As companies aim to become data-driven, data cleansing becomes a crucial part of an organization’s business intelligence strategy.

According to the 1-10-100 quality principle mentioned by Validity, the relative cost of fixing a data quality problem increases exponentially over time. It takes $1 for identifying bad data at the earliest stage, $10 for correcting existing problems at the middle stage, and $100 for fixing a problem after it causes a failure at a later stage. As companies aim to become data-driven, bad and dirty data continues to be the biggest obstacle in their execution. This is why data cleansing is the most crucial part of an organization’s business intelligence strategy.

What is data cleansing?

Measure data cleanliness to ensure data quality

Data cleansing workflow

Data cleansing activities

Best practices to maintain data cleanliness

Importance for businesses

#analytics #big data #big data analysis tools #big data architectures #business strategies #from our experts #data-driven #digital transformation