Data Cleaning And Its Principles - By: Maneet Puri

The fundamental meaning of Data cleaning is to improve the quality of the data by sorting out inaccurate or incomplete data. The process is completed with correcting those errors and omissions. The process of Data Cleaning involves checking of completeness, format, limit checks, reasonableness checks and review of the data, done to identify outliers such as statistical, temporal, geographic and environmental, along with other errors. The process does not end here, instead the core of the process is assessment of data by subject area of experts, like taxonomic specialists.

Well, the general framework of Data cleaning is drawn below;

Defining and determining types of error available in the documents.
Identifying instances of sorted out errors
Correcting the errors
Enhancing data entry process to further reduce the possibility of errors.

Take a look at the Key Principles of data Cleaning Procedure, for better understanding of this process-

Planning - with developing a Vision, Policy and Strategy - is Crucial

A proper planning is essential to ensure a good data management policy. Keeping the core idea of Data Cleaning, the process involves other aspects like data quality, vision and policy. These three aspects integrated in the process will improve the reputation of the organization among users as well as suppliers.

Organizing data and Documentation Improves efficiency

Documentation is the key to good data quality and if the data is organized properly, it helps in the tasks like checking, validating or correcting of data, improvise their efficiency and further reduces the time and expenditure involved in data cleaning.

Prevention is Better than Cure

It is always better to prevent emergence of error than to committing an error and then finding it to resolve further. However, finding and evaluating errors, in a way, gives you the feedback on your work and lessen the likelihood of re-occurring of those errors.

Responsibilities understood by Collector, Custodian and User

The task of data cleaning belongs to all, whether collector, custodian, or user. However, the prime responsibility belongs to the Information Management of the organization, who is supposed to take care of the storage and management of data.

Minimizing Data Duplication and Reworking on Data

Duplication of the data is a major area of concern faced by most of the organizations.

Feedback- A two-way Treat

It is oblivion that users are likely to sort out the errors from the data more often than each individual data custodian working in an organization does. So, it is important that the feedback given by the users should be turned to the custodians for improved results.

Accountability, Transparency and Audit-ability

A haphazard or unplanned activity for data cleaning often ends up in mess and is not productive. Therefore, accountability, transparency and audit-ability are essential elements in the process of data cleaning.

The idea of Data Cleaning is centralized to the improvement of overall quality of data and to make them “fit for use”. This way the existence of errors in data gets significantly reduced and the presentation of the documentation of data is improved. The errors in data is pretty common and usual, therefore finding out the errors to clean them and eliminating bad records of the data is a tedious process. However, the process can not be ignored as it is important to eliminate the bad records of data.

About the Author

Maneet Puri is the managing director of LeXolution IT Services, a professional IT Services company that provides a full range of KPO Services such as data entry, data mining and data extraction services. His company caters to clients from across the world.

Article Directory Source: http://www.articlerich.com/profile/Maneet-Puri/34624

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