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Data Cleansing

This is the process of detecting and removing and/or correcting a database's unclean data (i.e., data that is inaccurate, outdated, superfluous, unfinished, or formatted improperly). The objective of data cleansing is not just to clean up the data in a database but also to bring constancy to dissimilar sets of data that have been amalgamated from separate databases. Refined software applications are readily available to clean a database's data by means of algorithms, convention and search for tables, an undertaking that was once done physically and consequently still subject to human miscalculation.

Data cleansing, is the method of adjusting or eradicating information in a database that is wrong, unfinished, inappropriately formatted, or reproduced. A business in a data-intensive profession like banking, insurance, trade, telecommunications, or transportation might use a data cleansing device to methodically inspect data for errors by using a set of laws, algorithms, and search for tables. On average, a database cleansing device consists of programs that are able to correct a number of specific types of errors, for example putting in absent zip codes or detecting duplicate records. Making use of a tool can save a database manager a substantial amount of time and can be less expensive than mending errors manually.

Data cleansing is a vital undertaking for data warehouse experts, database managers, and developers alike. Deduplication, substantiation, and householding methods can be applied whether you are populating data warehouse components, incorporating recent data into an existing operation system, or sustaining real time dedupe efforts within an operational system. The objective is an elevated level of data precision and reliability that transmutes into enhanced customer service, lower expenses, and tranquility. Data is a priceless organizational asset that should be developed and honed to grasp its full benefit.

Data-cleansing methods occur in numerous forms including deduplication, substantiation, and householding. Because of restrictions in the way various transactional systems collect and accumulate data, these practices become a compulsory part of supplying correct information back to the business consumer.

Deduplication guarantees that a single correct record be present for each business unit represented in a business transactional or analytic database. Validation guarantees that every characteristic preserved for a specific record is accurate. Addresses are an excellent candidate for validation procedures where cleanup and conformation procedures are executed. Householding is the method of assembling particular customers by the household or organization of which they are an affiliate. This method has several remarkable marketing connotations, and can also aid cost saving procedures of direct marketing.

Cleansing data prior to it being stored in a reporting database is essential to provide worth to clients of business acumen applications. The cleansing procedure usually consists of deduping processes that put a stop to duplicate records from being reported by the system. Intimate Data's data analysis and data enrichment services can help improve the quality of data. These services include the aggregation, organization, and cleansing of data. These data cleansing /scrubbing and enrichment services can ensure that your databases - part and material files, product catalog files, and item information etc. - are current, accurate and complete.

Often the existing data has no consistent format being derived from many sources. Or it contains duplicate records/items and may have missing or incomplete descriptions. Intimate Data's process fixes misspellings, abbreviations, and errors. The data is normalized so that there is a common unit of measure for items in a class, e.g. feet, inches, meters, etc. are all converted to one unit of measure. The values are also standardized so that the name of each attribute is consistent, e.g. inch, in., and the symbol "are all shown as inch.

The Process
  • Identify authoritative data sources
  • Measure data quality
  • Use business rule discovery tools to identify data with inconsistent, missing, incomplete, duplicative, or incorrect values
  • Use data cleansing tools to clean data at the source
  • Load only clean data into the data warehouse
  • Identify and correct the cause of data defects
  • Schedule periodic cleansing of the source data
The data cleansing process runs in parallel with the data analysis task. As data quality issues are uncovered, the Analysis and Cleansing teams, in conjunction with Business users have to identify:
  • The specific attributes to be cleansed.
  • The Business Rules pertinent to the specific attributes and therefore the level of 'quality' required of the selected set of data.



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