Guarantee the quality of the data is not easy. Clearly, accurate data would be desirable, updated and complete, but unfortunately the real world is far from ideal. Achieve high quality data You need a very clear understanding of its meaning, context and intention., where there are no ambiguities and at the same time, if it is feasible, have standardized definitions that can serve as a basis for future decision-making.
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Ensuring the quality of data in the organization should not be considered as a specific action. Planning for continuous improvement based on iterations is the most effective approach and the one that can bring the business closer to success in terms of data quality. In this procedure, to be able to count on:
– Knowledge of the sources of origin.
– Data path control.
– Business glossary.
When are data quality errors discovered??
In the best case, misinformation is immediately recognized and excluded from the decision-making process. Worst, Faulty data, incomplete or unreliable are not recognized and lead to poor decision making. They are the effects of insufficient or non-existent data quality.
Most of the time, Unfortunately, that poor quality information is discovered at the end of the data transformation procedure, moment in which the information flow has reached the final consumer and the actions have been carried out.
This type of information is usually generated from data that has been stored for some time and, therefore, repairing it can be a complex project when you don't have:
- Process design.
- Human capacity and experience.
- Suitable technology for this.
Fundamental aspects for a Business Intelligence solution
The high quality information is essential in all aspects of today's business ecosystem. The data quality improvement Y, therefore, of the information derived from these and the knowledge they generate; are fundamental aspects throughout the procedure of implementation of a Business Intelligence solution, and also as a step prior to this stage.
In general, many errors and adverse incidents occur in the strategic decision-making processes, which are not attributable to the BI answer itself, rather, they are the result of poor data quality and incomplete information, outdated or inconsistent. This implies:
– Direct impact on the degree of competitiveness of the company.
– Increase in process costs.
– Inhibition of information exchange, the analysis, research and performance measurement initiatives.
Avoiding these undesirable consequences for any company means prioritizing data quality, promote proper information governance and ensure that steps are taken to promote data integrity in the organization.
