In 2018, Elliot Shmukler and his co-founders left their positions at Instacart with a bold idea – to use machine learning to solve the inherent data-quality problems in large datasets. Five years later, Anomalo’s concept is more relevant than ever as data quality takes center stage with large language models.
A Validated Thesis
Today, the startup announced a $33 million Series B investment, bringing their total raised to $72 million according to the company. Shmukler, Co-founder and CEO of Anomalo, believes that their initial thesis has been validated over the years. "If you’re going to use data to do anything – whether it’s dashboarding, decision-making, or these days to power generative AI applications – then you need a tool that’s actually monitoring that data and making sure it’s correct, of high quality and ready to be used," Shmukler told TechCrunch.
The Growing Need for Data Quality
As companies store increasingly large amounts of data in cloud storage and data warehouses like Databricks and Snowflake, the need for accurate and reliable data has only become more pronounced. "But in a time when everyone is looking to cut costs," Shmukler says, "we came up with a way to limit the data that Anomalo monitors to certain datasets, instead of monitoring everything, to help lower customer bills."
A Cost-Effective Approach
The approach has worked, and Anomalo has grown 15x since their Series A investment. Shmukler indicated that the company’s revenue is now close to $15 million, up from around $1 million in 2021. Furthermore, for the company’s recent fiscal third quarter, he reported that annual recurring revenue grew a staggering 177%, growth numbers we haven’t seen in some time from early-stage enterprise startups.
Finding Balance Between Growth and Efficiency
Shmukler understands that while investors welcome high growth, they also don’t want companies to burn through cash. To find the proper balance between growth and efficiency, Anomalo has set a couple of goals. "Our growth goal was based on percentage growth in ARR," Shmukler explains, "and our efficiency is actually based on burn multiple, which is emerging as one of these efficiency metrics that investors are paying attention to."
A Counterbalance to Growth
"We see that efficiency metric of burn multiple as a kind of counterbalance on our growth," he said. As the company’s revenue grows, Anomalo has been hiring and plans to double its workforce with the new funding.
Hiring and Diversity Initiatives
The company currently has 50 employees and is committed to diversity and inclusion. "We’re excited about the opportunities that this investment will bring," Shmukler said in a statement. "With this additional capital, we’ll be able to continue to innovate and push the boundaries of what’s possible with data quality."
A Bright Future Ahead
Anomalo’s success is a testament to the power of innovative thinking and the importance of accurate and reliable data. As large language models become increasingly prevalent, Anomalo’s technology will play an even more critical role in ensuring that these models are powered by high-quality data.
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