Datafold enables data teams to deliver high-quality data products while increasing the speed at which they iterate.
There’s a dirty secret that most data practitioners know but don’t often say out loud: most data is of insufficient quality to draw reasonable conclusions or use in customer-facing products. While data teams may have access to petabytes of data, they’re often too anxious to share insights lest bad information leads to bad decisions that make executives lose confidence in data and revert to intuition-driven leadership.
Some data teams address this problem by implementing core data models for which they expend considerable time and energy to provide stronger quality guarantees. However, as organizations expand and the number of data consumers increases, demand for data also escalates and maintaining a growing number of core data models becomes increasingly tedious and challenging. As a result, data teams often find themselves at an impasse: they can stop innovating and focus only on sustaining the quality of their existing core data models or they can try to keep pace with the business, knowing that they might deliver faulty intelligence to their stakeholders.
We invested in Datafold because they help data teams navigate this impasse, thereby maximizing utilization of their data and reaching their full potential. Datafold enables data teams to deliver high-quality data products while increasing the speed at which they iterate.
I almost lost faith in the untapped potential of Data teams before I met Gleb Mezhanskiy. As a data practitioner, I witnessed how challenging it is to move beyond “directionally correct” data to data that is precise enough to inform strategic and tactical decision-making. While I strongly believed that data quality improvements were key to building data-driven organizations, I saw most data observability tools cripple their users by forcing them to resolve incidents that could have been prevented and dismiss an exhausting number of false positive notifications. After looking at dozens of products that promised to help data teams improve data quality, I still couldn’t see a way for them to move as fast as their counterparts without breaking everything.
But then Gleb introduced me to proactive testing and data reliability engineering. Gleb has worked in different data roles at companies of all shapes and sizes for about a decade. In these positions and by interfacing with different types of data producers and consumers, Gleb identified a path to principled data product development. When you meet Gleb, you can immediately sense his intelligence and execution skills. After speaking with him for just a few minutes, his commitment to helping data teams and their stakeholders work better is also obvious.
In our first conversation, Gleb shared how the same engineering principles that enable developers to build quickly and build well could be applied in the data domain. Unlike existing data quality tools, Datafold offers tools that integrate seamlessly into analytics engineering workflows, minimizing the amount of work required to improve data quality so users can release more reliable data products fast. In addition, Datafold catches data quality issues before they occur – so data consumers aren’t impacted, and data producers need not spend time investigating and resolving incidents.
Gleb completely convinced me of the need for data reliability engineering to give data teams freedom under responsibility. Nonetheless, my belief in Datafold’s approach dramatically increased after speaking with Datafold customers. Unlike many data practitioners, who feel overwhelmed by the demands they face, Datafold users are energized and even more ambitious than ever. They feel confident about shipping faster, knowing that any data bugs can be detected before impacting customers. They are empowered to respond to stakeholder demands quickly, knowing that they can easily answer questions about surprising insights. They can embark on bigger, riskier projects, armed with intelligence on how data gets used.
Datafold users are emboldened by a future in which data can match or even exceed software’s impact on the world. We are too, and we could not be more excited to invest in the missing link in the modern data stack. Congratulations Gleb, Alex, and the Datafold team, on your $20.0M Series A.