Introducing the Next Class of Data Reliability Pioneers
They range from SMBs (small/medium businesses) to the Fortune 50. They span industries such as aviation; food and beverage; financial services; media; security; human resource management, and more.
What they all have in common is a dedication to providing the highest quality data to their internal and external customers.
Last year, we were thrilled to renew 100 percent of our customers, a testament to the enthusiasm and excitement around the data observability category – and our mission of accelerating the adoption of reliable data.
We’re honored to work with the following data teams and many others who are forging a path to data reliability and raising the bar for what it means to be data-driven.
JetBlue is New York’s Hometown Airline®, and a leading carrier in Boston, Fort Lauderdale-Hollywood, Los Angeles, Orlando and San Juan. JetBlue carries customers across the U.S., Caribbean and Latin America, and between New York and London.
JetBlue’s data team manages all the company’s data from bookings to flight times. Without end-to-end coverage and visibility, the team had to go to extraordinary lengths to fix issues at all times of the day, including weekends and holidays. They even had an “eyes on glass” team manually refreshing dashboards to ensure smooth operations.
“Analytical data is the lifeblood of JetBlue’s system operations and customer support, ensuring smooth and seamless experiences for travelers around the world. With Monte Carlo’s broad coverage and automated lineage, we can be confident our dashboards are accurate without manually monitoring,” said Ashley Van Name, general manager of data engineering, JetBlue. “Our team can identify, understand downstream impacts, prioritize, and resolve data issues at a much faster rate.”
Fox Networks produces and distributes content through some of the world’s leading and most valued brands, including: FOX News, FOX Entertainment, FOX Sports, and the FOX Television Stations.
Data observability is essential for the Fox data team to achieve data quality at scale. The digital organization receives data multiple times a day from over 200 sources. They process nearly 10,000 schemas and tens of billions of records per week.
“You can’t scale the team to maintain and support and validate and observe that amount of data. You have to have at least a few tools at your disposal. For us to make sure that we have trust in the data’s timeliness, completeness, and cleanliness, tools like Monte Carlo are “must-to-have,” said Alex Tverdohleb, VP of Data Services, Fox Networks. “It’s been a great addition to allow us to build an AI-powered overview of what’s happening in our data stack.”
“Data observability has become a necessity, not a luxury, for us. As the business has become more and more data-driven, nothing is worse than allowing leadership to make a decision based upon data that you don’t have trust in. That has tremendous costs and repercussions.”
Red Ventures (RV) is home to a wide portfolio of growing businesses and trusted brands, including advertising agency Red Digital, that provide expert advice at scale. RV publishes more than 130,000 pieces of content per year, receives 750 million+ unique website visitors globally per month, and employs more than 4,500 people across 5 continents.
Data quality and timeliness are critical for Red Digital as it impacts their paying customers.
Monte Carlo helped Red Ventures reduce the amount of time its engineers spend on one-off data quality requests, which for some teams, were more than 50% of all requests. The data observability platform has also helped provide the insight into data quality metrics and ownership its data leadership needs to implement best-in-class data service level agreements.
“We implemented Monte Carlo in October 2021 so when data goes bad–a table breaks, data shifts abruptly or some other reason–a monitor lets us know,” said Brandon Beidel, director of product management, data platform, at Red Ventures. “It also helped us optimize our time and resources. For example, maybe 6 of our 7 warehouses are running smoothly, so let’s take a closer look at the one that can be improved.”
tastytrade Inc., a leading financial network for futures and options trading, consists of tastytrade, the financial media network; tastyworks, the trading brokerage; and several other corporate entities.
Data is a competitive advantage for the network as they can leverage it to understand how content consumption is related to activity on their trading platform. The tastytrade data team needed to scale and run at the same breakneck speed as the rest of the fast-growing company.
“Monte Carlo’s ease of use really stood out. It was a huge benefit to have it set up and start working automatically as opposed to having to manage a full infrastructure that would require a full time data engineer dedicated to it. It’s advantageous to have monitoring, documentation, and lineage all wrapped together in the same cohesive UI instead of hopping from tool to tool,” said Alex Welch, vice president enterprise data and analytics, at tastytrade.
“A former basketball coach told me, ‘it’s not about going full speed all the time, you need to be able to change speeds at the right place and time,’ and that’s what Monte Carllo and this stack allows. We can move as fast as we want without feeling like we’re being held back by technology, but if something breaks or requirements change, we can pivot if we need to.”
Dr. Squatch is a leading men’s personal care brand that crafts high-performance products with only the finest natural ingredients. Reliable and up-to-date analytics are foundational to their ability to deliver great products to millions of customers worldwide.
