Are you in the retail sector? Those types of businesses that sell retail or retail products or services, also known as retail businesses. Good, if it is there, you will almost certainly be affected by poor quality costs of the data.
The poor quality data in retail stores They are not only expensive but also dangerous. Most retailers trust that your sales are affected every month due to inaccurate or incomplete contact details preventing the final sale from taking place. And that supposes a not inconsiderable percentage of income that in short does not materialize.
But poor quality costs of the data does not stop there. Not only those sales that are not produced by inaccurate data impact. These costs go much further. They also impact the quality of the analysis. subsequent data and decision making based on those analyzes. Therefore, as we say, it is a serious and dangerous obstacle that you have to know how to tackle.
Advances in technology have led to a large increase in the amount of data that retail companies must handle.. And with the increase in the amount and diversity of data, inconsistencies arose.
Poor quality data comes mainly from 4 different fonts:
- Input data. It is the main one and is mainly caused by human error and noise in communication.
- External data. Built-in from external databases that are not reviewed.
- Transactional systems. Errors that are generated something goes wrong when using POS terminals in a sale or when adding or canceling any of the items that intervene in the purchase procedure.
- Migrations. Produced when changing systems and incorporating data from the old system to the new.
Either of these possibilities increases the poor quality costs of the data because outdated information, inaccurate or missing preventing data from being used as intended.
Then, How can these retailers get their poor quality costs in decreasing data?
1. Data validation at the source
The more erroneous data that moves through our systems, more damage will do. Then, the main strategy we must implement is to validate the accuracy and integrity of the data at the source. When we talk about avoiding or improving human error in data entry, this can be done without having to change your management systems Sales. You can implement an API that in real time they validate this data and correct it or request its correction. Right at the point where they are happening.
2. Divide and conquer
You can't cope with all the problems of poor quality costs data everywhere at the same time. As we have seen, these errors can reach our systems by various parts. Tackling them all at the same time can only make matters worse and dilute the power of your data quality improvement initiatives.. Start by establishing the most important problems and try to attack them piecemeal.
3. See the complete system
Take advantage of this data quality improvement initiative to analyze and restructure your company's data flows. Poor quality data is very likely to be found not only in your sales department, but in all other departments. And each department can have definitions, classifications and alternatives to communicate them totally different. Then you must establish and describe the processes making sure that the steps are clearly defined and that they take into account all those different alternatives for handling and communicating the information.
