Data cleansing has played a significant role in the history of data management as well as data analytics and it is still developing rapidly. Furthermore, data cleansing in big data is considered to be a challenge due to its already high and increasing volume, variety and velocity of data in several applications.

Since real life data is dirty, it gets costly and, therefore, the significance of data quality management in business is highlighted. Data cleansing or scrubbing or appending is the procedure of correcting or removing inaccurate and corrupt data. This process is crucial and emphasized because wrong data can drive a business to wrong decisions, conclusions, and poor analysis, especially if the huge quantities of big data are into the picture. There are businesses who have lost a huge amount of money due to the big bad data.

Source: martechseries.com
Author: Vikas Bhatt