Data Cleansing and Enrichment

Data cleansing deals with identifying and removing errors from data in order to enhance its quality. Data quality problems exist in data collected from multiple sources, due to typos, spelling mistakes, invalid data, etc. The need for data cleansing increases considerably when files from numerous sources have to be integrated. In order to make accurate, current and consistent data available, verification and validation of data against reliable sources of information becomes necessary.

Issues hampering the Quality of Database:

Inconsistent Data

Storing data at many locations results in data inconsistency. Any modification that has been made at one place might be left out in other locations, making the data prone to inconsistencies.

Duplicate or Conflicting Data

Databases are prone to data duplication risks as they could be compiled from numerous sources. If the database is such that it will support core processes and decisions, issues related to duplication and data conflicts need to be resolved effectively since, its impact can be huge. When the problem shoots up, identifying and fixing the conflicting or duplicated data becomes a tedious task.

Data Irrelevance

Data footprint can be reduced significantly by removing irrelevant data. Eliminating irrelevant data can help focus on the remaining portion of data that is relevant thereby, saving time and efforts.

Data Incompleteness

Apart from duplication, the database also needs to be checked for missing data such as missing postal codes, email ids, etc., so that the database is always accurate and complete.

Outdated Data

Due to continuous inflow of data from various sources, it is normal for a database to get outdated after a certain point of time. Hence, it becomes imperative that a threshold limit is determined after which the data should be updated.

Data Cleansing Phases

Data cleansing includes several phases, such as:

Data Analysis:

A detailed analysis of data is required to detect types of errors to be removed. Manual inspection of the data must be analyzed comprehensively to gain insights about data properties and to recognize data quality issues.

Standardization of Data:

Data standardization is a crucial step to facilitate easy sharing across the organization. Ideally, standardization of data is to be performed during data entry stage. But, for any reason if it is not possible to do so, an extensive back-end process is required so as to remove all the inconsistencies present in the data.

Data Normalization:

Normalization of data usually involves splitting of large tables into smaller ones and mapping their relationship to reduce redundancy. The aim is to segregate data so that, any modification in one table results in modification in the rest of the database as well.

Quality Check:

Every phase of data cleansing should pass through quality checks. But nevertheless, it is imperative to have an exclusive quality check stage to ensure that the data adheres to quality standards and is accurate.  

Author Bio - Mike Wilsonn is a passionate content writer and blogger by profession. He love writing articles, reviews and blogs on wide range of topics including data entry, data cleansing services, ePublising and digital marketing industry. When he is not terrifying readers with his writing, he loves playing football. Presently, he is allied with SunTec India and you can visit him at www.suntecindia.com. 

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Created on Dec 31st 1969 18:00. Viewed 0 times.

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