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Monte Carlo for Technology

Use reliable data to run internal analytics and develop products more quickly and efficiently.

Integrate data quality monitoring tools into your existing stack with Monte Carlo
Gitlab
Gusto
HubSpot
MURAL
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Leading data teams in technology choose Monte Carlo

Learn how Prefect, a leader in dataflow automation software, saved 50% of engineering time with Monte Carlo

Benefits

  • 50%

    engineering time saved in triage and resolution of data quality issues

  • 16X

    faster implementation time compared to building an in-house solution

“It’s really easy for us to propose and make big changes upstream in the data warehouse and know exactly what is going to be impacted by the change. We’re able to explore specific fields and how they move through our data transformation steps—and to see what dashboards are going to be impacted and where they’re going to end up.”

Dylan Hughes

Senior Software Engineer

Data Stack

  • Lean two-person data team

  • Lack of understanding where data quality issues were across their entire stack

  • Significant time spent troubleshooting existing issues

  • Field-level lineage to show data flows and incidents between related objects

  • Integration with Slack to route incidents to the affected stakeholders

  • Automated data quality issues discovery across their entire data stack without requiring domain knowledge

Benefits

  • 50%

    engineering time saved in triage and resolution of data quality issues

  • 16X

    faster implementation time compared to building an in-house solution

  • Lean two-person data team

  • Lack of understanding where data quality issues were across their entire stack

  • Significant time spent troubleshooting existing issues

  • Field-level lineage to show data flows and incidents between related objects

  • Integration with Slack to route incidents to the affected stakeholders

  • Automated data quality issues discovery across their entire data stack without requiring domain knowledge

Data Stack

“It’s really easy for us to propose and make big changes upstream in the data warehouse and know exactly what is going to be impacted by the change. We’re able to explore specific fields and how they move through our data transformation steps—and to see what dashboards are going to be impacted and where they’re going to end up.”

Dylan Hughes

Senior Software Engineer

Use cases for SaaS

Win with accurate data

Ensure that you’re using accurate usage data when deciding where to invest in your product’s future.

Tailor the user journey

Use high quality data to create high quality feature recommendations for current users.

Attract and retain customers

Reliably report on product metrics, segmented by accounts, to understand potential areas of customer growth and churn.

Data observability is the first step before any data incident management steps, including incident response and escalation, can happen.

Manu Raj Senior Director of Data Platform and Analytics
Read their story

Use cases for hardware + networking

Improve ROI on Research

With lengthy and costly development cycles for hardware products, ensure that data stemming from your testing platforms is always within acceptable thresholds.

Solve HARDware problems

Identify whether gaps in manufacturing and inventory numbers are truly business issues or actually data issues.

Consolidate Field and Cloud

Use information from your devices in the field to proactively maintain or replace faulty hardware for a seamless production experience.

Learn more about our product

Detect

Detect

Find the data quality blockers across your entire stack that are preventing you from being fully confident in your product build decisions.

See how
Resolve

Resolve

Save time finding and fixing data issues across all of your product tracking objects

See how
Prevent

Prevent

As one of the fastest evolving industries, your data strategy is always changing – use our recommendations to mitigate future data disasters

See how

Use data observability to lead the technology industry

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