5 Key Pillars of Data Observability: How to Improve the Health of Your Data

Data Observability Self-assessment:
Keep track of all that apply to you.

True or False: Your data platform is looking to migrate to the cloud, or already has. 
True or False: Your data stack is continuously scaling with more data sources, tables, and complexity.
True or False: Your data team is spending 30% or more of their time finding, troubleshooting, and resolving data quality issues. 
True or False: Your data team is growing rapidly. 
True or False: Your team has had a larger number of data consumers in the last year than previous. 
True or False: Your company is dealing with data silos. 
True or False: You have a fragmented view of your data pipelines, resulting in incomplete integrations.
True or False: You have limited data governance. 
True or False: You use data as the key to understanding customer value propositions. 

If 2 or more of these apply to you, then your data health might be in danger. But no fear, you can improve the health of your data and performance using data observability.

In today’s data-driven world, organizations are dealing with massive amounts of data generated from various sources. This influx of data has given rise to the need for robust data management practices.

But, in 2021 alone 76% of businesses lost data as a result of downtime, and 20% of businesses who experienced a significant outage in the last three years resulted in damage to their reputation, revenue, and compliance adherence. (Source: Trilo)  

Data observability has emerged as a critical concept in this context, offering organizations the ability to monitor, assess, and maintain the health and performance of their data systems. Keep reading to learn more about the importance and benefits of data observability, the challenges organizations face without it, and the key pillars that underpin a successful data observability framework.

What is Data Observability? 

Data observability is a process and set of practices aimed at providing organizations with a holistic understanding of the health and performance of their data within IT systems. It encompasses automated monitoring, root cause analysis, data lineage, and data health insights to proactively detect, resolve, and prevent data anomalies. In essence, data observability ensures that an organization’s data is reliable, up-to-date, and consistently delivered to end-users.

Importance of Data Observability

50% of a data practitioner’s time is spent on finding, troubleshooting, and resolving data issues. Organizations are increasingly relying on data to drive decision-making, gain insights, and enhance customer experiences. Here are several key reasons why data observability is essential:

1. Enhanced Data Quality: Allows organizations to monitor and maintain the quality of their data. It ensures that data sets are accurate, reliable, and meet standards, therefore increasing trust in the data.
2. Proactive Issue Detection: Helps organizations detect data issues in real-time or near real-time, allowing for immediate corrective actions. This proactive approach minimizes data downtime and ensures that data remains usable.
3. Improved Data Reliability: By continuously monitoring data pipelines and systems, organizations can enhance the reliability of their data environment. This, in turn, leads to more consistent and dependable data for analysis and decision-making.
4. Greater Efficiency: Enables data teams to identify and resolve data issues quickly, reducing the time and effort required for troubleshooting. This efficiency translates into better productivity and resource utilization.
5. Increased Customer Satisfaction: Reliable data translates into better customer experiences. Organizations that prioritize data observability can enhance data quality and provide more accurate and timely services to their customers.

Challenges Without Data Observability

Not having a data observability framework in place can pose significant challenges and hinder an organization’s ability to effectively manage its data resources. Some of the key challenges include:

1. Lack of Organizational Support: Implementing data observability practices and tools often requires funding and executive support. Without this backing, it can be challenging to establish effective data observability.
2. Data Silos: Data stored in standalone silos or hidden repositories can prevent organizations from gaining a comprehensive view of their data assets, limiting their ability to observe and manage data effectively.
3. Incomplete Integration: Failing to integrate all systems into a data observability tool can result in a fragmented view of data pipelines, making it difficult to detect issues across the entire data ecosystem.
4. Limited Data Governance: Data governance programs are essential for ensuring data compliance and consistency. Without data observability, these programs may struggle to monitor and maintain data quality effectively.

5 Key Pillars of Data Observability

To establish a high-functioning data observability framework, organizations should consider the following key pillars:

Freshness

Freshness focuses on ensuring that data is up-to-date and updated at the required intervals. It prevents the use of stale data, which can lead to inaccurate decision-making.

Quality

Quality assessment examines the data itself, checking for issues such as null values and data outliers. It provides insights into the trustworthiness of data sets.

Volume 

Volume assessment verifies the completeness of data tables, ensuring that they contain the expected number of rows and columns.

Schema

Schema monitoring tracks changes in data organization, which can be indicators of broken data. It helps organizations understand when and how data schema changes occur.

Lineage

Data lineage provides a comprehensive view of an organization’s data landscape, including upstream sources, downstream systems, and data interactions. It aids in troubleshooting and supports data governance efforts.

By embracing the key pillars of data observability and recognizing its importance, organizations can establish a solid foundation for data-driven decision-making, customer satisfaction, and operational excellence. Data observability is no longer a luxury but a necessity in the era of big data.

Ready to unlock your organization’s full potential? Contact us today and transform your organization’s data challenges into opportunities.