Data cleansing involves identifying and removing errors and inconsistencies data in a given set of data with the aim to improve the quality of the data. In the data set that was provided, there are a number of inconsistencies and errors, namely, missing values or information and invalid data. Data cleansing is an important task for every organization. Undoubtedly, during the process of cleaning data, one is bound to encounter several challenges, and one has to find a way to remedy those challenges.
The first challenge is working with high volume data. This makes the data cleansing process tedious. Our data is composed of many elements or variables, each with a lot of entries. Such data sets tend to have a significant amount of data errors, which, sometimes, are difficult to detect. In such a case, the process of cleaning the data becomes not only significant but also formidable. To address this challenge, one ought to standardize the data and automate the validation process. This will not only cleanse the data but also help save time and reduce the risk of human error. The other challenge is missing values. Missing values occur due to omissions that happen when collecting the data. The remedy to this challenge is to flag the missing data and use algorithms to estimate the optimal constant for such a situation.
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Data quality is of central importance to organizations. This is because it helps them avoid costly errors. Data cleansing is the single best solution for steering clear of the costs that crop up when companies are busy processing errors. It also helps improve the decision-making process. In businesses, accurate and updated data supports analytics and business intelligence (Lewandowski, 2018). Clean data tend to build confidence in the accuracy of the results. This, in turn, helps organizations make informed decisions in the business processes.
References
Gulipalli, G. (2016). 14 key data cleaning pitfalls. [Online]. Retrieved March 12, 2020, from https://www.invensis.net/blog/data-processing/14-key-data-cleansing-pitfalls/
Lewandowski, P. (2018). What is data cleaning and why is it important? [Online]. Retrieved March 12, 2020, from https://sunscrapers.com/blog/why-is-clean-data-so-important-for-analytics-and-business-intelligence/
