The quality of data is fundamental to the success of any organization. Undeduped data, which includes errors, duplicates, and incomplete data, can negatively impact decision-making and operational efficiency.
Detecting and correcting these data issues is essential to maintain the integrity of information.
According to Tableau, the likelihood of a company having undeduped or inaccurate data is quite high. In fact, it is estimated that 91% of companies suffer from data quality issues, mainly for three reasons:
Human error This is the most common cause of the generation of ‘undeduped data,’ due to manual data entry, which can lead to spelling mistakes or incomplete information.
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Different systems When integrating various data, organizations often store them in different systems with varying structures, requirements, and aggregations, which leads to inconsistencies in the recorded information. |
Changes in requirements Data systems require constant updating of fields and recorded information, which, in many cases, engineers are unaware of when these system adjustments occur.
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Although these three factors have a significant impact on the generation of errors, this time we will focus on the one caused by “different systems.” It is no secret that data migration can be a real headache, especially when it involves rolling out a highly anticipated development for the organization.
But what makes data transfer between information platforms a generator of undeduped data?
- Format incompatibility: Different systems may use different data formats, which can lead to errors during data conversion or transfer.
- Human errors: Manual intervention in the transfer process can introduce errors, such as incorrect data entry or omission of information.
- Lack of standards: The absence of uniform standards for data management can result in inconsistencies and errors when moving data between systems.
- Synchronization issues: If systems are not properly synchronized, data may become outdated or incomplete.
- Data loss: During the transfer, data may be lost due to technical failures or connectivity problems.
- Mapping errors: When transferring data, it is necessary to map fields from one system to another. If this mapping is not done correctly, data may be mislocated or misinterpreted.
To mitigate these issues, companies can implement solutions such as process automation, the use of data integration tools, and the adoption of data quality standards. Here’s how each of them reduces the error gap:
Process Automation
Process automation reduces manual intervention, which decreases the likelihood of human errors. By automating repetitive and complex tasks, data transfer becomes consistent and precise. Additionally, automation can include automatic validations and checks to detect and correct errors in real time.
Data Integration Tools
Data integration tools are designed to facilitate the transfer of data between different systems. These tools can:
- Automatically convert data formats to ensure compatibility between systems.
- Accurately map data fields from one system to another.
- Synchronize data in real time to avoid outdated information.
- Monitor and log data transfers to quickly identify and resolve issues.
Adoption of Data Quality Standards
In addition to the two previous tools, implementing data quality standards ensures that all systems follow the same rules and practices for data management. This includes:
- Clear definitions of acceptable data types and formats.
- Validation procedures to verify data accuracy and consistency.
- Data cleansing policies to identify and correct undeduped or inaccurate data.
- Training and awareness for staff about the importance of data quality.
By combining these strategies, companies can minimize errors and improve the quality of data transferred between systems, resulting in more reliable and useful information for decision-making.
In conclusion, detecting and correcting undeduped data is crucial to maintaining the integrity and usefulness of information within organizations. By implementing appropriate strategies and tools, companies can improve the quality of their data and, consequently, the efficiency and effectiveness of their operations
Reference:
Tableau, Los datos “sucios” tienen consecuencias: Cómo solucionar los problemas más comunes de preparación de datos. Tomado de Los datos “sucios” tienen consecuencias: Cuatro soluciones a los problemas más comunes de preparación de datos
