Monte Carlo is focused on helping organizations obtain more reliable data. In early February, the San Francisco-based InsurTech startup took a step closer to fully realizing its mission with a new $25 million Series B financing round that will fuel further technology development and hiring.
The company’s business model is built around data observability, which involves the use of technology tools that help monitor enterprise data systems and troubleshoot any problems that arise. Data and analytics will help them do this affectively, and Monte Carlo wants to help organizations accelerate adoption of both technologies.
Why does data observability matter to carriers and insurers? Monte Carlo Founder and CEO Barr Moses explains why, below, in a Q&A with Carrier Management Editor Mark Hollmer.
Q: When did Monte Carlo launch?
Moses: Monte Carlo was launched in 2019.
Q: How many employees does it have? How many will it add with the new funding?
Moses: Monte Carlo has 25 employees, and is currently hiring for Marketing, Sales, Engineering, Product, and BizOps roles.
Q: Can you define data observability in layman’s terms?
Moses: As businesses increasingly rely on data to drive better decision making, it’s mission-critical that this data is accurate and reliable. Data observability is an organization’s ability to fully understand the health of the data in your system and prevent what we call “data downtime.” Drawing corollaries to the concept of application downtime (in other words, when websites are down), data downtime refers to periods of time where critical data is missing, inaccurate, or otherwise erroneous. Data observability uses automated monitoring, alerting, and triaging to identify and evaluate data quality and discoverability issues, leading to more trustworthy data, more productive teams, and happier customers.
Q: Why should carriers care about data observability?
Moses: In 2021, companies across industries spend, on average, $15M firefighting data quality issues, and 1 in 5 companies lose revenue and customers due to bad data. When money is involved, the stakes are even higher. Compared to 10 or even 5 years ago, insurance carriers are ingesting more and more third-party data across more and more critical workstreams.
Third-party data (i.e., policy claims, financial information, and PPI) is often manually aggregated, increasing the possibility error. For the insurance industry, missing or erroneous data can have significant implications on the business, leading to lost revenue, poor business decisions, and a lack of client trust. Data observability gives carriers peace of mind that their data is accurate and reliable at each stage of its lifecycle (from ingestion in the database to your analytics dashboards) by providing an automated approach to data governance.
Unlike traditional data governance solutions (i.e., data catalogs and metadata management systems), data observability tools never access your data, and instead, monitor your data at-rest. This additional layer of security ensures the utmost compliance with actuarial regulations and other industry standards.
Q: How widespread is the use of this technology/concept?
Moses: Observability has been a concept in software engineering for the past 10 years, with many businesses leveraging an observability and monitoring solution to ensure the reliability of their websites and software. Given the high volume and sensitivity around financial data, the insurance industry is among the earliest adopters of data observability, with insurance technology companies like Hippo Insurance and The Zebra charting the path forward.
Outside of insurance, industries that handle lots of sensitive data, including financial services, retail, e-commerce, and digital marketplaces, are at the forefront of this space.
Q: Do carriers need to update their digital/data capabilities for data observability to be viable for them?
Moses: No – data observability is available to all companies regardless of their digital or data capabilities. Unlike vendor-specific solutions, data observability is founded on the principle of being technology-agnostic. Moreover, a good approach to data observability will actually use machine learning and automation in a way that makes reliable data more accessible and scalable for your company by removing the need to manually validate your data.
Q: In your funding announcement you list one InsurTech as a customer. Do you have any other P/C industry clients? If so, can you give an example or two?
Moses: Monte Carlo currently works with dozens of carriers and InsurTech companies, including Hippo Insurance, a leading insurance and smart home device provider, as well as The Zebra, a home and car insurance comparison site.
Q: Is there any consensus yet as to how ubiquitous data observability is slated to become?
Moses: Nowadays, data-driven decision making is integral to the success of any business, and insurance carriers are no exception. According to SNS Telecom & IT, insurance companies invested $2.4 billion in big data technologies and it is expected to increase to $3.6 billion by 2021, pointing to the increased appetite for accurate, real-time data analytics to power decision making. In the context of the insurance industry, these decisions could be related to anything from the probability of an event occurring to the type of policy to mandate for a given client.