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Data cleansing challenges
Data cleansing, itself, is one of the most important challenges for any company dealing with digital information. The corruption of data causes inconsistent and false results leading to disastrous decisions. A cleansed and fully verified data is a valuable asset for any UK based company. Data cleansing operations usually start during the process of data entry. Run-time data cleansing approach is the most suitable approach for small companies but when the data is arriving from different locations, the data cleansing process cannot be performed during the entry process without losing performance.
Some of the common techniques involve manually checking records and using automated software to detect and repair errors in the stored data based on preset business rules. Although data cleansing looks like a simple task yet it is quite exhaustive and challenging. Here are a few of the common challenges that a data cleansing operation usually faces.
Information loss and correction of errors
One of the major challenges in data cleansing is to correct the values within data and remove duplicate data entries. In most of the cases, the amount of information available against any such anomaly is quite limited leading to insufficient decision critical data needed for correction of these entries. In such a scenario, data deletion remains the only viable choice.
Maintenance procedures for data cleaning operations
Data cleansing is a costly job and often there is a risk of data corruption after it has been cleansed. The data can be cleansed manually or through software but the organisations have not yet mastered and perfected the tools and techniques to guarantee the accuracy and integrity of data after cleansing. There are no standard procedures for verification of data accuracy during the data entry process except caution.
In an integrated environment, where data is flowing in from different substations into the central database, run-time data cleansing seriously affects the performance and reliability of the system. Performance issues are clearly visible in large systems where data is inter-dependent and arriving from different geographical locations. The lags in the arrival of connected data can cause the data cleansing system to get stuck in an iterative and sometime infinite loop compromising the performance of data handling system.
Data cleansing framework
It is often impossible to predict the data cleansing results until enough information about the behaviour and nature of data is available. A framework is needed in these cases to accurately design a data cleansing strategy. In many cases, this framework is linked with integration and maintenance procedures as well but there is no solid framework solution to accurately predict the entire data cleansing process in any project.
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