Monte Carlo announced Data Product Dashboard, a new capability that allows customers to define a data or AI product, track the health of corresponding data tables and training sets, and report on the product’s reliability to business stakeholders, directly in their data observability platform.
Data products refer to an application or asset – such as key dashboards, large-language models, or software – that delivers trusted information or services to downstream consumers. Examples of data products include an airline’s flight tracking system that combines real-time GPS data, flight manifest tables, and historical arrival and departure information; a customer relationship management platform syncing data across marketing tools; or an AI model that trains on financial data from thousands of sources to forecast future stock returns.
One of the biggest hurdles to data product adoption? Data trust. For instance, a 2023 survey of over 200 data engineers conducted by Wakefield Research and Monte Carlo revealed bad data impacted 31% of revenue, which rose from 22% in the previous year’s survey. That same survey also found 74% of respondents – data engineers and other data consumers – reporting that business stakeholders first identified problems with the data most or all of the time.
“As companies ingest larger volumes of data, the opportunity to build impactful and innovative data products exponentially grows. In order for data and AI products to realize their full potential, however, data teams must treat them with the same diligence as software applications, and that includes ensuring their accessibility, performance, and most importantly, reliability,” said Lior Gavish, CTO of Monte Carlo.
“Data Product Dashboard is the first solution of its kind to help organizations manage and improve the data quality of the tables and assets powering their most critical data applications, and in the process, foster greater trust and collaboration between data teams and their stakeholders.”
With the launch of Data Product Dashboard, Monte Carlo broadens the conversation around data quality beyond individual tables and zeroes in on the reliability of specific use cases. Customers can now easily identify which data assets feed a particular data product and unify detection and resolution for relevant data incidents in a single view.
Driving data adoption at scale with Data Product Dashboard
Available to all customers today, Data Product Dashboard will focus on three main areas to help data teams better track and improve data health and reliability for critical data products across the organization:
- Define data products. Data Product Dashboard makes it easy to define the scope of specific data products based on the tables feeding it and their data and AI products, including dashboards and large-language models. Users can select the relevant tables and their associated assets to define specific data products, thereby keeping everyone aligned on data product definitions.
- Track data product health over time. The solution reports on key data health metrics and KPIs over time, including the number of incidents impacting a given data product, incident status and severity, monitor coverage for the tables feeding a given product, and more. This enables teams to create both trust and accountability in the data, tying your tables and assets directly to tangible business outcomes.
- Communicate data product reliability to stakeholders. Data Product Dashboard makes it easy to share high-level stats about data product reliability with downstream stakeholders, executives, and others reliant on them to inform their work.
Data Product Dashboard is just one release in a steady stream of recent Monte Carlo product launches dedicated to reducing the time to detection and resolution for data downtime, and in the process, driving the adoption of trusted data for businesses worldwide.
Over the past few months, Monte Carlo expanded their end-to-end coverage by releasing native integrations with data catalog Atlan, code and model repository GitHub, business intelligence solution Sigma, and more components of the modern data stack to ensure reliability at each stage of the pipeline.
And last October, the company unveiled their Data Reliability Dashboard, which provides a snapshot view of an overall data environment, providing data reliability metrics over time.
“Monte Carlo’s Data Product Dashboard is an exciting development for organizations who are adopting data mesh and putting a premium on the reliability of their data products such as business critical dashboards and AI models. We are currently using it in a deployment with a large enterprise to easily track and surface the data health of key assets to increase trustworthiness across data teams and data consumers,” said Manisha Jain, data engineer and lead consultant at Thoughtworks.