Data Cleansing, the fact of detecting and correcting inaccurate data records, is one of the most critical steps in any data related project. By the end of the day, it is all about data quality. The cleaner the data, the higher its value.
The cleanliness of data is as important as the volume and the variety. Once the information is inaccurate, it does not matter how big or diverse the data is. Any decision taken from insights derived from this data will be useless.
Below some issues faced in real-life due to inaccurate data:
- Assuming that most of the customers are males due to the fact the “male” option while creating a new customer is the default one, and promoting products for men more than for women, will affect sales, especially when having more females than males customers in reality.
- Sending deliveries to wrong addresses or calling the customers on wrong numbers due to outdated information is worse than doing nothing. Neither the customer is happy, nor the business is satisfied.
- Employees analyzing duplicate data for the same customer existing more than once in the database due to empty spaces in his name is a waste of time, money and resources, and may lead to sending promotion messages to this same customer more than once at the same moment, which can cause frustrating and losing him. Non-productive employees and losing a customer are the two worst nightmares of any business.
Would you keep the old number of your friend saved in your contacts if he changed it and someone else is using it now? Without even considering fixing it, would you keep your old broken laptop in the drawer if it is not working?
For smarter decision making, better knowledge of customers, higher employees’ productivity, more useful marketing campaigns and increase in ROI, any business should make sure the data available is clean enough before starting any analysis on it.
Computer Engineer • Entrepreneur • Blogger