Data Observability vs Data Monitoring: Understanding the Key Differences

Global data creation is projected to grow by over 180 zettabytes by the year 2025. This massive surge in data creation and its utility stands between what we call success and failure for businesses. Hence, the ability of businesses to consistently leverage data and navigate through this vast sea of data becomes crucial.

Organizations are deploying manpower and precious time to maintain the integrity and accuracy of data internally. This is where Data Observability and Data Monitoring step in and ensure the quality and reliability of large amounts of data. While both Data Observability and Data Monitoring play a role in maintaining the accuracy of enormous amounts of data, their approach and objectives are distinct. Let’s see how.

Data Monitoring and its Application

Data Monitoring gives you information about the usage pattern of applications and helps IT teams detect and resolve issues. However, you have to know which metrics to track for monitoring to function properly. 

To better understand Data Monitoring and its application, let’s take an example of a large online retailer receiving thousands of customer orders every minute. A data monitoring system continuously tracks various data related to ordering processes. It collects information such as the number of orders received, order processing time, inventory levels, and delivery status. 

1. Order Volume

While receiving large orders, the data monitoring tool will alert the delivery team whenever it detects anomalies in order volumes. If there is a sudden surge above a predefined order limit, the monitoring system will send an alert to the relevant IT team. This alert will indicate a potential spike in order demand. A retailer can then plan out the allocation of additional resources to manage the increased order volume and the inventory accordingly.

2. Order Processing Time

Along similar lines, if the order processing time goes past a specified limit, the Data Monitoring system will trigger an alert. This alert will indicate to the operations team to find out the reasons behind delays and resolve roadblocks in the entire ordering process.

3. Order Delivery Status

Status of deliveries can also be tracked using the Data Monitoring system. This will ensure that delays or issues during order shipping can be resolved quickly. The monitoring system will alert the delivery team and allow them to contact customers who are facing delayed deliveries and offer timely updates about the same.

From small-scale to large-scale enterprises, agile Data Monitoring systems can help such businesses respond to and resolve issues quickly and efficiently. This will improve user experience and help companies maintain their competitive edge.

‍

Data Observability and its Application

Data Observability solutions completely customize your data engineering stack, ensuring data security, minimizing risk, and saving time. It focuses on the performance and health of data sets. Such systems offer holistic insights into data infrastructures and their behavior. This allows companies to identify and rectify issues proactively. Observability uses custom frameworks and sophisticated ML models that help identify anomalies, errors, and inconsistencies using historical data. 

A data observability system conducts anomaly detection to check a platform's health and performance comprehensively. To understand the working process of Data Observability tools and systems, think about a cloud-based software-as-a-service (SaaS) platform. The SaaS platform offers project management tools for teams and organizations. The platform collects and processes large amounts of data related to user activities, project progress, and task assignments.

The SaaS company incorporates a data observability system to boost its reliability and performance. This is how a Data Observability system can help a SaaS-based organization get insights about its platform’s health and performance:

1. Error Detection

Whenever there is a sudden rise in response times or high error rates, the system alerts the operations team, showing potential performance issues. The operations team can then coordinate with the IT team to investigate and resolve issues promptly,  ensuring uninterrupted service for users.

2. Data Security

The data observability system can also detect inconsistencies or changes in user behavior patterns. If a user account shows unusual activity, such as excessive requests or suspicious access patterns, the system can raise alerts for potential security breaches or account compromises.

‍

Differences Between Data Observability and Data Monitoring

Both Data Monitoring and Data Observability play vital roles in ensuring the integrity, reliability, and performance of data systems. While data monitoring focuses on real-time tracking of data quality, data observability takes a more holistic approach by incorporating system-level metrics. Here are some of the key differences between Data Observability and Data Monitoring:

‍

‍

Final Thoughts

Businesses should leverage both Data Monitoring and Data Observability systems to maximize the value of their data. By integrating robust monitoring and observability tools, businesses can gain insights about data accuracy, consistency, and visibility. In the data-driven landscape, where data is the fundamental driving factor of decision-making, the combination of Data Monitoring and Data Observability empowers organizations to build resilient and reliable data pipelines, leading to more accurate insights and informed decision-making processes.