Post by ummefatihaayat12 on Feb 28, 2024 15:17:20 GMT 8
There are many ways to evaluate the quality of analytical data in terms of precision, accuracy, representativeness, completeness and sensitivity in relation to its intended use. Quality control helps to achieve better results in these evaluations and, therefore, to optimize decision making and reduce risk. In general, there are two types of errors that can occur in a data set: Errors of commission – These are the result of incorrect or inaccurate data being included in the data set. They may appear due to errors during the input process, among others. Errors of omission: they originate from data or metadata that have not been included. The situations that give rise to these types of errors have to do with a lack of data documentation, human errors during data collection or entry, or the presence of anomalies in the field that affect the data.
Quality control is the set of actions that helps avoid input errors or those generated later in a data set. These activities guarantee the quality of the data and its suitability for use and analysis, in addition to facilitating the monitoring and maintenance of the required data quality standards throughout its life cycle. Web analysis , due to its strategic importance, and manual data entries, due to its tendency to present errors, are the two most critical areas in terms of quality control and where these types of techniques India Part Time Job Seekers Phone Number List should be applied with extreme caution. Quality control and web analysis Data is an irreplaceable asset when it comes to obtaining a snapshot of the online reality of the business. Thanks to them, different problems that users face on the corporate website can be discovered, their behavior is better understood and the intentions of clients and potential clients can be predicted. The data generated by the different channels available to the business on the Internet provide important clues and signals that improve the learning capacity, helping to facilitate the decision-making process.
However, data is not always a faithful representation of reality; Sometimes they can even distance themselves (and the business) from the truth or a correct interpretation of the facts. This happens when there are unresolved quality control issues. A decrease in data quality can mislead data analysts and lead to an incorrect assessment of a situation. A scenario of this type should be avoided at all costs since not only would the skill of the analyst or the meaning of the data involved be called into question, but all reports, all analyzes and all decisions made to date would be called into question. To prevent this type of situation and its consequences, the data must be subjected to a thorough quality control before any web analysis work is to be carried out. This exhaustiveness does not imply that data that is not one hundred percent accurate should be discarded, since achieving these figures would imply a completely unjustifiable cost, but rather that this objective must be approached as closely as possible, within the limits of coherence. And, to do this, there is nothing better than applying the following recommendations: 1. Check that the number of audited pages is as close as possible to the number of pages that make up the corporate website.