The modern data stack is on the rise. Many companies use raw data from their SaaS analytics tools as input for their data warehouse, but this introduces problems downstream. Are there better ways?
The tracking plan. Every company that’s serious about analytics has one, but they are notoriously difficult to properly set up & execute. What if you didn’t need one?
With this release we now provide a Tracker SDK for React Native, so you can use Objectiv's advanced modeling on your mobile app's user behavior data as well.
Analytics tracking instrumentation shouldn't cost you a lot of time. So we've put a lot of effort into ensuring that it's incredibly simple and quick.
We've released Objectiv v0.0.15, which enables validation of data at the first step of the pipeline: the Tracker.
We've released v0.0.14. This release adds several new pandas-like date & time operations to the Bach modeling library that provide more granular control when wrangling/exploring time-related data.
We released v0.0.13, which adds a
to the open analytics taxonomy, enabling analysis of marketing & product performance straight from your
As part of our mission to enable effective data science in the product analytics workflow, we’ve completely redesigned the analytics tracker from scratch. One of its unique features is the ability to capture exactly where in your product an event was triggered. This information is stored in the event itself. Today, we want to show you how that works and why it matters.
During the last 12 months, our team has silently worked on an open-source project that aims to effectively integrate data science into the product analytics workflow. In this series of blog posts, we want to take you along our journey and show you what we’ve been working on.