Factors Determining The Quality of Data

Published: 30th January 2008
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What is Quality Data
In layman term, Quality Data can be defined as data that meets the specific needs of a business. Each and every business in the world runs on different set of activities and they are dependent on data but in a specific format and with different criteria as well. From an organization standpoint, the flow or process of data movement could be different from one organization to another. Moreover, the volume of data and its categories could also vary depending on the nature and stature of the particular business.

Collection of data in an unstructured manner may not be of any use to an organization until it can be viewed and processed in a manner that adds to the production cycle and therefore, to the productivity of the organization. However, usually, the primary form of the data is mostly unstructured and more often than not, incomplete and even inaccurate for a particular process. Hence, the data is required to be cleansed, structured and organized in a particular format so as to meet the specific objective the organization. This is where the data cleansing process comes into the picture in the entire process of an organization. The process of data cleansing is indeed virtually inevitable to any organization in the modern business.

So, how does one determine if certain data has requisite quality or not? Following are some of the essential criteria that the data has to meet to be called Quality Data for an organization.

Accuracy
The quality of data is assessed on the basis of accuracy, which essentially means the given data must be accurate from the factual validity aspect. Usually, the correctness of the data can be established with a perfect correlation between the data value and the source of data. The quality of data is also determined from the error-freeness of the given data.

Completeness
The data should be complete in all respect. Usually, the completeness of the data is determined and defined in accordance with the specific needs, goal or objective of an organization. Therefore, completeness of data is essentially a relative criterion.

Consistency
The data has to be consistent in nature. If the data maintained satisfies a set of constraints, it is called quality data. Consistency in data can critically impact the factual correctness of the outcome.
Timeliness
The given data, even though satisfies other conditions, can not be called quality data if it is not updated. Therefore, it is extremely essential to ensure that the current data is timely and up-to-date at the time of processing. Obsolete data can largely distort the outcome of the process.

Uniqueness
Uniqueness is also an essential ingredient of quality data. Any duplication or repetition of data could result in major fallacy in the outcome of the process.

Validity
The given data should be valid and for that reason, should satisfy certain criteria predefined by the process. Invalid data is of little use to any organization and therefore, requires to be validated before used in the process.

The above factors are, more or less, similar for determining the quality of data that is to be used for any purpose. Even if the nature of activity varies, the data must satisfy the above conditions in order to offer seamless result in the process.



Maneet Puri leads LeXolution IT Services (LIT), a leading company offering web services India, Data cleansing services India and KPO services to its offshore clients. Maneet has a substantial experience of 10 years in implementing various knowledge driven process for hundreds of clients located overseas. Visit www.lexolutionit.com if you are looking for more information on web based-applications and their benefits to your websites. Maneet also runs a weblog popularly known as http://all-that-web-demands.blogspot.com where he shares with his readers about the latest developments and innovations on web that can positively impact your websites.


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