Significant Aspects of Data Quality Solutions

Dirty data can be generated in a business database due to variety of reasons like errors, incorrect data, overwriting and duplication. Inconsistent data and redundancy can also result in dirty data. With so many error causing factors, it is obvious that dirty data leads to a variety of business problems that may possibly get aggravated enough to cost thousands of dollars.

This calls in for the need to manage and improve the quality of data by employing various types of data quality services. Quality data is essential for the efficiency and performance of any business. Apart from the cost factor, the reputation and the business of the company are also at stake.

The features of data quality services can be highlighted as follows:

  • Data cleansing is done to remove the erroneous and incomplete data. The information that is repeated or is inconsistent is removed too. Thus this process is a sort of enrichment of information. Data cleansing is mostly an automated process with the help of tools and platforms.
  • Data matching is conducted so as to eradicate repetitive data and also to understand the causes that result in repetition and see to it that they are eradicated.
  • Data verification is a process that is conducted to validate the data that has been or is been worked upon. This process can be carried on individually or along with other processes.
  • Data profiling is the method of analyzing the data source to identify, manage, match or cleanse the data. Data profiling can also be used effectively to profile web pages and website data and also for lead management using website data.
Apart from all these functions, the data quality is monitored at regular intervals. This helps to resolve various problems like incompetency, incompleteness, errors, invalidity and duplication of data. Though data quality management appears to be a tedious task that involves time, money and maintenance, the results that it provides are long term and helps to generate profits more than expected.

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