Importance of data quality for decision making

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Do you know how to ensure data quality and integrity in your organization? Here you will find the clues you need to recognize errors in your information.
Ensuring data quality is not easy. Of course, accurate, up-to-date and complete data would be desirable, but unfortunately the real world is far from ideal. Achieving high-quality data requires a very clear understanding of the meaning, context and intent of the data , with no ambiguities and, if possible, standardised definitions that can serve as a basis for future decision-making.



Ensuring data quality in the organization should not be considered as a one-off action. Planning continuous improvement based on iterations is the most effective approach and the one that can bring the amazon database closer to success, as far as data quality is concerned. In this process, it is of great help to have:

- Knowledge of the sources of origin.

- Control of the data path .

- Business glossary .



Data quality as an essential part of MDM


When are data quality errors discovered?
At best, bad information is immediately recognized and excluded from the decision-making process. At worst, faulty, incomplete or unreliable data goes unrecognized and leads to erroneous decision-making. These are the effects of insufficient or non-existent data quality.

Unfortunately, most of the time, such poor quality information is discovered at the end of the data transformation process , when the information flow has reached the hands of its final consumer and actions have been carried out.

This type of information tends to be generated using data that has been stored for some time, and therefore, repairing it can be a complex project when you do not have:

Process design.
Human capacity and expertise.
Appropriate technology for this.


Key aspects for a Business Intelligence solution
High-quality information is essential in all aspects of today's business ecosystem. Improving the quality of data and, therefore, the information derived from it and the knowledge it generates are fundamental aspects during the process of implementing a Business Intelligence solution , and also as a previous step to this stage.

Typically, many errors and adverse incidents occur in strategic decision-making processes , which are not attributable to the BI solution itself, but are the result of poor data quality and incomplete, outdated or inconsistent information. This leads to: