Data Cleansing And Data Mining

By Author: Manohar Reddy
Total Articles: 8
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Data cleansing, also known as data scrubbing, is the process of ensuring that a set of data is correct and accurate. During data cleansing, records are checked for accuracy and consistency, and either corrected, or deleted as necessary. Data cleansing can occur within a single set of records, or between multiple sets of data which need to be merged, or which will work together.

At its most simple form, data cleansing involves a person or persons reading through a set of records and verifying their accuracy. Typos and spelling errors are corrected, mislabeled data is properly labeled and filed, and incomplete or missing entries are completed. Data cleansing operations often purge out of date or unrecoverable records, so that they do not take up space and cause inefficient operations.

As your business expands and needs to keep track of ever larger quantities of data, it's likely that many of your records will become out of date, while others may fall victim to error. Rather than having to manually sort ...
... through databases to find erroneous or missing data, you can save time and money while also safeguarding against the future when employing data cleansing processes.

Data cleansing does precisely what its name implies. The program searches through all the records you instruct it to - which can be a single set of records or multiple data sets - and can instantly detect inaccuracies or inconsistencies that will be brought to your attention. Data cleansing is capable of more than just identification though, and can be easily programmed to auto correct or automatically delete these results to ensure all your data is reliable and up-to-date.

By this point in time, you've probably heard a good deal about data mining -- the database industry's latest buzzword. What's this trend all about? To use a simple analogy, it's finding the proverbial needle in the haystack. In this case, the needle is that single piece of intelligence your business needs and the haystack is the large data warehouse you've built up over a long period of time.

Data mining products are taking the industry by storm. The major database vendors have already taken steps to ensure that their platforms incorporate data mining techniques. Oracle's Data Mining
Suite (Darwin) implements classification and regression trees, neural networks, k-nearest neighbors, regression analysis and clustering algorithms. Microsoft's SQL Server also offers data mining functionality through the use of classification trees and clustering algorithms. If you're already working in a statistics environment, you're probably familiar with the data mining algorithm implementations offered by the advanced statistical packages SPSS, SAS, and S-Plus.

Data mining (also known as Knowledge Discovery in Databases - KDD) has been defined as "The nontrivial extraction of implicit, previously unknown, and potentially useful information from data"[1] It uses machine learning, statistical and visualization techniques to discover and present knowledge in a form which is easily comprehensible to humans.

Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late.

Author bio:- Manohar Reddy is specialized in outsourcing data entry services. He offers quality and all kinds of data entry, data cleansing , data processing, data mining, data conversion and document scanning services to the U.S., Canada, Australia, India and many more.