The importance of trustworthy data — why we invested in Snowplow

As a fund we’re bullish on the transformational impact that AI is having on the Enterprise. Having a high quality, trustworthy data foundation that underpins analytics and AI technologies feels fundamental. This is why we love Snowplow.

Snowplow enables companies to build a high quality customer data set across multiple engagement channels including web, mobile, email, support desk and connected devices. The tech is differentiated by the quality of the data delivered: the accuracy, completeness, richness, granularity and well-defined structure.

As a result, Snowplow data is “business-ready”: analysts and data scientists do not have to spend time cleaning and tidying the data before drawing insights from it. They can plug it directly into AI and BI use cases.

The data pipeline technology at the heart of Snowplow has been available via open source (free for developers to use) for a number of years and the brand is well known throughout the data science community. This tech is deployed within over 600,000 domains and apps and used widely by data teams across the globe, with 60% of these deployments in the US.

This broad adoption has resulted in the technology being used across many use cases. Typically, Snowplow has been deployed in companies that engage with their customers via digital channels (websites, apps) and have data teams that take customer data quality and ownership seriously. Today, the business is focusing on three target sectors to scale: Media, Retail and Travel.

Take the Travel sector, where Snowplow has built an impressive client base (inc. Secret Escapes, culture trip, Tourlane, Omio, Mr & Mrs Smith), and the product delivers significant business value in a number of common use cases:

Enabling a holistic view of the end-to-end customer journey — cross-device and post-purchase (which is vital for insight into cancellations);
Enhancing attribution and providing better visibility on effectiveness of marketing activities — something Peak AI, another of our portfolio, is also driving at in the AI layer;
Real-time access to customer behavioural (event-level) data and the required flexibility for continuous product enhancement via A/B tests; and,
Personalising user experience, leveraging behavioural data, in real-time (a capability not possible with other solutions).

Add the features coming down the roadmap and you have a compelling proposition for any data team in the space.

The Snowplow Pipeline

Having spoken to Snowplow clients, sales prospects and data science teams across our portfolio there were some universal themes that came out of the conversations – Snowplow is loved because of its focus on data quality, ownership and control, all elements lacking from other propositions in the space.

Snowplow Insights is the future

The open source heritage is an incredible asset and the product is loved by technical users that like to tinker before using. The technical nature of the open source products requires users with a certain level of technical competence. The Snowplow Insights product has made the tech more accessible, driven in part by the introduction of a UI designed for data teams.

The initial commercial product was an advanced, automated managed service proposition, which was quickly adopted by clients as they understood the benefits of outsourcing this resource intensive task.

Over time, the product has evolved into a more feature rich upgrade to the open source technology with a UI that enables a range of data governance and data quality features. This is the product that we are excited about — Snowplow Insights offers the Snowplow data pipeline as a service with SLAs and a UI and API to enable data teams to:

Govern data collection: inc. definitions of all data collected and the evolution of them over time;
Manage the data delivery: inc. adding new enrichments, destinations and custom data processing steps; and,
Measure and monitor pipeline performance: inc. proactively identifying data quality issues and actioning quarantined data.

Snowplow Insights — user friendly data governance at your fingertips!

Time to shine

After getting to know the company over the past two years, it was clear that now is the right time for Snowplow to raise funding and initiate a phase of rapid growth having successfully bootstrapped growth at over 100% y-o-y up until this point. The market is maturing quickly, and data governance is now a Board level concern — this has resulted in an increasing volume of inbound interest. In addition, the product now offers a compelling upgrade to open source users across the data science community which, with a little bit of education, could result in a huge growth opportunity for the business.

Winning team

Venture is ultimately all about backing world class teams and this investment is no different.

We first got introduced to Snowplow when Tim, the COO, moved from Gousto (the UK recipe box rocket ship — an MMC portfolio company and Snowplow user) to join the team. Over time, we got to know the co-founders Alex and Yali and quickly became incredibly impressed with technical expertise and relentless product focus — all clearly evident in the quality of the open source product.

During the investment process, we sat in on some sales and customer success calls and spent time with a number of team members- the outcome of which was a feeling that the team has developed a special culture. The team are thoughtful, open and challenging and we are excited about working together on the adventure ahead.

We spent a fair bit of time focusing on the Go To Market strategy and evolving the thinking on the required capabilities and team members — which resulted in agreement that the team will be adding a VP Sales, VP Marketing and VP Customer to the management team in the near future. Exciting stuff.

As a fund, we’ve recently made a number of investments in the DevOps / DataOps space – Tyk, Cloudsmith and StorageOS — as this is an area where we see the potential for tremendous value creation.

When talking to prospective Snowplow clients it quickly becomes clear that the best place to start with data governance, quality and performance is at the foundational layer of the stack.