A survey found that 76% of data scientists consider data preparation their least favorite part of their job, and a majority of their time is spent on data preparation. Nevertheless, data preparation is necessary to make raw data ready for…

A clear understanding of data health enhances data quality and trustworthiness. This is where data exploration comes into play.  Data exploration provides extensive insights into the characteristics of your data. You can uncover data anomalies and learn how to address…

Machine Learning (ML) focuses on developing algorithms and models that enable computers to learn from and make predictions or decisions based on data. It encompasses various techniques, such as supervised learning, unsupervised learning, reinforcement learning, and more. In ML, getting…

The pursuit of accelerated business growth hinges on data-driven strategies. McKinsey suggests that companies who use consumer behavior insights strategically perform 85% better in sales growth compared to their peers. But, the traditional manual data preparation process, which often involves…

Did you know that marketing professionals alone use an average of 15 different data sources to collect customer information? While this may seem surprising, the predictions show that this number will increase to 18 this year, and that’s not even…