Achieve data integrity
Make sure that third-party data is tagged and classified appropriately.
Make your data quality as stringent as your industry regulations.
Learn how Clearcover, an automotive insurance leader, increased data quality coverage by 70 percent with Monte Carlo.
Benefits
70%
increase in data quality coverage
50%
faster resolution times
“We no longer had to tailor specific tests to every particular data asset. All we really had to do was sign up, add the security implementation to give Monte Carlo the access that it needed, and we were able to start getting alerted on issues. Monte Carlo gave us that right out of the box.”
Rising data volumes stemming from business applications and departments
Lack of domain knowledge into department-specific data sources
Complexity associated with their growing data stack implementations
Reduced implementation times with out of the box coverage for new datasets
Automated data lineage creation to isolate and remediate incidents
Custom field health thresholds to meet internal SLAs
Benefits
70%
increase in data quality coverage
50%
faster resolution times
Rising data volumes stemming from business applications and departments
Lack of domain knowledge into department-specific data sources
Complexity associated with their growing data stack implementations
Reduced implementation times with out of the box coverage for new datasets
Automated data lineage creation to isolate and remediate incidents
Custom field health thresholds to meet internal SLAs
“We no longer had to tailor specific tests to every particular data asset. All we really had to do was sign up, add the security implementation to give Monte Carlo the access that it needed, and we were able to start getting alerted on issues. Monte Carlo gave us that right out of the box.”
Make sure that third-party data is tagged and classified appropriately.
When it comes to your customers’ finances, one inaccurate field can have detrimental effects. Invest in a data quality solution that alerts you before incidents occur.
Leverage application data to improve the user experience at each stage of the customer journey.
Monte Carlo is a very good way for us to understand our data quality at scale.
Ensure your consumers are ingesting reliable data to drive financial decision making.
You have models to understand where risks are in your portfolio, but you need to know where risks exist in your data, too.
Ensure your service is industry-compliant with accurate data to avoid costly fines and reputational damage.
Now, we can start having those proactive conversations to prevent downtime before stakeholders are affected, versus finding out after the fact that something was broken and then rushing to get it fixed.
Out-of-the-box coverage across all your data tables, opt-in monitors for key assets, and monitors-as-code.
Don’t just sound the alarm when data incidents occur. Empower your data teams to resolve incidents in minutes, not days.
Rich insights enable your team to proactively ensure data quality, and make better infrastructure investment decisions.