Problem

Analytics are hard to get right – especially for complex business models (like API-first SaaS).

Building a flexible analytics stack is typically a challenging, non-trivial project. Everyone wants accurate, comprehensive data that can be easily consumed by teams across the company, but getting there hasn’t always been easy. This often requires a team of data engineers to build, test, and monitor a custom ETL (extract, transform, load) pipeline that cleans, aggregates, and loads data from disparate data sources into a single data warehouse. Once the pipeline is in place, it’s not as simple as “set-it-and-forget-it.” Brittle ETL scripts often break, data schemas frequently change, and databases in the warehouse need version upgrades or run out of disk space. Not to mention the fact that a small army of SQL-proficient analysts is often needed to make sense of the data, building dashboards and visualizations for executives and other teams to consume. This presents a significant challenge for large companies with near-infinite resources, whereas it becomes nearly impossible for small, lean teams with limited time and resources. Clearbit is no exception.

As a demand generation marketer, I'm constantly searching for the right mix of ad platforms and publishers to bring in leads. But when each platform reports on different performance metrics, in different formats, with varying levels of automation, aggregating metrics quickly becomes a nightmare. I've spent countless hours copying and pasting across files, formatting cells, and writing complex Excel formulas to report on paid performance.

From the start, Mode's goal has been to make data more accessible. When analysts and their business partners work together on one data platform, everyone is empowered to explore data in ways they're comfortable with. As analysts build and share SQL-powered dashboards, business partners can self-serve answers. Analysts are freed up for ad hoc analysis that inspires more questions, and the cycle continues.