Originally published on the Two Sigma Ventures Blog on April 26, 2023
Anomalo Founder & CTO Jeremy Stanley met his co-founder & CEO Elliot Shmukler, when working at Instacart and bonding over their shared love of data.* The difficulty of ensuring their data was of high quality led them to found Anomalo, a no-code platform for validating and documenting all the data in a company’s warehouse.
We spent time with Jeremy to understand their vision for Anomalo, the most important reasons that data quality matters for businesses, and advice for entrepreneurs to build a smart data-first organization.
Driven by a desire to understand how humans interact in complex systems
Jeremy’s first love was math, which led him to go deep into math research. Later, he prepared to apply for a degree at Cornell University, where he was met with the daunting task of writing an essay about what he wanted to do with his life.
He set out to learn about predictive modeling, neural networks, and machine learning as ways to predict behavior in data. That launched him on his career path, first in insurance–which Jeremy describes as “a natural place to start if you’ve got a deep understanding of data and want to predict people’s behavior.” He tried his hand at an adtech, then ultimately got into personalization, and then logistics routing at Instacart.
When he joined Instacart, the company had around two hundred employees and was growing very rapidly. But it was also losing a lot of money for every delivery, presenting an exciting opportunity for the right data capture to translate to insights and opportunities for optimization, to be able to achieve that efficiency gain. Jeremy spent his first year or two driving efficiency improvements, then rolling out algorithms for optimally routing shoppers in the grocery delivery process and understanding inventory at the store locations in a more predictive way. Elliot, Jeremy’s co-founder at Anomalo, joined as the Head of Product, then evolved into the Head of Growth–roles where the two worked closely together to try to figure out how to help make Instacart a unit economic profitable business, by leveraging new modern algorithms and data-driven approaches.
Jeremy and Elliot saw eye to eye on technology, philosophy and strategy–which Jeremy describes as the “core important things in running a business” – and they decided to form a partnership and started to whiteboard ideas for what their startup could focus on.
Data quality was one of the ideas that stood out to them as needing a better solution in the market, and they had the right skillset to tackle it and felt they could get to something that customers would find valuable quickly. The two got to building that right away, right after their second meeting.
Overhauling historical approaches that don’t work with the way we use data now
Data quality was a big sticking point Jeremy and Elliot witnessed throughout their careers. “It was not uncommon for the very best analysts to spend half of their time fighting data quality issues. And every time any meaningful insight about the business was raised, there was always doubt and speculation as to whether or not the data itself could be trusted, underneath that insight,” Jeremy reflects.
They recognized a timely convergence of two trends: 1) the movement to the cloud and to cloud data warehouses like Snowflake, BigQuery and Databricks, that make it easy for organizations to make all their data available in a single queryable environment, and 2) the increasing literacy and use of data by everyone throughout the organization, from product to operations, marketing, finance, and beyond.
This led Jeremy and Elliot to create Anomalo as software with intelligence integrated into it, that could perform that type of monitoring easier and faster, letting users know about data quality issues before their customers found out about them.
So how does it work?
Anomalo is building machine learning algorithms that can learn whether there are seasonality patterns or types of chaos that typically happen in that dataset and identify anomalies (yes, Jeremy confirmed, the inspiration behind the name ‘Anomalo’). The other dimension of their offering is around visualization and reporting on data quality issues.
Navigating not only the technology, but also change management dynamics
When starting Anomalo, Jeremy’s role first focused on heads-down building, but once the product had launched, he started spending more time writing posts about what they built. Now, he’s writing an O’Reilly book as an opportunity to tell that technological story end-to-end and summarize what they have learned to help organizations go through the same kind of transformation process that some of Anomalo’s earliest customers are experiencing (first chapter can be previewed here).
From a change management perspective, he shares the two things he believes are most important:
- Who you hire, and making sure to hire people who can demonstrate at the very least, a deep respect for the use of data to make decisions or to improve products, and to “look for that as a value and a skill that you try to select for across the organization–to try to stack the deck organizationally,” and
- To avoid compartmentalizing ownership over the data, which breeds sentiments like “it’s someone else’s problem to do that instrumentation or to deal with the analytical limitations of an issue.” Instead, Jeremy recommends you need the engineers, product owners, and others to feel ownership of the data. “If the person making product decisions and interpreting A/B tests doesn’t feel like they can have the ownership to make the strategic decisions around what data should be collected upfront and how you’re going to be in trouble.” Jeremy shares how his favorite organizational model is when you have data professionals tightly integrated cross-functionally, reporting into the same leader that’s responsible for a component of the product or a part of the platform, to make it known that data is a shared responsibility rather than something outsourced to a dedicated team.
On the horizon for Anomalo
What’s next for Anomalo?
Though Anomalo’s start was with solving for data quality issues in the context of data scientists, machine learning engineers, analysts or product owners, now Anomalo is working towards ensuring they can meet the needs of the data engineer who cares about the flow of the data through all of their systems. They have some exciting announcements coming up around product enhancements there soon, and are also hiring for a role on their data platform team–the team that owns the integration layer between the business logic and machine learning they do at Anomalo, and the data warehouses that their customers depend upon.
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