We are excited today to share our investment in Iteratively, which we’ve kept under wraps since leading an investment round in the company in November of 2019. Iteratively announced today their $5.4 million of total funding from Gradient Ventures, Fika Ventures, and PSL Ventures.

Our excitement around this company stemmed, in part, from a personal experience when Ben Gilbert was a Program Manager at Microsoft in 2012.

Ben was in charge of the analytics tracking spreadsheet for Microsoft Office for iPad. Like any good PM, he defined the full set of events that the team wanted to track in the product, compiled it into a spreadsheet, and then created work items for each event so that the engineers could implement them in the. It will surprise nobody that as soon as the engineers were done implementing the tracking code, the team thought of new things they wanted to measure, and the beautiful "source of truth" spreadsheet Ben had created quickly became woefully out of date.

Fast forward to 2019 when we met Patrick and Ondrej. We were shocked to hear that in their over 200 customer development conversations, this was still an enormous problem for companies! PMs were still creating the analytics tracking spreadsheet, heaving it over the wall to engineering to implement, and then letting the two get out-of-sync as future versions of the product were shipped. This of course leads to data quality problems, where you can’t trust that your analytics are correct. For any of the analysts or data scientists out there, you know what a pain it is to have to be aware of and account for `User_Logged_In` and `userLoggedIn` in your query.

Iteratively solves this problem. It takes the analytics implementation as seriously as software testing. If you author a bug in your code, the tests stop it from building and making it out the door to customers. Why should proper analytics be any different?

Iteratively takes a proactive and collaborative approach to this problem. PMs, engineers, and the data team collaborate in Iteratively’s web UI to define the schema. This is then downloaded through Iteratively’s SDK into the codebase, and if a tracking event is ever missing or improperly implemented, engineers can see it right where they are working in a text editor or IDE.

This is an enormous problem in the technology industry. According to Gartner research, poor data quality costs businesses in the US more than $3 trillion per year, and analysts and data scientists today waste up to 80% of their time preparing and cleaning data!

Iteratively’s focus on ensuring data quality has made them a trusted partner to organizations like Dribbble, Box, and Artifact Uprising. Their SDK integrates with analytics providers like Amplitude, Mixpanel, or even custom data warehouses so customers can store and visualize the data however they see fit.

The Iteratively team possesses a relentless focus on finally creating the source of truth for analytics data. This trustworthy foundation unlocks countless new data use cases from personalization and recommendation engines to drive growth, churn prediction and prevention to improve retention, and new 1-1 marketing scenarios.

We couldn’t be more excited to be on the journey with Patrick, Ondrej, and the rest of the Iteratively team, along with Gradient Ventures, Fika Ventures, Ascend.vc, and some great angel investors.