Data observability benefits entire data pipeline performance
Data observability benefits include improving data quality and identifying issues in the pipeline process, but also has challenges organizations must solve for success.
Organizations serious about using data for better decision-making and machine learning should consider tapping into the benefits of data observability. Without data observability, businesses lack the end-to-end insight into data they need to optimize data pipelines and ensure the responsible use of data.
Data observability eliminates data downtime by applying DevOps best practices. For example, data observability platform provider Monte Carlo calculates data downtime by the average time to detection of an incident plus the average time to resolve an incident multiplied by the number of incidents in a given period.
"Data observability is an organization's ability to fully understand the health of the data in their systems," said Barr Moses, CEO and co-founder of Monte Carlo. "Data observability tools use automated monitoring, automated root cause analysis, data lineage and data health insights to detect, resolve and prevent data anomalies. This leads to healthier pipelines, more productive teams and happier customers."
Gartner is focusing on the practical aspects of data observability, which it calls "applied observability," and considers this one of the top 10 strategic technology trends of 2023. Applied observability "is the applied use of observable data in a highly orchestrated and integrated approach across business functions, applications, and infrastructure and operations teams to enable the shortest latency from action to reaction and proactive planning of business decisions."
Digital advertising software provider Choozle was able to recover 50% of engineering time by reducing time to detection, triage and remediation of data issues using Monte Carlo. Meanwhile, digital ticketing and experiences marketplace SeatGeek reduced the number of data incidents from 10 per month to zero and cut the resource cost of root cause analysis by half while improving efficiency across teams. In addition to reducing time-to-discovery including unknown unknowns, the company improved ELT system stability, eliminating data platform-related anomalies.
The most fundamental benefit of data observability is helping ensure data reliability. It also helps ensure organizations are meeting requirements.
- Accelerate time to value. A data observability platform should seamlessly connect with an organization's existing stack, without modifying data pipelines, writing new code or using a particular programming language. This accelerates time to value and maximizes testing coverage without having to make substantial investments.
- Meet security and compliance requirements. Data observability monitors data at rest and does not require extracting the data from where it's currently stored.
- Minimize downtime. Data observability platforms use machine learning models to automatically learn an organization's environment and data. It uses anomaly detection to identify and speed the resolution of issues.
- Minimize false positives. Data observability uses a holistic view of the data and the potential effects from any issue instead of individual metrics. The platform should require minimal configuration and almost no threshold setting so organizations can avoid spending resources configuring and maintaining noisy rules.
- Get broad data visibility with minimal effort. Data observability requires no prior mapping of what needs monitoring and in what way. It helps data professionals identify key resources, key dependencies and key invariants.
- Enable rapid triage and troubleshooting. Data observability provides rich context, rapid triage and troubleshooting, and effective communications with stakeholders affected by data reliability issues.
- It prevents issues from occurring in the first place. Data observability exposes rich information about data assets so that changes and modifications are responsible and proactive.
Challenges with enabling data observability
Data silos remain a challenge at many organizations. Even as they recognize the growing importance of data and implement data-driven strategies, the teams responsible for the data may be considered plumbers building and maintaining pipes rather than partners.
"The challenges of data observability are rarely technical or even budgetary," Moses said. "For many data teams, the challenge is effective communication and education."
Barr MosesCEO and co-founder, Monte Carlo
One of the biggest misconceptions of data observability is that it's another term for data quality monitoring. It also encompasses automation, root cause analysis, field-level lineage, incident remediation, effect analysis and operational analytics to prevent future issues and improve data reliability over time.
The most common challenges relate to building the framework with the wrong materials and the wrong data outputs, which leads to erroneous conclusions, said Tony Davis, chief observability evangelist at Broadcom Software.
"In these cases, the secondary or add-on benefit of continuous improvement of core systems is not possible since the observability framework is defective," Davis said. "In the classical sense, data observability is very simple: Can you look at the output of a system, whether physical or digital, and determine its state (either perfect or defective) and draw impactful conclusions from what you observed? The additional component of the observation process is what you learn that can make the system better."
Who's responsible for data observability
Several people are responsible for data observability in an organization, each of which have different concerns. For example, the chief data officer will want to know if different departments are getting the data they need to be effective and whether data-related risks are being managed effectively.
A business intelligence analyst wants to understand whether the data team is translating data into meaningful insights for the business, the data is reliable and the insights are easy to understand. Similarly, a data scientist's concern is with data reliability and where the data came from.
The data governance lead wants to ensure unified definitions of data and metrics across the organization as well as who has access and visibility into what data. Meanwhile, data engineers want to know the data platform can scale, whether data ingestion is reliable, whether the data platform is accessible, if data downtime episodes need a quick fix and whether the data engineers are able to do their jobs effectively.
Finally, the data product manager wants to know if their team has the right tools and offerings to make decisions and whether the data is GDPR and CCPA compliant.
Data observability provides a holistic view of all data and monitors for it for potential issues. With it, organizations can benefit from improved data quality, consistency, accuracy, efficiency and performance end to end. They can also ensure data reliability, security and compliance.