The data cleaning, By herself, does not guarantee immunity to errors, deficiencies and flaws in the quality of information. There are still companies that do not know the relevance of carrying out an adequate maintenance of your data. And it is exactly this data maintenance program the one that supports cleaning actions. It's about two complementary approaches with the same goal: preserve data quality and ensure consistency, integrity, update and accuracy.
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Data maintenance and cleaning: allies to boost the quality of information
In the same way that the facilities and offices of any business are cleaned daily, the data cleaning It should be part of the company's plan to ensure the quality of the information. These actions are specific Y, preferably, must be carried out by experts with the best technologies.
The application of, as an example, one of data quality software, offers the ability to detect and correct errors that have accumulated over time. Using sophisticated techniques and automated processes can identify potential duplicates and, at the same time, find all non-compliant data.
A data cleaning usually consists of three actions:
- Error prevention: occupies the 10% cleaning effort.
- Fault detection: usually represents the 30% Of action.
- Repair of all identified irregularities: need the 60% of the resources and time allocated to the action of data cleaning.
Despite that, if cleaning is still an isolated action, its effectiveness would end up being null. The data cleaning is the procedure of dealing with errors within the database, ensuring that anomalies are located retrospectively and proceeding to erase all identified errors automatically and at once.
To prolong its positive effect over time, good maintenance is needed. It is takes care of the correction and permanent verification, as well as the performance of periodic controls that make it possible to know the status of quality. Is about procedure that involves data quality in a cycle of continuous improvement. Even when there are no rules for its structuring, it is usually advisable that the data maintenance plan be distributed as follows:
- Failure prevention: 45%
- Error detection: 30%
- Repair of irregularities: 25%
Working in this way prevents data quality issues from escalating to an unsustainable level, what is difficult to solve.
Maintenance and updates are not more important than the data cleaning. It is also not a priority since, long-term, the organization needs both to ensure that reporting standards are met and that the data used for analysis and decision making is complete and consistent. To support cleaning and maintenance initiatives, the organization must promote a culture of data quality, where all users and staff who interact in some way with the information, be aware of the importance of observing certain rules that guarantee the best use.
