Data Observability: Don’t Let Bad Data Sink You

What does data observability mean?

Data downtime drains businesses of revenue, missed opportunities, and customer trust. However, data observability can prevent these costly disruptions. By implementing data observability, organisations gain a comprehensive way to monitor data, detect anomalies, and ensure its reliability throughout its lifecycle.

The 2023 State of Data Quality survey highlighted that data downtime nearly doubled over the past year. Over 50% of respondents reported that data quality issues impacted at least a quarter of their revenue. In fact, the average revenue affected by data quality issues rose from 26% in 2022 to 31% in 2023.

Data observability addresses this problem by providing a complete solution to keep data accurate and reliable. With this approach, teams can detect, resolve, and prevent data incidents with end-to-end visibility. This visibility allows monitoring of freshness, schema, volume, and quality across the data ecosystem. Like its DevOps counterpart, data observability relies on automated monitoring, alerting, and triaging to detect and resolve issues, ensuring data quality and discoverability.

Data downtime is costing businesses millions in lost revenue, missed opportunities, and eroded trust. But what if you could prevent these costly disruptions? Data observability offers a comprehensive solution to monitor your data, detect anomalies, and ensure its reliability throughout its entire lifecycle.

An overview of the Monte Carlo data observability platform

As a leader in data observability, Monte Carlo is dedicated to eliminating data downtime, i.e., periods of time when Monte Carlo, a leader in data observability, aims to eliminate data downtime—when data is incomplete, incorrect, or missing. The platform ensures data reliability at each stage of the data pipeline.

Data observability on the Monte Carlo platform is built around five pillars:

• Freshness: Tracks the recency of data and the frequency of table updates.
• Volume: Checks for missing or duplicate data and flags significant changes in table size.
• Schema: Monitors structural changes in data, noting who made changes and when.
• Quality: Confirms data falls within expected ranges.
• Lineage: Tracks which downstream assets are impacted when data breaks and identifies contributing upstream sources.

Monte Carlo’s data collector connects securely to data warehouses, lakes, and BI tools without storing or processing actual data. Instead, it only extracts metadata, logs, and statistics. This setup allows Monte Carlo to understand the data environment and historical patterns, automatically detecting abnormal behaviour and alerting users when anomalies arise.

Monte Carlo assigns an Importance Score, between 0 and 1, to tables, with 1 indicating high importance. Using machine learning, it detects freshness, volume, and schema incidents across the data stack. The platform includes ML-powered anomaly detection, data lineage, quality insights, and integrations with a range of data tools, including databases, catalogues, and BI platforms.

In case of data incidents, the platform presents clear, structured context, including affected tables, incident severity, and notification channels. Users can investigate with “Field Lineage” and “Table Lineage” tools to identify potential upstream or downstream sources.

The main benefits of the Monte Carlo data observability platform

Highlighted on TechRadar by Devoteam, the Monte Carlo data observability platform provides a scalable approach to data quality management. It surpasses traditional testing and monitoring methods with its advanced capabilities.

The platform’s main benefits include:

• A centralised view of data health, covering schema, lineage, freshness, volume, users, and queries.
• Automated monitoring, alerting, and root cause analysis for data incidents, with minimal configuration needed.
• Detailed lineage tracking for visibility into data flow and dependencies.
• Customisable and ML-generated rules to support flexible data management.
• Enhanced data reliability insights for informed and proactive decision-making.

What can Monte Carlo be used for?

Monte Carlo enables several key use cases for modern data teams:

Customer-facing data products: Monte Carlo’s integration into workflows shortens time-to-detect and time-to-resolution for data incidents. This is essential for creating reliable data products and maintaining end-to-end data observability.

Data quality monitoring and testing: Automate data quality tests with out-of-the-box monitoring powered by machine learning recommendations. Enable centralised monitoring of production tables and customised monitoring for critical assets.

Data mesh and self-serve analytics: Ensure reliable self-serve analytics with robust data quality and integrity. Support data mesh principles by creating domain-specific ownership and trustworthy data products.

Report and dashboard integrity: Monte Carlo automatically identifies impacted data consumers, BI reports, and dashboards during incidents. Teams receive table and field-level lineage, helping them understand relationships between upstream tables and downstream reports.

In conclusion

With an unprecedented volume of data driving business decisions, data downtime from broken dashboards, ineffective ML models, or inaccurate analytics can cost large companies millions. Therefore, data must be accurate, current, reliable, accessible, and continuously monitored. Monte Carlo provides an end-to-end data observability solution in a user-friendly product, ensuring data remains a trustworthy and valuable asset.

Want to assess Monte Carlo’s relevance and potential for your organisation?

Connect with one of our experts today and find out if Monte Carlo is the right solution for you.

This article is part of a larger series centred around the technologies and themes found within the edition of the TechRadar by Devoteam. To learn more about Monte Carlo and other technologies you need to know about, please explore the TechRadar by Devoteam.