Today we officially announce that Iteratively has secured $5.4 million in new capital 🎊 We’re extremely excited and proud to have Gradient Ventures, Google’s AI-focused venture fund, Fika Ventures, and early investors PSL Ventures and Essence VC join us on our journey. Our investors took a leap of faith in us and share our vision for where we’re headed.
How did we get here?
Ondrej and I didn’t come up with the idea for this product overnight. As long-time friends and teammates, we spent six months doing extensive customer research before starting Iteratively in June 2019. Ondrej had a past successful exit with Syncplicity (acquired by EMC) and I had spent the past four years leading teams as a Design Manager at Atlassian. We went into the research phase with an open mind and lots of exploratory questions.
We interviewed over 400 product managers, data teams and engineers, and found the number one pain point was bad data. This pain was universally felt and many teams were struggling to keep up having to spend countless hours trying to make sense of heads or tails. We finally had heard enough (and experienced this pain ourselves!) and knew there must be a better way.
We were very lucky to have early customer adoption and a group of teams who were so excited about the problem we were solving that they were willing to work through early MVPs. A special thanks to the teams at Box, Artifact Uprising, Beekeeper, Dribbble, Octopus and many more for bearing with us since the early days. Having early customers meant we could iterate quickly, ship fast (early 😅) and get rapid feedback on what we were building.
Even today, we spend heaps of our time on customer development, speaking to data teams, product managers and engineers who are willing to share their data challenges with us.
What’s equally exciting is that it’s not just Ondrej and myself on this journey anymore; we have a great team with us, equally passionate about creating value for our current and future customers by helping them build back trust in their data. We have several open roles if you’re looking for your next adventure.
Why data (quality) matters
We’ve all heard the numbers thrown around: “poor data quality costs businesses in the US more than $3 trillion per year” and “analysts and data scientists waste up to 80% of their time preparing and cleaning data”. Big numbers, yes, but they feel empty and sort of intangible.
What we saw talking to teams matched our own experiences working at companies like Atlassian and Microsoft: data and product teams have lost confidence in their data. They simply don’t trust it. This lack of trust doesn’t happen overnight, it erodes over time as poor processes, inadequate tooling and basic human error gets in the way. Teams often call this out as their biggest challenge, but for others it’s an accepted reality that sure, we’re capturing all this data, but we know we can’t rely too much on it. We built our own solution at Atlassian to solve this exact problem, and wanted to solve it for everyone else once and for all.
Ten years ago, data accuracy and completeness didn’t matter much. Being just directionally correct was okay. Many teams didn’t use data to make decisions or power data products. Sampled data and simple dashboards were the norm. Heck, modern data teams haven’t been around for all that long. Well, all that has changed now.
It’s clear from pioneers such as Amazon, Netflix, and disruptors in every established industry, that data has become a company’s most valuable business asset and what will differentiate successful companies and products going forward. Data use cases have moved way beyond reporting: personalization and recommendation engines to drive growth, churn prediction and prevention to improve retention and AI research that will change the world as we know it. Once you’re creating 1-1 user journeys or embedding data in your product, you can’t rely on data that’s 90% accurate. You just can’t.
Doubling down on a collaborative and quality-first approach to analytics
While there are a plethora of data tools in the market today, Iteratively is taking a proactive and collaborative approach to solving the problem of poor data quality. We help teams ensure that data quality issues are caught before ever making their way into production. Solving and preventing issues proactively and at the source means data consumers have high quality and trustworthy data that’s ready for use, with no cleaning required.
We believe that data is a team sport and so we’ve architected Iteratively from the ground up for collaboration, building tooling and workflows that work for all the relevant stakeholders. Iteratively brings product managers, data analysts and engineers together to empower cross-functional teams to collaborate on their analytics, while ensuring the quality of the data - and the governance around it - remains high. Most tooling today is either made exclusively for product managers, engineers or data analysts and this creates friction across teams, often leading to erosion of trust.
“Data is very much a team sport - it involves almost every team in a data-led organization. That said, there’s very little tooling out there that’s built for both non-technical teams like product and marketing as well as engineers and front-end developers. With Iteratively, we have built a platform and workflows where all the relevant stakeholders can work together to ensure the data they collect is accurate, complete and meaningful.”
– Ondrej Hrebicek, Co-founder and CTO
Iteratively enforces a reliable source of truth for teams’ analytics and is complementary to teams’ existing stack, integrating directly into their existing infrastructure, whether that’s custom-built or third party tools like Amplitude, Mixpanel, Segment, Snowplow, dbt and more. And for enterprises like Box, we help them enforce data governance by preventing PII from leaking or being captured in the first place.
And where are we going?
With the latest funding, we’re doubling down on a collaborative and quality-first approach to analytics. We have an exciting journey ahead of us and a clear vision for how Iteratively can help companies solve the problem of bad data once and for all. We’ll be growing the team further, investing heavily in product and engineering while also hiring for customer success, sales and marketing.
We’re striving to become the best-in-class data collection platform for modern data, product and growth teams. We believe that providing teams with an easy way to collaborate while ensuring the integrity of their data will empower all organizations to finally leverage their data as a competitive advantage.
We’re proud of the customers, team members and investors who believe in us and look forward to a thrilling 2021 🚀. Please join us on our journey by creating a free account today and stay up to date with us in the Iteratively Community Slack.