Boston's Metaplane Secures $13.8 Million in Series A Funding to Enhance Data Quality Solutions
Metaplane, a Boston-based startup focused on addressing data quality issues for enterprises, has successfully raised $13.8 million in a Series A funding round. The investment was led by Felicis, with participation from Khosla Ventures, Flybridge, Y Combinator, Stage 2 Capital, B37, and SNR.
The company aims to use the funds to advance its AI-powered data observability platform, striving to create the “most powerful, configurable, and user-friendly solution for trustworthy data.”
Competing in the Data Observability Landscape
Founded by MIT graduate Kevin Hu, former HubSpot engineer Peter Casinelli, and ex-Appcues developer Guru Mahendran, Metaplane is competing against well-funded rivals like Monte Carlo, Observe, and Acceldata in the fast-growing data observability market. With a three-fold increase in its customer base over the past year, Metaplane counts brands such as Bose, Sigma, Klaviyo, and ClickUp among its clients.
The Importance of Data Monitoring
In today’s data-driven business environment, organizations rely on data analytics for informed decision-making and to anticipate key operational aspects, such as inventory management for significant events. The rise of generative AI applications compels businesses to integrate data from various sources for increased value. However, the complexities of managing numerous data pipelines make it challenging to maintain quality oversight.
Metaplane leverages AI to empower enterprises to proactively monitor data incidents across their ecosystems.
Integrating with the Data Stack
“Our platform seamlessly integrates with various components of the data stack, including ingestion tools like Fivetran, cloud data warehouses like Snowflake and BigQuery, transformation layers such as dbt and Airflow, and BI tools including Sigma and Tableau. Notably, we are the only observability solution that integrates with transactional databases like Postgres and MySQL, identifying issues even within dbt pull requests on GitHub,” explains Hu.
Machine Learning for Data Quality Monitoring
Once integrated, users can easily configure monitors for frequently updated tables to track essential data quality metrics like freshness, row count, uniqueness, and nullness. This setup takes approximately 15 minutes, after which the AI starts functioning.
The platform’s machine learning model learns from the data characteristics, using historical metadata to flag anomalies, including schema changes, within one to two days. This fully automated feature sends alerts directly to relevant data teams, ensuring accurate and timely notifications.
“Our models utilize extensive historical data to account for seasonality and minimize repetitive alerts. We understand that each business is unique, which is why we allow users to customize models to filter one-off anomalies or adapt to evolving trends,” Hu notes.
Metaplane also provides domain-specific monitoring, detecting data problems with granular control, tracking data usage changes, and analyzing cloud warehouse expenditures. Its comprehensive coverage allows for detailed column-level lineage tracking, offering insights into the downstream impacts of issues as well as upstream root causes.
Achieving Significant Results in Data Quality Management
Despite having less funding compared to its competitors, Metaplane has made significant strides in the data observability field. In 2023, its Annual Recurring Revenue (ARR) increased six-fold, with its customer base surpassing 100 enterprises, including recognizable names like Klaviyo, Bose, ClickUp, and Census. By January 2024, these clients had conducted 500 million data quality checks on over 40 million assets, successfully resolving 80,000 incidents.
“Every company should have confidence in its data, which is why we offer a free self-serve model. This approach has led to substantial organic growth, attracting more users than any other observability tool,” emphasizes Hu.
Future Developments for Enhanced Data Observability
Looking ahead, Metaplane plans to invest the new funding primarily into research and development to enrich its observability platform for enterprise teams. Upcoming enhancements will focus on automating monitoring architectures and expanding the range of observable metrics, data sources, and interconnections.
“Our vision is to create a platform that learns from each customer's specific needs, providing tailored recommendations for monitoring and alerting structures as they evolve. We aim to broaden our metrics while deepening existing ones, ensuring our clients possess the necessary context to effectively identify and resolve data quality issues,” Hu concludes.
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