lets you include stakeholders in developing your framework.
• Quantify framework value: The impact of data governance implementation should be quantifiable for each use case and the organization as a whole. Identify which KPIs you should monitor and run routine reports to demonstrate the value of your framework to your stakeholders. Data Observability tools can help improve those metrics by eliminating data silos throughout your pipeline, leading to more productive teams and healthier pipelines.
• Enhance data capabilities: Improve data value for your users and address individual data usage needs. Data Observability is key to maximizing the insights you can glean from your available data by enabling collaboration across your organization, identifying ad resolving data issues and letting you better understand defining data characteristics such as content, classification, and origin.
• Establish a scalable delivery model: Once you have developed your initial use case, you can establish your data governance framework as a scalable service — as you continue to add use cases, you will see increases in ROI and new opportunities for organization-wide use.

Without Data Observability, it is difficult to get a data governance framework up and running, especially if you plan to implement complex applications in the future.

Key Features of a Data Observability Tool

The following features are critical to achieving Data Observability:

• Monitoring: Your Data Observability software suite should monitor your data both at rest and in motion for a high-level view of your data's health. For example, monitoring data at rest ensures datasets arrive on time and teams can update datasets according to established intervals. Similarly, monitoring datasets in motion ensures they successfully move through the pipeline and are tracked all along the way to validate source to target.
• Alerting: You want to be able to address data issues as soon as possible to prevent problems from escalating. Your software suite should include an automated alerting feature that kicks in the second it detects any abnormalities or under pre-set business rules.
• Tracking: Your software suite should be able to set and track specific data events, providing you with rich context to enable efficient troubleshooting when problems arise.
• Comparisons: By monitoring and logging the state of your data system over time, you contextualize your data, making it easier to detect and respond to anomalies.
• Analysis: Your Data Observability tool should provide automated anomaly detection that intuitively adapts to your organization's data pipeline. With effective analyses, you will gain powerful insights to enhance business strategies and decisions.
• Logging: Logging features enable quicker resolution by recording data events in a standardized format.
• SLA tracking: Being able to measure your overall data quality and pipeline metadata against the standards set out in your Service Level Agreements (SLA) is key to ensuring customer satisfaction and evaluating your system's health.

At the end of the day, an effective Data Observability software suite will integrate into your existing modern data workflow with minimal need for manual configurations. It should also utilize machine learning to automatically map your environment and data, providing a holistic view of your data and the potential impact it may experience from specific issues.

Why Is Data Observability Important?

As businesses across various industries continue to work towards digital transformation, their data becomes increasingly important in their day-to-day operations. These data ecosystems consequently increase in complexity — and so do the risks of data quality issues that could cause costly business mistakes.

Data Observability lets you thoroughly understand your organization's data pipelines so you can troubleshoot data problems in almost any scenario, even in highly complex systems. When you pair a practical data strategy with effective Observability tools, you can even prevent many of these issues from arising in the first place.

Essentially, Data Observability increases organizational confidence in your data's accuracy so you can make well-informed business decisions and maintain the trust your customers have placed in you.

Why Observability Is Crucial for Both DataOps and DevOps Teams

The main issue for data organizations is that they lack true visibility into their data, which can negatively impact important business decisions.

For example, data silos make exploratory data analysis (EDA) incredibly difficult by interfering with your team's ability to create complete visual representations such as graphs and charts, which are essential to gathering quality insights.

Data Observability makes EDA possible by broadening the scope of what your data teams can see — with a complete view of data across your organization, DevOps and DataOps teams can put their data in context, leading to more informed business processes.

Why Do Data Organizations Need Data Observability?

All organizations need standardized, readily available data to keep key business processes running smoothly. Data Observability lets organizations discover and address data issues in real-time, preventing these problems from traveling further down the pipeline and affecting business processes. The process of data validation manages a source to target confidence.

Data-intensive applications rely on accurate, high-quality data to function properly. For example, machine learning is a highly data-intensive application that relies on AI Observability to keep stakeholders updated on the health and quality of the system's model, data, and predictions. AI Observability relies on end-to-end Data Observability to work properly because it enables visibility into every stage of the data pipeline, helping uncover common ML problems like stale models, data drift, and changes in data quality.

