Tableau, Looker, PowerBI, and other business intelligence (BI) tools have been instrumental in helping companies extract insights from their extensive data repositories. However, a new entrant has emerged, targeting data teams with a more technical inclination.
Founded in Toronto, Canada, in 2021, Evidence made its debut in Y Combinator’s summer ’21 startup cohort, offering a modern alternative to established BI solutions. While BI tools share common features, they often differ in their target audience. Some, like Google’s Looker, focus on code-based workflows for data ingestion, while others provide a user-friendly drag-and-drop interface aimed at less technical data analysts. There are also tools that blend both approaches.
Evidence takes a code-centric approach, allowing teams to construct data products using SQL and markdown. Moreover, it is entirely open source.
In a bid to expand its market presence, Evidence has recently secured seed funding and is introducing its premium cloud product for businesses that lack the resources to deploy and self-host it.
While drag-and-drop BI workflows are convenient for managing and manipulating data, they may lack the precision and depth offered by manual methods. Evidence’s co-founder and COO, Sean Hughes, explained that drag-and-drop processes often lead to data products that are challenging for end-users to utilize and for data teams to maintain. In contrast, Evidence relies on code for each step, from data sourcing to report definition, which aligns with the preferences of modern data teams resembling software engineers. This approach supports version control, governance, and efficient collaboration using Git, enabling a complete project history. Teams can revisit older product versions and repurpose code as needed.
Additionally, a code-centric approach facilitates continuous integration and deployment (CI/CD) efforts, allowing teams to work on development versions, run tests on changes, and release updates to production through pull requests.
While Evidence may seem to oppose the broader no-code/low-code movement, Hughes sees it as an extension of a distinct trend gaining traction in the analytics field. Data teams increasingly seek to operate like software engineers and are embracing code-driven, open-source products in their data stack.
Hughes drew an analogy with Squarespace, a platform for building websites. While Squarespace serves many users, it may not meet the needs of professional web development teams. Similarly, no-code/low-code reporting tools are suitable for some but insufficient for technically inclined data teams. Evidence aims to deliver an advanced solution for such teams, exceeding the capabilities of no-code/low-code tools.
Open source is a significant advantage for Evidence compared to industry heavyweights like Looker and Tableau. Tools such as Lightdash, Metabase, and Apache Superset compete for the favor of data teams. However, Evidence distinguishes itself with its open source approach and code-based workflow, appealing to businesses worldwide.
After an initial period in early-access mode, Evidence is expanding access to its cloud service through a new invite-based program, supported by $2.1 million in seed funding from A Capital, Y Combinator, SV Angel, and various angel investors. This program includes a free starter tier with up to 5 viewer accounts and a team plan priced at $500 per month, accommodating up to 50 viewer accounts. Customizable plans can support additional enterprise-grade requirements like single sign-on (SSO) and more viewer accounts.
