srinivas vadlakonda

• Oct 6th, 2006

 

clearning means cleaning the data like filtering and merging before loading the data into datawarehouse

jessa

• Oct 25th, 2006

 

data cleaning is removing discrepencies from the record like removing duplication , redundancy etc..in short making the data as relevant as can be made for the ultimate purpose of business analysis

jyothi

• Feb 10th, 2007

 

Data Cleaning is a process of avoiding the unnecessary information in the process of data maintainance. Data Cleaning can be done by using clustering

chinnodu

• Mar 2nd, 2007

 

It is a process of identifying and changing the inconsistencies and inaquerecies

Hakoonamatata

 Profile Answers by Hakoonamatata 

• Jul 9th, 2007

 

Data cleaning is a self explainatory term. Most of the data warehouses in the world source data from multiple systems - systems that were created long before data warehousing was well understood, and hence without the vision to consolidate the same in a single repository of information. In such a scenario, the possiblities of the following are there:
1. Missing information for a column from one of the data sources;
2. Inconsistent information among different data sources;
3. Orphan records;
4. Outliar data points;
5. Different data types for the same information among various data sources, leading to improper conversion;
6. Data breaching business rules

In order to ensure that the data warehouse is not infected by any of these discrepencies, it is important to cleanse the data using a set of business rules, before it makes its way into the data warehouse.

sub_guha

 Profile Answers by sub_guha 

• Jan 24th, 2010

 

Data cleaning, technically called "Data Cleansing"is a group of methods for making data more reliable and accurate. Usually companies store data in warehouses so they can make meaning out of it and take related decisions; so if the data is un-reliable or in-accurate the decisions are usually the same, leading to millions of dollars loss.

How can we cleanse:

Standardization - making data follow same rules, notifications, codes

Enrichment - filling in missing data based on some reference value (eg. City name)

De-duplication - finding and removing seemingly same but actually duplicate data 

Validations - commonly used for making sure data follows business rules