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Top 15 Data Cleansing Procedures That You Must Know

Top 15 Data Cleansing Procedures That You Must Know

Enriched data is a major prerequisite when it comes to making business decisions as in most of the cases; dataset quality turns out to be poor owing to numerous circumstances. Data inconsistency thus creates gaps leading to major setbacks during data processing. Such lacks in business decisions then lead to extreme costs for the organization. Hence, it is extremely important for the data managers to ensure proper data cleansing procedures. Outsourcing experts in the present scenario, highly press upon the removal of data inconsistency cleansing data on various levels.

Now, there will always be setbacks arising adhering to data inaccuracy and inconsistency which can simply be listed as follows:

  • High volumes of data – Loading data from data warehouses carry a significant amount of dirty data and data errors. This anyway forges the way for data cleansing.

  • Mis-spellings – Typing errors are what generally lead to database constraints. Spelling mistakes are also way difficult to identify and correct.

  • Lexical errors – Name discrepancies define lexical errors when a structure of data items and specified formats render inconsistent.

  • Mis-fielded values – Entered values might be syntactically correct but may not belong to the specified field.

  • Domain format errors – If the value for a particular attribute is correct but does not comply with the format of a domain.

  • Irregularities – Non-uniform usage of values as in different currencies in salary lead to inconsistency in values.

  • Missing values – Non-existence of data in fields signify unavailability as well as dummy values and null values.

  • Contradiction - Error occurs when the same real-world entity is described by two different values in data.

  • Duplication – Same data can be mentioned several times due to some collection or collation error where a minor difference in a thing like the middle name might create two fields.

  • Integrity constraints – Values that don’t fall in the desired set of requirements like a 13th month.

  • Cryptic values or abbreviations – Commonly used abbreviations would definitely pave way for data inconsistency.

  • Violated Attribute Dependencies: When data in two columns are inconsistent as in where a wrong pin code is specified for the stated city.

  • Wrong references - Inhibit data validation and result in data mismatch.

  • Embedded values – Collation of multiple values in same fields.

Data cleansing services thus come within intricate bounds for any business and this is where a trusted third party is required to take over the burden. We, at Hvantage Technologies, believe in perfect precision for your data management and assist in catapulting your data services ten folds.


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