8 simple building blocks for data preparation

Data preparation accounts for about  80 percent of the work of data citizens.  This means time is spent cleaning, organizing and collecting data sets rather than mining curated datasets for analytics and building predictive models.

It’s expected that by 2022, automated data preparation will be utilized in more than 70 percent of new data integration projects for analytics and data science.

Modernizing your data preparation can help in reducing the time to insight and data delivery. You can increase your data users productivity by spending more time on delivering insights and creating predictive models with an automated machine-learning (ML) based platform. 

These trends are driving the adoption of automated data preparation to scale from self-service models to expanded analytics capabilities.