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Common Issues Encountered During Data Cleansing

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Quality data is necessary for proper business decision-making. However, the quality of a dataset can be affected by inconsistencies, errors, missing data, etc. Data inconsistency can occur due to various reasons, such as wrong manual entry, misspelling, missing information and presence of redundant data in different representations. Failing to correct these errors can lead to major issues in the subsequent data processing stages. This, in turn, can lead to faulty business decisions. It is therefore necessary to identify and correct inaccurate data is a dataset and improve the quality of the data using effective data cleansing techniques. A professional data cleansing company will have systematic data cleansing and scrubbing procedures in place to improve the integrity of client information.

 Common Issues Encountered During Data Cleansing

Data cleansing can deal with discrepancies and errors in both single source data integrations and multiple source data integration. To avoid errors, the process must be handled by an expert. Here are some of the issues that may be encountered during the data cleansing process:

  • High Volume of Data – Data warehouses load huge amounts of data from a variety of sources and may carry significant amount of dirty data. In such situations, data cleansing can become a daunting task.
  • Misspellings – Generally, misspellings occur due to typing error. It is necessary to detect and correct the wrong spellings and grammatical errors. As database contains a huge amount of data, it could be difficult to detect spelling mistakes at the input-level. In fact, spelling mistakes in data such as names, addresses are always difficult to identify and correct.
  • Lexical Errors – Typically, lexical errors occur in data due to name discrepancies between the structure of the data items and the specified format. Data that does not meet requirements of integrity are referred to as data anomalies. A type of data anomaly, semantic errors include incorrect variable types or sizes, nonexistent variables, and subscripts out of range. Semantic anomalies lead to lexical errors that act as the interface between structure of data items and given format of table.
  • Misfielded Value Problems – This issue occurs when the values entered do not belong to the field. For instance, entering the name of the ‘country’ in the field for ‘city’.
  • Domain Format Errors – These errors occur when the value entered for a particular field does not match the domain format. For instance, a NAME field requires the first name and surname to be separated with comma. When the name is entered without a comma, the input may be correct but it would not comply with the domain format.
  • Irregularities – These include spelling and formatting errors such as incorrectly written categorical variables, and inconsistencies in the date format, and non-uniform use of units or values.
  • Missing Values – There could be missing values for variables where complete data are necessary. This occurs due to omissions while collecting data.
  • Contradictory Data – This type of inconsistency occurs when two dataitems in the data set disagree with each other. For instance, valid age given as ’10’ does not match with marital status: ‘divorced’.
  • Duplication – This error occurs when the same data is represented multiple times due to a data entry error.
  • Integrity Constraint Violations or Illegal Values – This describe values that do not satisfy integrity value constraints. Usually, violation occurs when the value entered is outside the limit of values allowed for representing a particular attribute.
  • Cryptic Values and Abbreviations – This refers to using cryptic values (e.g. experience=B) and abbreviations (e.g. Occupation=DB Prog) (www.ijcsmc.com). These errors increase reduce sorting ability.
  • Violated Attribute Dependencies – In this type of error, the value of a secondary attribute does not match with the primary attribute. For instance, when the listed city is not in the country or postal zip-code does not match with the mentioned city.
  • Embedded Values – This error occurs when multiple values are entered in the same field. This will restrict data indexing and sorting abilities.

If the data has a lot of errors, it can lead to incorrect decision making, which may adversely affect the growth of your business. These issues can be easily avoided by following proper procedures during the design and execution of cleansing. Outsourcing the data cleansing task to experienced data entry service providers is a feasible way to ensure that data is as accurate and consistent as possible.

About Rajeev R

Rajeev R

Manages the day-to-day operations of MOS from NY. With an interest in information technology, Rajeev has guided MOS to extensive use of digital technology and the internet that benefits MOS as well as MOS clients.