Data has experienced a renaissance over the last 10 years. Companies of all sizes have recognized the value of their data, and invested in making analytics and data science core functions of their businesses. Cloud data warehouses like Snowflake, Redshift, and BigQuery have become the systems of record for analytical data. Around them, an entire ecosystem of data tools and products has grown to comprise the modern data stack we know today.
Many of these new products are data-driven applications that connect directly to cloud data warehouses. They bring tailored data workflows and visualization to different functions and roles within the organization. Traditional business intelligence (BI) tools serving generic use cases for the whole company have been redesigned into workflows purpose-built for customer success agents, growth marketers, product managers, and data scientists.
Analytics is expanding into new forms – into spreadsheet-like financial planning software, drag-and-drop product analytics tools, notebooks for exploratory data analysis, and reverse ETL tools that push data from the data warehouse into SaaS applications. One thing is clear: we’re headed for a future where all teams want to consume data in the tools of their choice.
Data teams in crisis
For decision-makers, these new applications enable new and exciting ways to consume data. But for data teams, they represent an increasing gap between the company’s need for greater data consumption and the teams’ ability to govern it.
Because most traditional BI tools have their own ways to define metrics, the same definitions end up duplicated in multiple places, creating inconsistency and extra work for data teams. Despite best intentions, it’s common to find modern data teams who struggle for consensus on foundational “revenue” or “active users” definitions.
Often, these same data teams become stewards of an ever-growing set of dashboards, reporting, and analytic workflows. Due to a lack of monitoring and testing capability, the messy sprawl leads to mistakes, broken data, and ultimately, costly business decisions.
We know these problems because we’ve seen them firsthand at leading technology companies as data analysts and engineers. We also know that there’s a much better path forward.
Business intelligence, reimagined
While the role of BI is more important than ever, BI software needs a major upgrade.
In a world where decision-makers consume data through many data-driven applications, companies need to standardize metric definitions across different interfaces so that everyone is on the same page. And data teams responsible for business measurement need much better tools for managing shared business logic, metadata, and integrations.
Today, we’re launching Supergrain, our take on a reimagined BI paradigm. It’s an API-first, developer-centric approach to BI that unifies business logic and serves consistent metrics that teams can query anytime, from anywhere. For data analysts, Supergrain provides a simple workflow for developing, testing, and publishing metric definitions. For decision-makers, it delivers source-of-truth metrics for querying and visualization in the tools of their choice.
We call our solution “Headless BI”. With a single, interoperable metrics layer that is decoupled from data visualization, Supergrain is the easiest and fastest way to manage and integrate business metrics across organizations. This is the next evolution of BI and critical infrastructure for all modern data teams.
Where we’re going
At Supergrain, our mission is to enable the next generation of data-driven organizations. We envision a world in which every team member is a data consumer, regardless of function, role, or preferred tool. These teams effortlessly consume data and make decisions via applications tailored to their workflows. The underlying data itself is consistent, easily accessible, and well-governed.
Today we’re launching a public beta version of Supergrain with three key components that lay the foundation for our vision:
- Metrics framework – a flexible, YAML-based metrics definition language and CLI workflow that enables rapid development of metrics.
- Metrics portal – a web application that provides teams a catalog of their published metrics and tools to manage their integrations.
- Metrics API – an open API and metrics query language that powers any data application or workflow that consumes metrics.
If you’re interested in learning more, please give us a try. We’ll be incorporating feedback and improving the product over the coming months.
Finally, we’re thrilled to announce that we’ve raised $6.8M in seed funding led by Benchmark, with participation from Base Case Capital and Operator Collective. We also welcome a group of operator angels who share our vision, including Calvin French-Owen (cofounder, Segment), Benn Stancil (cofounder, Mode), and Spenser Skates (cofounder, Amplitude). Additionally, Benchmark’s Chetan Puttagunta, who led early investments in leading enterprise software companies like Elastic, MongoDB, and MuleSoft, has joined our board of directors.
We have an ambitious and exciting roadmap, and the funding and support will accelerate our progress toward our vision.
If you’re excited to build the future of BI with us – we’re hiring!