Data mapping plays a fundamental role in streamlining integrations to boost collaboration and consequently efficiency within an enterprise.
Data mapping allows businesses build a logical data model (by creating a connection between the source and target tables or attributes) and define how data will be structured and stored in the data warehouse. This promotes data warehouse optimization, thus enabling companies address the issue of storage waste. In addition, it allows businesses make the decision-making process more data-driven, which helps minimize various potential issues that may result in reduced sales and profits. Data mapping also reduces errors and thereby, decreasing the costs of computation.
Despite the fact that data mapping is tremendously useful, there are a few challenges in this process. Companies need to proactively handle these challenges or constraints for data mapping to offer best results.
Data mapping is indeed a time-consuming process as it matches every piece of data to get rid of any redundant or incorrect data, thus offering quality results. The process involves handling different input data types along with determining which mapping technique to use, and this takes some time.
Organizations can use automated data mapping mechanism to make the process faster. With superior built-in functions and drag and drop mapping mechanism, automated data mapping techniques allow business users quickly create data mappings keeping the accuracy intact.
Incomplete information or missing values are one of the primary reasons why companies find it difficult to draw conclusions from data. If the missing values are not properly handled then the insights derived will be inaccurate, and therefore the decision-making will get affected.
Companies must update every bit of data to avoid this problem. It is important to be detailed than to apply discretion as incomplete data alter and hamper the desired quality of data map, thereby failing the entire process of data mapping.
It is a common mistake for organizations to treat data map as a final product when in reality it is a continuous process. Data mapping can offer efficient results when companies are open for updating any change, upgrading or making an alteration in the data.
In the automated data mapping process, data mapping is considered as a living entity. Hence, it allows organizations make updates and changes as a new set of data sources are appended or changed.
The accuracy of data mappings is contingent on how comprehensive or detailed it is. With a large amount of information at hand, it becomes arduous for companies to create data mappings with accuracy. This problem can be easily solved by automated data mapping mechanism. Automated data mapping powered by AI allows business users create quality-assured data maps with minimum human intervention.
Currently, data mappings have become a crucial segment of data integration and migration. But, to derive maximum value and deliver the best results, one has to overcome challenges mentioned above using an automated data mapping technique.