Highlights:

  • The new Data Product Dashboard aims to make detecting vulnerabilities in company data even more accessible and simplify several associated duties.
  • Monte Carlo says its tool can track AI models and training datasets.

The Data Product Dashboard, a new tool from startup Monte Carlo Data Inc., can assist businesses in identifying quality concerns in their business data.

Monte Carlo, situated in San Francisco, has received over USD 230 million in investment. It provides a data observability platform that assists businesses in ensuring the accuracy of the information they use to make business decisions. The platform developed by the startup can detect duplicate records, out-of-date information, and other data quality issues.

The new Data Product Dashboard aims to make detecting vulnerabilities in company data even more accessible and simplify several associated duties.

Analytics tools, for example, frequently process data from several sources. When a corporation suspects that an application has swallowed incorrect data, determining the root of the problem can be time-consuming. It can be challenging to tell what information was absorbed and when.

Monte Carlo claims that its Data Product Dashboard makes the work easier. The tool allows you to develop a technical description outlining the data assets an application consumes. When an issue with information quality arises, engineers can refer to that definition to decide where to look for the root cause.

In an interview, Lior Gavish, Chief Technology Officer and Co-founder, elaborated on how the feature works. “They can basically go into the tool, define what constitutes the data product — what are the objects that are actually the data product, whether it’s tables or BI reports or models — and understand not just what’s going on with those specific objects but what’s going on with everything that’s upstream of them,” Gavish mentioned.

The Data Product Dashboard is also useful for machine learning initiatives. Monte Carlo says its tool can track AI models and training datasets.

The tool creates metrics to describe the quality of a company’s data assets. It can display the number of concerns affecting a particular asset, their severity, and whether they have been resolved. It also includes technical details on the database tables that comprise a given data asset.

Another advantage the company claims is that the tool can help engineers spot issues in datasets before business users. “From a trust perspective, there’s a huge difference between you finding out that the address was wrong versus the person who built the system for you telling you,” Gavish explained.

As a part of Monte Carlo’s data observability platform, the Data Product Dashboard is currently in testing.