why data science projects fail

Download New whitepaper

Feature engineering accelerated, not automated.

Rasgo empowers Data Scientists to engineer the best features faster by simplifying the time-intensive work of data exploration, data prep, and feature selection.

Your model called. It’s waiting for training data.

We listened to 1000s of Data Scientists.

The story is always the same.

Feature Engineering

Who wants to build features from scratch every time?

say scaling up features is the top challenge
say quality features are important to success
Data PReparation

You didn’t earn a PhD to wrangle data.

time spent cleaning & organizing data
total time spent preparing data
projects never make it to production
data extraction & Exploration

Looks like weeks down the drain, just to get the data.

time spent collecting data sets
blocker is knowledge of data assets

Push the data lifecycle forward, make time for data science.

Define the project

Data Scientists spend 80% of their time preparing for modeling by joining datasets, profiling and correlating features, and extracting training data. It leaves little time for data science.

Get your data going.

Learn the secrets behind over 20 different models with Rasgo’s Model Accelerator Directory.

Customer churn analysis

Reduce churn by predicting which customers are likely to churn and why.

Marketing attribution

Determine which marketing campaigns drive a customer to purchase.

Digital demand forecasting

Predict future demand for services, such as sessions or downloads.



More accelerators designed to ramp up your ML projects.

See all

Accelerate feature engineering, build better models.