While a pure monitoring system is limited to checking for your known unknowns, a Data Observability software suite prepares you for unexpected abnormalities. This ability helps data teams meet rigorous SLAs.

How Does Data Downtime Affect Organizations?

Data downtime refers to periods of incompleteness and inaccuracy in your data. Many factors, such as bug fixes or sudden schema changes, can cause data downtime.

These periods can be detrimental to your organization. According to the ITIC annual Hourly Cost of Downtime Survey for 2021, 44% of enterprise organizations estimate that every hour of data downtime costs them more than $1 million.

The extent of data downtime is directly related to the complexity of your overall data system — as your organization grows and adds more applications, data downtime is likely to occur more frequently. However, because data teams often approach data quality and lineage issues as they arise, data downtime remains problematic.

Data Observability provides the visibility you need to catch data issues early, letting you minimize data downtime and resolve the problem's root cause before it causes further issues. In other words, Data Observability is an investment that both keeps your data up and running in the present and prevents issues from occurring in the future.

What Is the Process of Implementing a Data Observability Platform?

A unified Data Observability software suite is an essential way to achieve full data trust in your organization, but you need to build the proper infrastructure before you can begin using it. A sound framework is key for adding this technology to your current stack.

The following three components are absolutely necessary for developing a solid Data Observability framework:

• DataOps culture: Everyone in your organization, including leadership, must buy into the idea of a DataOps program before you can even consider implementing a Data Observability platform. Use information such as the future DataOps market size, projected ROI, and key benefits of Data Observability to justify the investment.
• Standardized data platform: Building an organization-wide DataOps culture creates the foundation you need to begin developing your Data Observability framework. Standardized libraries for data and API management enables effective communication between your teams by allowing them to use the same language.
• Unified Data Observability platform: Once you have the proper infrastructure in place to support your Data Observability schema, choose a unified platform that will let your whole organization access the state of your data system. The ideal platform for your organization will include all the features needed to create a centralized metadata archive, which data teams can use to gain end-to-end pipeline visibility.

By following these steps, you create an environment that can support your chosen Observability platform.

How RightData Supports Data Observability Efforts

If you are looking for a software suite that can make Data Observability more attainable for your organization, RightData's RDt software is for you.

RDt is a scalable, efficient software suite that lets you and your stakeholders identify and solve data quality issues. RDt automates and expands the internal data auditing process, increasing confidence in your organization's readiness for external audits.

With RDt, you can uncover issues early on in data production — this proactive approach helps you prevent compliance issues and reputational damages, thus minimizing financial risk. Plus, by accelerating these test cycles and facilitating CI/CD processes, RDt reduces the cost of delivery.

Some other key features include:

• Dataset analysis
• Data reconciliation
• Data and dataset validation
• Dataset comparisons
• Administration and customer management service (CMS) capabilities
• Reporting and collaboration

In addition, RightData's modern data integration platform, called Dextrus, the data quality and Observability features work completely with an entire workflow. You can complete your data pipeline with Dextrus, our comprehensive, high-performance data platform. Dextrus lets you build both batch and real-time streaming data pipelines in just minutes — plus, it integrates the analytics into the ETL pipeline building phase, letting you analyze data at any stage of integration.

With Dextrus, you can create and maintain an accessible cloud data repository for both cold and warm data to fulfill any of your organization's data analytics needs.

Noteworthy Dextrus features include:

• Intuitive self-service dashboard
• Code-free operation
• Low learning curve
• Exceptionally low data latency
• High throughput
• Quick dataset insights
• Easy, streamlined data preparation
• Query-based and log-based CDC

Combining RDt and Dextrus lets you harness the power of big data by enhancing organization-wide visibility into your pipeline. Build an efficient pipeline using Dextrus and test it for faulty data using RDt — the solution could not be simpler.

Make a Difference in Data Observability With RightData

At RightData, we strive to help data engineers improve their organization's data strategies by providing high-quality tools for gaining better insights into critical business data.

In addition, we recognize the evolving role of data governance professionals who bridge data quality with the pillars of Data Observability for impact on data trust across the enterprise. Their role is critical to success because policy, technology, and business decision-making all must work together. If you are looking for a DataOps platform that covers the entire data pipeline process from creation to testing, we are here to help.  Schedule a live RDt demo. One of our expert team members will get back to you as soon as possible.