Maintaining data quality is of high importance in making a valuable business decision. Nevertheless, often data quality of a dataset becomes a work of errors, inconsistencies, and missing data among many other meager explanations. Data inconsistency arise as a result of various reasons including misspelling, manually entered wrong entry, and the existence of redundant data in different representations. Not correcting this flawed data can lead to huge problems following data processing eventually to wrong business decisions taken causing plenty of bucks!

There is only one place where your data can have a systematic data scrubbing and data cleansing procedure, and that’s called a data entry outsourcing service.

Data Cleansing refers to a process of detecting inconsistencies and removing errors from data to enhance its value. Call for data cleansing accelerates significantly when there are two or more data sources integrated. In order to make the process of data precise and consistent, there are some major problems that need to be avoided. These are:

1) Cryptic Standards and Abbreviations

Cryptic standards are the use of values and abbreviations in the fields.  It comprises of mentioning a full name rather than using just the initials. For instance, instead of mentioning the full name of an organization, initials are used. Error of this kind increases the chances of replication and lessens the sorting tendency.

2) Transgressed Attribute Dependencies

Violated attributes are the errors that arise when the secondary value doesn’t coincide with the primary attribute. Let’s take the example of countries where the listed country does not lie in the correct state, or when the postal code doesn’t match with the mentioned city.

3) Lack of discretion

Irregularities or lack of discretion are the non-uniform use of component or values. For instance, while entering the salaries of employees, multiple units of currencies are entered. Error of this kind entails biased elucidation and can repeatedly bring faulty result.

4) Logical Errors

Logical errors arise in data due to name conflict occurring between the structure of the data items and the specific format. For instance, a specific dataset features for name, designation, and salary. When an intermediate value say (designation) is not entered, the data followed by it altogether changes. In the above scenario, when the value for designation is not entered; value for salary is read as designation.

5) Huge Volumes of Data

Data warehouses load large volumes of data from multiple data sources endlessly. To this, they also carry a considerable amount of dirty data ( i.e. data errors). In such case, the need for data cleansing becomes mandatory and alarming at the same time.

Making data accurate, avoiding duplication, and being consistent at the same time are the core principles of data management and data cleansing helps in achieving it. If the above protocols are followed rightly during designing and execution of cleansing job, the data quality will be enormously refined. Outsourcing the same to an expert can considerably accelerate up the job ensuring the cleansing of data is achieved at its optimum.