How to solve Data Inconsistency and Duplication?
TARIQ EL YASSOURI
Leading a CX & Digital Transformation. Group Director - Data, Customer Centricity & Telesales. Ex-Maserati, Ex/Mercedes-Benz.
Inconsistency and duplication are common challenges that arise when dealing with data collected from different sources, formats, systems, or methods. When data is collected from various sources, it can have inconsistencies in naming conventions, formatting, or data structure. This can make it difficult to merge and analyze the data effectively.
Additionally, manual or automatic data entry or updates can introduce errors or duplication. For example, when data is entered manually, there is a higher risk of human error, resulting in duplicate records or inconsistent data values. When data is updated automatically, such as through data syncing or integration processes, errors can occur if proper validation and error checking mechanisms are not in place.
To address these challenges, it is essential to establish data governance practices and implement data quality management strategies. This includes:
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By implementing these strategies and practices, businesses can mitigate the challenges associated with inconsistency and duplication in their data, ensuring that the data remains accurate, reliable, and suitable for analysis and decision-making.