What Is Data Observability, and How Will It Help You Succeed?

Data pipelines are complex and prone to data loss, inconsistency and slow processing. Watch this webinar to learn how data observability can be a game-changer for data-driven organizations.  

More than ever, businesses need reliable and available data. However, an ever-increasing volume and complexity of data requires a new approach to data quality. This is where data observability comes in.

A set of automated processes that help ensure that data is available, correct and up to date, data observability can help you address a range of issues. Whether you’re trying to address concerns that have resulted from the rapid growth of the data community or ward off reputational risk and its accompanying costs, data observability can help.

After all, any disruption or bottleneck in the data flow can result in a lack of trust in the data and require expensive remedial steps. And this can reduce the organization’s ability to:

• Identify opportunities
• Mitigate risks
• Reduce (IT) costs
• Create a differentiated customer experience

Job-monitoring capabilities focus on data pipelines and execution of data processes (movement, data warehouse loads). Typically, they report only on the overall execution status (success or fail). They do not analyze the "contents" of the job or provide answers to questions such as:

• Were all the rows transferred?
• Has the quality profile of the data changed?
• Has any new confidential data been exposed?
• Did the job complete within the expected SLA timeframes?

Without this understanding, you do not know why a predictive model produced a given result. You have no insight as to why reports provide conflicting information, and you have no visibility into why other downstream processes failed. And that is why organizations are looking beyond job execution statistics. Data observability offers a more proactive approach to detecting, troubleshooting and resolving problems with the actual data and the data pipelines that manage and transform it.

Data observability helps to ensure that data pipelines — the critical part of any data-driven organization responsible for collecting, processing and delivering data to stakeholders — are reliable, efficient and of high quality.

As mentioned above, data pipelines are complex systems prone to data loss, duplication, inconsistency and slow processing times. Another area where the increasing complexity and scale of modern data pipelines have become especially critical for organizations is FinOps. Here, organizations want to manage and optimize the financial aspects of cloud computing with better cost control, resource management and budget management.

Data observability addresses these issues by observing data in real time. This allows you to track data lineage, create visualizations to provide insights, set up alerts, establish and enforce data governance policies, catalog data assets, and leverage machine learning and AI algorithms.

With data observability, organizations can quickly identify and resolve data and data quality issues to help ensure that the organization can maintain its standards and expectations for data quality throughout the entire data lifecycle. This, in turn, allows data consumers to trust the data and make decisions based on accurate, timely and reliable information. And the benefits of data observability extend to other areas, such as:

Improved customer experience. Data observability can help ensure customer data is consistent across all channels, including online and offline interactions. This can help prevent confusion or frustration on the part of the customer.

Enhanced model performance. Data observability tools can help organizations monitor the performance of machine learning models, identifying and resolving issues that could impact performance. This can help ensure that models are accurate and reliable, leading to better predictions and outcomes.

Improved cost control. Monitoring data quality and pipeline performance can help organizations identify areas where resources are underutilized or overutilized and optimize their usage, improving their overall bottom line.

Mitigate risk and enhance compliance. Data observability can provide a clear and detailed view of the data lineage, including where it came from, how it has been transformed and where it is being used. This can help organizations verify that data is used appropriately and complies with regulations.

Resolve issues faster. When issues are discovered in the data or the data pipeline, data observability allows organizations to understand the impact on systems and processes, speeding up time to resolution.

Increased pipeline efficiency. With data observability, data engineers gain deep visibility into the performance and behavior of their data systems. This can help identify opportunities to optimize and tune their data pipelines to enhance the overall operational efficiency of their data infrastructure.

Data observability also benefits data engineers by enabling faster issue resolution, improving data quality, increasing efficiency, reducing risk and promoting better collaboration. By leveraging these benefits, data engineers can focus on delivering high-quality data and insights to support business goals and objectives.

The first step is to assess the current state of the data infrastructure, including data sources, data quality and data governance policies. Based on this assessment, a plan can be developed to address issues and implement the necessary tools and technologies. Here are some key elements that are necessary to deliver data observability:

Monitoring tools – Data observability requires monitoring tools to collect and analyze data from various sources, including data pipelines.

Data quality tools – Data quality tools are essential for monitoring the quality of the data being processed by data pipelines. These tools can detect issues such as missing data, data duplication and data inconsistency. Data quality tools can also help remediate problems with the data.

Data visualization tools – Data visualization tools present data in a visual format, making it easier to analyze and interpret. These tools can monitor data pipeline performance and quality, enabling data observability.

Automation – Automation is essential for delivering data observability at scale. Automated processes can be used to detect and resolve issues in data pipelines quickly and efficiently.

Data governance – Data governance manages the availability, usability, integrity and security of data used in an organization. Data governance helps ensure that data pipelines deliver high-quality data and that the organization uses its data within stated policies and guidelines.

Skilled personnel – Skilled data engineers, data analysts and data scientists are essential for delivering data observability. They can design, develop and maintain data pipelines and use monitoring tools to detect and resolve issues.

Culture of collaboration – Data observability requires collaboration between data management, development and operations teams. Team collaboration is crucial for efficient data pipelines and high-quality data.

Data observability can help you address challenges like these:

• Data quality issues
• Pipeline bottlenecks
• Operational issues such as system downtime, errors in processing or other problems that can impact business operations

The importance of data observability cannot be overstated: it can be critical to the success of any data-driven organization. Without proper observability tools, it can be a challenge for you to detect and resolve issues.

And this can lead to problems with data quality, potential data loss and disruptions for your business. Delivering data observability requires the appropriate tools, the correct processes and the best people with the right skills and relevant expertise.

Today’s businesses know that high-quality data is crucial for making informed decisions. When data is accurate, complete and relevant, it provides a clear picture of the situation. Decision-makers can make confident, well-informed choices grounded in evidence and data.

The modern organization is managing higher volumes of data at faster rates for more users. Data observability offers a way to help ensure that data pipelines can deliver high-quality data that is reliable, timely and accessible.

To learn how data transparency fuels better business intelligence and data governance, join our webinar, “Deliver High-Quality Data Faster with Data Observability.”