Monte Carlo for financial services.

Make your data quality as stringent as your industry regulations.

Book a demo

Trusted by the data teams at

  • Credit Karma
  • Sofi
  • Pitney Bowes

NASDAQ scaled data trust across 6,000 corporations and 30 financial exchanges

2,2000

FinServ institutions monitored.

8 hours

saved per incident.

Challenge

  • 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.

Solution

  • Reduced implementation times with automated for new datasets.
  • End-to-end data lineage to isolate and remediate incidents.
  • Custom field health thresholds to meet internal SLAs.

“Data observability enables you to understand the quality of your data, data lineage, and data impact and the financial cost of your data being down. Data in the wild doesn’t stay static, and what you’re doing today may not be relevant in six months. That’s where data observability really shines, because it provides the mechanism to understand what your data is doing over time.”

Mike Weiss AVP Product Management

Read their story

Use cases for financial technologies.

Achieve data integrity.

Make sure that third-party data is tagged and classified appropriately.

Scale proactive incident resolution.

One inaccurate field can have detrimental effects. Invest in a data quality solution that alerts you before incidents occur.

Increase customer engagement.

Leverage application data to improve the user experience at each stage of the customer journey.

Use cases for financial services and insurance.

Make accurate decisions.

Ensure your consumers are ingesting reliable data to drive financial decision making.

Remove the risk from risk management.

You have models to understand where risks are in your portfolio, but you need to know where risks exist in your data, too.

Ensure data compliance.

Ensure your service is industry-compliant with accurate data to avoid costly fines and reputational damage.

Detect.

Out-of-the-box coverage across all your data tables, opt-in monitors for key assets, and monitors-as-code.

See how

Resolve.

Don’t just sound the alarm when data incidents occur. Empower your data teams to resolve incidents in minutes.

See how

Prevent.

Rich insights enable your team to proactively ensure data quality, and make better infrastructure investment decisions.

See how