Hybrid Edition

September 28-30

Vilnius and Online

Sonal Goyal


India, Nube Technologies
Virtual Café

Preparing Data for Analytics

Main Problem

Data scientists and data analysts spend a majority of their time preparing data for analytics. Poor data quality, data silos, lack of common standards lead to more time wrangling data and less time analyzing it. Data engineers too are affected building data wrangling pipelines to get the right data in one place.

Why this topic is relevant?

If we can use data directly or with minimal effort, the job of a data scientist and engineer are greatly simplified. They can be far more productive and yield greater business results.

Common Trends

Uses of tools for data preparation, machine learning for preparing data for analytics, ad hoc wrangling are some of the ways data is prepared today.

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