Nick Johnson, VP of Data, IT & Security at Dr. Squatch, and his team are responsible for building data products that power product and customer analytics, marketing initiatives and data science across the company. Dr. Squatch’s data infrastructure leverages Snowflake, dbt, and Looker to support these efforts. To ensure that this infrastructure and the decisions it informs are accurate and reliable, Nick turned to data observability with Monte Carlo.
“Monte Carlo gives us end-to-end visibility into our data infrastructure with automated incident detection and alerting, freeing us from having to write thousands of data tests by hand. Their solution is particularly helpful for monitoring ETL pipelines and identifying cases where data is flowing, but something looks off (for instance, distribution anomalies in a key field or errant schema changes),” said Nick. “This ‘insurance’ lets us know when data breaks before it impacts downstream users, saving us hours of troubleshooting and giving our stakeholders greater confidence in our data. With Monte Carlo, we have greater peace of mind and a happier data team.”
SoFi is a personal finance company that provides loans, mortgages, credit cards, investing and other banking products. Their mission is to help people reach financial independence to realize their ambitions.
The SoFi data team underwent an in-depth vendor evaluation process before determining that Monte Carlo was the best solution to accelerate their efforts to scale their data quality standards.
“As SoFi scales our data tech stack, it’s critical that the data powering decision making and our digital products are reliable and trustworthy. Monte Carlo has given us tools to build out a data quality program that will provide consistent and reliable data to our consumers,” said Dan Delorey, Head of Data, SoFi. “Data Observability can make it easy to monitor for, identify, and resolve data issues as they arise, while offering a holistic view of data latency and pipeline health through automated lineage.”
PayJoy, a pioneering credit provider to under-served consumers in emerging markets worldwide, is a fast-growing company that gives customers the ability to afford their first smartphone on credit.
With PayJoy’s explosive growth, its data ecosystem has gotten more complex and the need for data reliability has grown. The data team began investigating data observability solutions to increase data trustworthiness across the company.
“Before we started using Monte Carlo, it was difficult to know where to start resolving data downtime and who would be impacted by these issues downstream,” said Trish Pham, Head of Analytics, PayJoy. “Monte Carlo has allowed our team to provide more value to the business, giving us confidence in our data strategy and peace of mind in the integrity of our data. We were able to implement end-to-end data observability out-of-the-box and scale beyond manual testing.”
Gusto’s people platform helps businesses take care of their hardworking teams. Launched in 2012 as ZenPayroll, Gusto serves more than 200,000 businesses nationwide.
The data team needed an automated, end-to-end solution that would alert them to data incidents, but was smart enough to avoid flagging false positives from reconciled tables and hourly schema changes.
“To deliver on our mission of making HR and operations easier and more scalable for growing companies, Gusto needs reliable and trustworthy data, from ingestion in the warehouse to our executive dashboards” said Shanshan Wu, Technical Product Manager, Gusto. “Monte Carlo’s end-to-end data observability has enabled us to have better visibility and deliver better services to our customers. This 10,000-foot view into data health has been a big win for the business.”
Kargo is a mobile-first advertising company that creates consistently innovative campaigns for over 200 of the world’s best-known brands.
It’s a data driven company that needs accurate data to fuel its systems and machine learning models.
“Kargo is committed to creating consistently innovative marketing campaigns for over 200 of the world’s best known brands. Critical to our vision is the ability to leverage real-time, accurate data to drive insights and power our data platform, which is where data observability comes in,” said Jose Cabal-Ugaz, Data Technical Product Manager, Kargo.
“Monte Carlo’s end-to-end data observability solution allows us to improve data reliability behind the scenes while reducing the burden on my data team to manually test and validate data pipelines. Now, we’re the first to know and solve data issues when they arise, and have the tools to troubleshoot in a quick and effective way – before they affect downstream systems and machine learning models.”
Choozle is a leading digital advertising software company that gives small and medium-sized businesses access to enterprise grade advertising technology.
The data engineering team decided to proactively evaluate data observability solutions in advance of a major platform upgrade with a powerful unified reporting capability, which allows users to connect external media sources.
“We had previously built a lot of custom monitors to see if cron jobs were failing with alerting and escalation through PagerDuty at a job level, but what stuck out to me was Monte Carlo had such a tight integration with Snowflake that it just auto-discovers every possible issue with our data. The setup was crazy easy for the coverage we got. I didn’t see any competitors that could serve our needs like Monte Carlo does,” said Adam Woods, chief client officer, Choozle.
“Without a tool like this, we might have monitoring coverage on final resulting tables, but that can hide a lot of issues. You might not see something pertaining to a small fraction of the tens of thousands campaigns in that table, but the advertiser running that campaign is going to see it. With Monte Carlo we are at a level where we don’t have to compromise. We can have alerting on all of our 3,500 tables.”
Interested in joining the data reliability movement? Get started with data observability by reaching out to Will and the rest of the Monte Carlo team!