From Tableau and Looker to PowerBI and beyond, there is no shortage of business intelligence (BI) tools designed to help companies unlock insights from massive sets of data. But a new entrant has arrived on the scene with some new BI intelligence, aimed at more “technically inclined” data teams.
Founded in Toronto, Canada, in 2021, certificate Featured from Y Combinator (YC) Summer 21 group of startups With the promise of a modern alternative to popular BI incumbents. In fact, although many BI tools share basic characteristics, they often differ in terms of… from They target: Some offer more code-based workflows for data ingestion like Google’s Looker, others offer a drag-and-drop interface aimed at less technical data analysts, and others offer a combination of both.
Furthermore, business intelligence software comes in a variety of proprietary and open source flavors, factors that can influence which tools a company wants to deploy.
The guides, for their part, approach things largely from a code-based point of view, enabling teams to build data products using SQL and markdown. Moreover, it is Completely open sourcePreface.
Looking to expand its brand footprint, Evidence today announced that it has raised a tranche of seed funding and unlocked its premium Cloud product For companies that lack the resources to self-publish and host guides.
Drag and drop drop
Drag-and-drop workflows in business intelligence have their place, insofar as they allow data teams to more easily manage and manipulate their data. But this may lack the complexity and detail that manual methods provide.
“This process of creating drag-and-drop reports is fine for many data teams, but very painful for more technically inclined data teams,” Sean Hughes, Evidence co-founder and COO, told TechCrunch. “It leads to data products that are very difficult for end users to use, and for data teams to maintain.”
Within Evidence, every step—from defining data sources to defining reports—is done using code. According to Hughes, this is preferential for many modern data teams who prefer to work like software engineers. For example, it supports version control and governance – users can manage entire workflow and team collaboration using Git, and can create a complete and accurate project history. This also means that they can revisit old versions of the product, cut/copy/paste old code and reuse them.
“Most BI tools are full of outdated, broken, and irrelevant reports because creating something, and then moving parts of it somewhere else, takes too long,” Hughes explained. “As a result, you don’t want to throw things away. That’s not the case with evidence.”
Furthermore, the code-based approach also supports teams in their broader continuous integration and deployment (CI/CD) endeavors.
“You can work on a development version of your project while making changes, run tests on those changes, and release the updates to production through a pull request,” Hughes explained.
In some ways, Evidence may seem like a pushback or “resistance” against the broader no-code/low-code movement, but Hughes believes his company acts as an “extension” of a separate movement that has been gaining traction in analytics. space.
“Data teams increasingly want to work like software engineers, and are starting to adopt code-driven — and open source — products into their data stack,” Hughes said.
One analogy Hughes uses to emphasize this point is that of Squarespace, the billion-dollar giant that helps almost anyone build their own website. It certainly serves a purpose for millions of people, but it’s not suitable for every scenario.
“No-code and low-code reporting tools work well for many people, but are too limited for more technical data teams,” Hughes said. “It would be like giving Squarespace to your front-end web development team. Squarespace works great for a segment of users who need to set up a simple website, but a professional developer will want and need to do more. We’re focused on building something amazing for technical-oriented data teams that need to go beyond What is possible in a no-code or low-code tool.”
Open source is also a major selling point compared to industry heavyweights like Looker or Tableau, with the likes of Lightdash, Metabase, Apache Superset (Which he has A VC backed business entity as well) compete for the affections of data teams. Most of these tools, according to Hughes, look very similar to Tableau or Looker, except that they can be self-hosted. This is a huge benefit in itself, of course, as companies can retain full control of their data, but this approach is open source sum With a code-based workflow that Evidence hopes will attract companies around the world.
After a long period in early access mode, Evidence is now also expanding access to its cloud service to a wider audience as part of a new invitation-based program, backed by $2.1 million in seed funding from A Capital, Y Combinator, SV Angel, and a raft of angel investors. .
“Everyone on our current waiting list will have access to Evidence Cloud,” Hughes said. “We’re moving from a waitlist to invitation-only, where anyone with an invitation can access. Invitations can be received from existing Evidence Cloud customers, or directly from an Evidence team member.
The Cloud plan includes a free starter tier that includes up to 5 “viewer” accounts (i.e., end users), along with a $500-per-month Team plan that includes up to 50 viewer accounts. Additional enterprise-level requirements, such as single sign-on (SSO) and more viewer accounts, can also be supported as part of a customizable plan.