In the data-driven era, where global industries have in hand the benefits of digitalization, the exponential growth of raw data can create an imbalance in the Datasphere.

Hence, it becomes imperative to responsibly unlock the value of raw data coming in a variety of formats and locations. Data Wrangling gives a clear picture of cleaning, organizing, and transforming the raw data into a usable format playing the role of protagonist in the data industry. The umbrella term of Data Wrangling involves the processes of data discovery, data structuring, data cleaning, enriching data, validating data, and publishing data. Manual or Automated, Data Wrangling is a necessary step in the Data Science lifecycle.

Despite the fact that Data Wrangling is an iterative process, can it drown the data professionals beneath the huge volumes of endless data?

- MADHAVI DESAI