Scaling Data Trust and Collaboration with Monte Carlo and Atlan’s New Integration
According to a recent survey, data engineers are reporting nearly twice as many data incidents this year as last. On average, it takes 15 hours per incident to reach a resolution. And nearly 75% of the time, business stakeholders are the first to identify data issues. As data leaders know, when business users encounter data that’s missing, erroneous, or otherwise inaccurate, decision-making is compromised and trust in data erodes.
That’s why we’re excited to announce that uniting diverse data team personas to collectively ensure data quality just got easier, thanks to a new integration between Monte Carlo’s data observability platform and Atlan’s active metadata platform.
Together, these market leading tools make it possible for companies that rely on data for a competitive advantage to understand and improve data quality, while ensuring data consumers have all the context they need, within their existing workflows, to make informed decisions with data.
Monte Carlo gives teams end-to-end data observability through automated detection, alerting, and incident resolution for data quality issues. Atlan is a home for diverse data teams, serving as a single source of truth that activates metadata across the modern data stack to enable new modalities of collaboration. And both platforms already provide deep integrations across the modern data stack, including Slack, Snowflake, dbt, Databricks, Sigma, and Fivetran.
Now, these two platforms work in tandem to provide data teams with enhanced visibility into key data operations and granular insights for data asset discovery and exploration. Data consumers can easily access up-to-date information about the quality of data assets before they use them, streamlining collaboration across the organization while building trust in data.
How businesses can leverage Monte Carlo + Atlan
With Monte Carlo and Atlan, teams can gain an up-to-date understanding of their data health, build trust in data, and support innovative new ways to approach distributed data infrastructure.
Extending visibility into data health
Teams using Monte Carlo and Atlan can now quickly understand the health of specific data assets across their Data Estate. Data status updates in Atlan about data health are informed by Monte Carlo, and data teams can now view monitors and tests created for each production table.
“Before we modernized our data stack, our data monitoring was very reactive,” said Michael Weiss, Senior Director of Product Management (NAM, Data Access and Analytics) at NASDAQ. “The pipeline might succeed, but we wouldn’t know if the data was right, wrong, or indifferent. With Monte Carlo and Atlan, we can catch data incidents early on, and provide everyone with clear visibility into the current status of data accuracy. This is proving valuable and has been critical for the executive team to have confidence we can deliver on our promise of reliable, trustworthy data.”
By extending visibility into data health, data teams can work proactively to resolve issues faster and ensure any impacted business users are aware of potential downstream impacts.
Increasing data trust and collaboration across the business
With this new integration, data consumers can view details of the latest data incidents and anomalies detected by Monte Carlo. This helps all data team personas, regardless of technical skillset, to keep close tabs on the reliability of any given asset, based on a common metadata control plane.
“With 1,600 employees serving over 1,000 clients with actionable, data-driven insights, we churn through massive volumes of data on a daily basis,” said Kenza Zanzouri, Data Governance Strategist at Contentsquare. “Our internal teams are always focused on creating value with new dashboards, models, or data explorations, so ensuring that data is reliable is essential. With Monte Carlo and Atlan, we’ve been able to shift from manual checks and testing to automated data quality coverage—and make it readily apparent to business users when data assets may be impacted by quality issues. This helps us scale in the long term and improve communication between departments.”
By dramatically improving visibility across data operations and streamlining communication, thereby enabling wider trust in data, data consumers and engineers can work with data in more efficient, collaborative, and innovative ways.
Enabling domain-oriented data management
Forward-thinking data organizations are increasingly moving to adopt distributed data architectures like the data mesh. Monte Carlo and Atlan help provide those data teams with peace of mind about data reliability, which can be a challenge when assets are owned by domain teams and available through self-serve access.
“At BairesDev, we provide leading businesses around the world with technology teams on demand, and implemented a data mesh approach to achieve data quality, availability, and performance across our organization,” said Matheus Espanhol, Data Engineering Manager at BairesDev. “Automation is an absolute necessity to achieve strong data governance across decentralized domains. With Monte Carlo and Atlan working in tandem, we can automate data quality standards while maintaining visibility into how each team follows global policies and sets local policies within the domain.”
With end-to-end visibility into data health and a centralized source of truth, it’s now possible for data teams to facilitate self-serve analytics without compromising on governance and quality standards.
How the integration works
With this new integration, Atlan and Monte Carlo work together seamlessly to centralize information and communication about data quality.
Data incidents detected by Monte Carlo will be surfaced in Atlan, with all the context needed to understand exactly what and where data has been impacted. When incidents are resolved, they’ll be cleared—meaning a lack of incidents indicates all is well with a given asset. What is more, any Monte Carlo custom monitors will be recorded as native assets in Atlan, providing line of sight for data consumers to the underlying data quality framework implemented with Monte Carlo.
By uniting a wider spectrum of data personas, Monte Carlo and Atlan enable the activation of data consumer business context, such that this valuable IP can be productionized to further advance data trust and a deeper culture of data quality.
The integration takes five minutes or fewer to configure, with no API use needed, and all data is available via the Atlan Chrome extension. This means teams can access rich metadata context directly in the workflows where they use the underlying assets, such as Data Clouds, Lakehouses, Warehouses, and popular Business Intelligence tooling
Get started today
To learn more about building trust in data with Monte Carlo and Atlan, explore our documentation.
- Create an account-service API key
- Configure the integration in 5 minutes (with interactive walkthrough)
- Metadata sourced from Monte Carlo
Ready to start improving your data quality with end-to-end data observability and cataloging?
Reach out to request a Monte Carlo demo to see data observability in action or start a free trial with Atlan to see data collaboration work.