Data mapping is an essential process in the industry today. It helps optimize warehouse better, and thus addressing the issue of storage waste. It improves the quality of data which is extremely helpful as one does not have to sift through a pile of unusable data anymore. When done correctly, data mapping greatly helps in reducing errors and thereby, decreasing the costs of computation. However, there are still a few challenges in this process which cost money and time to rectify. These challenges need to be dealt effectively for data mapping to offer its best results.
Time is a major constraint
It is undeniable that data mapping is a time-consuming process as it works by thoroughly matching every piece of data to eliminate any redundant or incorrect data, thereby offering the best quality results. It is extremely helpful in optimizing the data, but it is a long and tedious process which definitely takes some time. While it is inevitable as of now but there are ways to make it faster. It is a good idea to arrange the data into simple and template-based packets which can be easily processed and incorporated. This is more efficient and easier to get results with.
Companies often make the mistake of missing out on information which results in inaccurate data and insights as derived from incomplete data. It is important to update every bit of data, however insignificant it might seem. You never know how crucial one single bit of data might turn out to be. Therefore, it is better to be thorough than to apply discretion and enter inadequate data which can severely alter and hamper the desired data map, thereby failing the entire process of data mapping.
The need for constant updating
People often make the mistake of treating data map as a finalize product when in reality it is actually an ongoing running process. For it to function effectively and offer efficient results, it is important to update any change, upgrade or alteration in the data as and when it happens. Failing to update results on time can lead to backdates logs and invalid data which does not come to much use.
Loss of accuracy due to human error
Last but not the least, human error is a major factor when it comes to the failures of data mapping. The biggest liability of data mapping comes to an error or misinformed decision on the part of humans. It can lead to inaccurate, duplicate, redundant or useless data without proper insight. In order to not waste time and resources in this manner, it is important to be extra careful when it comes to any human entry and involvement.
In today’s scenario data mapping has become an extremely important part of data integration and migration. It is practically indispensable owing to its contribution to quality and costs. While there are some challenges which still persist, it is essential to work towards handling these challenges better in order to produce more effective and quality results using data mapping.