Data Engineering is Making Relying on Gut Decisions A Thing of the Past
Salesforce Ventures places another bet on the continued, massive growth of data and invests in data observability startup Monte Carlo.
Years ago, companies were led by executives who made decisions based on “gut feel.” If that’s hard to believe, chances are pretty good that you run your business based on data — as much of it as you can get your hands on. And, chances are, the more data-centric your business, the more important it is to you that your data is accurate and can be trusted.
But there can be dozens of touchpoints between you and the original data source, and mistakes can come from anywhere in the organization. The only way to know whether you can rely on what your data is telling you is to proactively monitor the health of your data flow so you can course-correct if something goes wrong — because when it does go wrong, businesses come to an expensive, screeching halt. According to an IBM study, poor data quality costs the US economy up to $3.1 trillion yearly.
At Salesforce Ventures, we believe every single company that wants to make data-driven decisions will need an effective, proactive approach to solving this problem. That is why we are proud to announce that Salesforce Ventures is joining Accel, GGV Capital, Iconiq, and Redpoint Ventures in investing in Monte Carlo.
Digital transformation = data, data, and more data
It’s hard to overstate the massive worldwide explosion in data. More than 59 zettabytes (ZB) of data will be created, captured, copied, and consumed in the world this year, according to a Global DataSphere report from International Data Corporation. The report predicted that the amount of data created over the next three years will be more than the data created over the past 30 years.
As our personal and professional lives move online, and as businesses continue to migrate to the cloud, more and more data will be generated, stored, and analyzed — and managing it all is getting increasingly complex. Business leaders are now looking to use data to make decisions across every function in the organization. To do so, they need access to trusted data. Yet as the applications ingesting, storing, processing, and modeling data become increasingly siloed and complex, the opportunity for error grows.
If someone in the company finds an error in a report that is caused by bad data — say, only 28 days out of 30 were included in a monthly sales summary, or a customer calls to complain about incorrect data in a product dashboard — suddenly everything is in doubt. People wonder: “If this chart is wrong, what other charts are wrong?”
In the same way that New Relic, Datadog, and other performance management solutions ensure reliable software and keep downtime at bay, Monte Carlo solves the problem of broken data pipelines and data downtime — periods of time when data is missing, inaccurate, or otherwise erroneous.
Monte Carlo tracks and observes data as it progresses throughout its life cycle, using machine learning to analyze the data, ensure that quality is up to par, and track relationships between data assets. Their system proactively identifies data downtime and notifies those who need to know, while also making it easier for non-engineers to get answers when they need them. Since its launch in 2019, Monte Carlo has helped data teams reduce infrastructure costs by 3X, save 120 hours per week and decrease the number of data incidents by 90%.
Data as a competitive advantage
Monte Carlo’s mission is to accelerate the world’s adoption of data. Making sure data can be trusted is a core brand promise. Its customers include a number of companies for which data is a competitive advantage and data integrity is existential, such as Affirm, Fox, Intuit, Nerdwallet, PagerDuty, and Vimeo.
The company’s founders each came from leading software companies where data is used as a competitive advantage. Their expertise comes together in a unique way to solve a growing problem they each personally experienced around monitoring valuable data flows. Barr Moses, Co-founder, and CEO was previously VP Customer Operations at Gainsight, where she built the company’s first data team; Lior Gavish, Co-founder, and CTO was formerly SVP Engineering at Barracuda, where he built fraud detection systems powered by data, machine learning, and analytics.
“In 2021, it’s not enough to use data to drive accurate decision making; you need to trust it, too. If you’ve been on the receiving end of bad data, you understand the pain: lost revenue, wasted time, and most importantly, lost confidence,” says Moses. “Monte Carlo enables teams to stop bad data before it impacts the business.”
“As companies become more data-driven, it’s fundamental that organizations not only understand the health of their data but also have the data observability necessary to trust it from end to end,” says Gavish.
The data management landscape is ripe for disruption. Solutions abound to help companies manage where data comes from, where it goes, how it moves around, how it gets prepared and how it gets used. At Salesforce Ventures, we are bullish on companies that solve data problems. We hope you will join us in welcoming Monte Carlo, and if you are building something in this space, we hope to hear from you.
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