Data Observability: The Next Frontier of Data Engineering

The term data observability refers to an organization’s capabilities to fully understand the health and state of data in their systems. It is an umbrella term that encompasses a set of workflows and technologies that enable organizations to discover, debug, and fix data issues in real-time, thanks to big picture visibility into all their data - which may be scattered across various programs, platforms, and servers.

For true data observability, businesses must make data sets and their metrics visible to the right stakeholders, in order to further the goal of maintaining data quality and reducing downtime. As organizations increasingly rely on data for virtually every aspect of their operations, ensuring data quality, reducing errors and data gaps, and closely monitoring data pipelines have become mission critical.

Businesses pursuing data observability should leverage DevOps observability best practices to eliminate data downtime, automate monitoring, alerting, and triaging to identify and evaluate data quality and discoverability issues. Data observability also covers other concepts such as data lineage, metadata management, and data governance.

Here’s how pipeline monitoring and observability for your data can help your business streamline its processes.

Improved data quality and consistency

Poor quality data is a major challenge for many organizations, and data observability can help dramatically improve it. With automated monitoring, alerting, and triaging, organizations can identify and evaluate data quality issues, such as missing or inaccurate data, before they negatively impact your business and its processes.

Data observability means better data consistency, including that the same data is used across all departments within your organization. It also reduces the risks of errors that are caused by inconsistent, inaccurate and incomplete data.

Increased operational efficiency

By providing timely and uninterrupted access to data, data observability boosts operational efficiency. Armed with the ability to detect and resolve data problems before they negatively affect your organization, your business can be sure that high-quality data is delivered on time for your workloads and processes.

This may look different depending on your business’ unique needs - for example, efficient monitoring might mean instant notifications around inconsistencies for some organizations, while others might prioritize quicker access to data that can be used by product teams, or analyses that are swiftly generated based on customer feedback.

Observability technology can lead to faster time to market, improved productivity, and better customer satisfaction. It’s important to note that data observability provides efficient monitoring and alerting, which can scale up in alignment with your growing business.

Streamlined data management

Data observability can also help organizations manage their data more effectively. By providing automated monitoring, root cause analysis, data lineage, and data health insights, data observability tools can detect, resolve, and prevent data anomalies.

This leads to healthier data pipelines, more productive teams, and happier customers. By improving data management, your organization can be certain that data is used in the right context and is correctly understood throughout its lifecycle.

Proactive issue detection and prevention

Another major benefit of data observability is the ability to detect, predict, and prevent issues before they occur. Leveraging a robust data observability platform that uses ML (Machine Learning) models and anomaly detection techniques, your organization can automatically learn their environment and data and minimize false positives.

A holistic view of data and the potential impact from any particular issue can lead to smarter decision-making, as issues can be resolved before they impact your organization. By embracing a proactive strategy regarding issue detection and prevention, organizations can avoid costly downtime and minimize the impact of potential problems.

Some of the key aspects of data health that can be tracked using data observability include:

Lineage

Data lineage tracking monitors the data's journey, from its origin to its destination, including all the transformations, changes, and metadata associated with that data. It helps ensure the data's integrity, provenance, and compliance with regulations, such as GDPR or CCPA.

Freshness

Freshness measures how up-to-date the data tables are and the cadence at which they're updated. It's essential to know how current your data is in order to ensure the accuracy of the analyses and decisions that are made based on that data.

Data quality

Monitoring data quality is a crucial aspect of data observability. It involves regularly checking data against predefined data quality rules and presenting the results and trends. Patterns, such as the quality of the data in your pipeline consistently worsening, can alert you to problems which need to be remediated.

Schema

Schema tracking monitors the changes in the data's structure, such as data type, field names, and their relations. It helps ensure that the data is correctly structured, and schema changes don't break the system or affect downstream processes.

Volume

Data observability can track the quantity of data being processed, stored, and transferred within your organization. It helps identify bottlenecks in data pipelines and capacity planning issues that can affect the system's overall performance.

By tracking these fundamental elements of data observability, data teams can gain valuable insights into the quality, reliability, and health of their data systems. These insights can help prevent data issues before they occur, improve data accessibility and trust, and ensure that data is delivered timely and efficiently for key business decisions. Data observability is critical for managing and scaling data platforms in today's data-driven business landscape.

What’s the connection?

Data observability and data governance are two critical concepts that play important roles in ensuring the accuracy, reliability, and efficiency of data-driven systems. While they are distinct and separate concepts, there is a close connection between the two.

Data governance is the process of managing the availability, usability, integrity, and security of data used in an organization. It involves establishing policies and guidelines that ensure data is collected, stored, processed, and shared in a secure and compliant manner. The goal of data governance is to ensure that data is accurate and trustworthy for decision makers. It is essential to maintaining the quality of data across the entire data pipeline.

The term data observability applies to the practice of monitoring, measuring, and analyzing data in real-time to understand its health and state. Data observability aims to ensure that data is always available, complete, and up-to-date.

For a robust and effective data governance strategy, businesses must invest in data observability. This is for a number of reasons. Observability provides data governance teams with real-time insights into the health and state of data, enabling them to detect and address issues before they become significant challenges. This makes data governance more proactive rather than reactive, which helps to reduce costs and improve efficiency.

Observability provides a way for data governance teams to monitor the quality and accuracy of data at any given moment, so that the teams can check that it meets your organization’s defined standards and rules. This helps to prevent data quality issues from entering the data pipeline and affecting downstream processes.

Simultaneously, data governance provides the framework and guidelines that enable observability to be applied consistently across the entire data pipeline. Data governance provides the necessary documentation and metadata that enables observability to be performed effectively. This includes data lineage, data definitions, and data mapping, which are critical for understanding the context of data and identifying potential issues.

Data governance teams need data observability in order to gain critical feedback on the effectiveness of data governance practices, to identify areas for improvement, and to adjust their policies and guidelines accordingly

To learn more about what the future holds for data observability, as well as how you can leverage the power of data observability to streamline your business practices and ensure smarter decision-making, get in touch with KPMG Edge Data team today.