What Does Data Observability Mean?
Data has become a critical component of digital services, products, and decision-making. Inaccurate or flawed data can lead to wasted resources, loss of revenue, and reduced trust in the organisation. Studies show that companies lose millions of dollars annually because of bad data, resulting in missed opportunities, misallocated budgets, inaccurate reporting, and customer churn.
According to the 2023 State of Data Quality survey, “The problem of erroneous and inaccessible data still exists today. In fact, data downtime nearly doubled year over year”. Over 50% of respondents stated that at least a quarter of their revenue was impacted by data quality issues. The average percentage of revenue affected by these issues increased from 26% in 2022 to 31% in 2023.
Data observability solves this problem by providing an end-to-end solution to ensure data is accurate and reliable. With this approach, teams can detect, resolve and prevent data incidents by having visibility across the data ecosystem. This concept is about getting visibility over your entire data ecosystem so that you can monitor freshness, schema, volume, and quality and understand your environment deeply enough to prevent the same issues from happening over and over again.Similar to its DevOps equivalent, data observability makes use of automated monitoring, alerting, and triaging methods to detect, assess, and resolve issues concerning data quality and discoverability.
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 the data is incomplete, incorrect, or missing, and making sure that your data is reliable at every stage of the data pipeline.
Data observability is based on five pillars:
- Freshness: How recent is the data and how often are tables updated?
- Volume: Is there missing data or duplicate data? What counts as a large change to table size?
- Schema: Has the organization of data changed? If so, who made the changes to the schema and when?
- Quality: Does your data fall within the expected range?
- Lineage: If data breaks, which downstream assets were impacted and which upstream sources are contributing to the issue?
Monte Carlo uses a data collector deployed in its own secure environment to connect to data warehouses, data lakes and BI tools. It does not store or process the actual data. Only metadata, logs and statistics are extracted. Monte Carlo then learns about the data environment and the historical patterns and automatically monitors for abnormal behaviour, raising alerts when anomalies occur or when pipelines break.
By using query and BI tool reads to understand the importance of tables in data warehouses or lakehouses, Monte Carlo recognises Key Assets and assigns an Importance Score between 0-1 to every table, with 1 signifying higher importance.
It detects freshness, volume, and schema incidents out of the box, providing end-to-end coverage across the entire data stack. Notable features include ML-enabled data anomaly detection, data lineage for getting to the root of the problem, data quality insights, as well as integrations and interoperability with other data tools (databases, catalogues, BI tools, warehouse and lakehouse platforms).
In case of data incidents, a clear and concise context is presented in a structured format, including the table(s) involved, the incident owner, severity level, notification channels, as well as the number of affected users, queries and reports. Users can investigate the incident using the “Field Lineage” and “Table Lineage” tools and identify potential sources that live upstream or downstream.
The main benefits of the Monte Carlo Data Observability platform
The Monte Carlo Data Observability platform provides an extensive approach for achieving better data quality management at scale, surpassing the capabilities of traditional testing and monitoring solutions.
The main benefits include:
- A centralised view of the data ecosystem, including schema, lineage, freshness, volume, users, queries, etc., to achieve a better understanding of data health over time;
- Automatic monitoring, alerting, and root cause analysis for data incidents, without requiring significant configuration or threshold-setting;
- Lineage tracking;
- Custom and machine learning-generated rules;
- Enhanced data reliability insights.
What can Monte Carlo be used for?
Several typical use cases stand to gain from the implementations of Monte Carlo, such as:
- Data quality monitoring and testing
Automate data quality tests with out-of-the-box monitoring, powered by machine learning recommendations, to enable centralised monitoring of all production tables and customised monitoring for critical assets.
- Data mesh and self-serve
Ensure reliable self-serve analytics with data quality and integrity, supporting data mesh and ownership of trustworthy data products. Create flexible domains to map data sources and consumers with efficient detection and resolution of data incidents and anomalies.
- Report and dashboard integrity
Monte Carlo automatically detects impacted data consumers and affected BI reports and dashboards during an incident. Teams are provided with table and field-level lineage to understand relationships between upstream tables and downstream reports.
- Customer-facing data products
Monte Carlo’s integration in workflows results in shorter time-to-detect and time-to-resolution for data incidents, vital for creating data products kept reliable and trustworthy by maintaining end-to-end data observability.
In conclusion
Unprecedented volumes and sources of data are being used to drive everyday business decisions, which means that data downtime due to broken dashboards, ineffective ML, or inaccurate analytics can translate into millions of dollars of lost revenue for large companies. So it has become imperative for data to be accurate, current, reliable, accessible, and easily monitored. Monte Carlo offers end-to-end data observability delivered in a user-friendly product.
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 2023 edition of the TechRadar by Devoteam report. To learn more about Monte Carlo and other technologies you need to know about, please download the TechRadar by Devoteam.
