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Product Analytics Pipeline

We're on a mission to create
the ultimate workflow
for data scientists

Objectiv is a data collection & modeling library that puts the data scientist first.

The project is open-source and we’re building it in public.

Objectiv proposes the adoption of an open, common taxonomy for analytics
to unify the way data scientists collect & model data.

The open taxonomy for analyticsThe open taxonomy for analytics

It enables a shared way to collect well-structuredpre-validated data that is ready to model on without significant gruntwork. Models & datasets become reusable and interchangeable, enabling data scientists to build on knowledge & practices of others.

DownHow it worksDown

1.
Map your application to the open taxonomy

The open taxonomy of analytics is our proposal for a common way to collect, structure and validate data.

It defines classes for each common event type and the contexts in which they can happen. It describes their properties, requirements and their relationships with other classes.

A class for each event type
Map your application to the open taxonomy

With Objectiv, you map your application to this taxonomy, creating a contextual layer that's used for data collection & validation.

In most cases you can skip the discussion on what to track, because the taxonomy is designed to ensure the collected data covers a wide range of common analytics use cases.

The initial version of the taxonomy is built for product analytics. We have plans to support other fields as well.

2.
Debug your instrumentation on the fly

Objectiv comes with a set of tools that help you set up error-free tracking instrumentation.

For instance, you can validate your instrumentation against the taxonomy and get live feedback in your IDE and console while developing, enabling you to catch errors before data starts flowing in.

Live debugging feedback on your instrumentation
3.
Collect rich & descriptive data that’s ready for modeling
Validated, rich, descriptive + well-structured

Objectiv’s tracker collects data that is unusually rich. Events can carry multiple contexts, including the exact location in the UI from where they were triggered.

All data is well-structured, self-descriptive and has been validated at the first step of the pipeline, so you can use it for modeling with minimal additional gruntwork.

4.
Use Pandas-like operations on your full data set

Objectiv features an SQL abstraction layer for modeling that enables you to use familiar dataframe operations on your full data set, straight from your notebook.

You can output your models to SQL queries with a single command, effectively closing the gap between experimentation and production.

Talk Pandas, get SQL
5.
Reuse parts of any model
Quickly build models by reusing what others have made

By embracing the open taxonomy, you can reuse your own models for other projects and quickly build models reusing parts of others.

That behaviour pattern you've created to exactly identify the heavy users of your website? You can likely reuse that for your mobile app by changing a single line of code.

Objectiv is open source and we’re building it in public.

Have opinions on where we should take this or want to stay in the loop?

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