According to Gartner’s report, 40% of businesses fail to achieve their business targets because of poor data quality issues. The importance of utilizing high-quality data for data analysis is realized by many data scientists, and so it is reported that they spend about 80% of their time on data cleaning and preparation. This means that they spend more time on pre-analysis processes, rather than focusing on extracting meaningful insights.
Although it is necessary to achieve the golden record before moving on to the data analysis process, there must be a better way of fixing the data quality issues that reside in your dataset, rather than correcting each error manually.