Data cleansing - also called as data scrubbing - is a process of detecting and removing and/or amending the errors, inconsistencies & abbreviations in a database ( part and material files, product catalog files, and item information etc) that could be incorrect, redundant, out-of-date, incomplete, improperly formatted or duplicated to improve its quality. Organizations can transform and combine disparate data, remove inaccuracies, standardize on common values, parse values and cleanse dirty data to create consistent, reliable information and help reduce unnecessary costs wastage.

An organization in a data-intensive field like banking, insurance, retailing, telecommunications and transportation might use a data scrubbing tool to systematically examine data for flaws by using rules, algorithms, and look-up tables.

Data Cleansing is multi-faceted process, and a number of problems must be addressed to solve any particular data-cleansing problem. The process of Data Cleansing involves standardizing, validation and correction of data to maximize integrity and value.

Data Entry Help a leading data entry and data conversion outsourcing company, specializes in data cleansing processes. We offer all type of Data Cleansing, Data Analysis, Data Scrubbing and Data Enrichment Services. We provide high level of accuracy, timely deliveries, total confidentiality and cost effective Data Cleansing services. We use sophisticated software packages that assures 99 percent accuracy. Our cleansing process consists of numerous methods that extract data from the source database, transforming them (cleansing), and loading it back to a target database to attain a homogeneous data pool. It also elimination all post-editing processes.

We provide following data cleansing outsourcing services :

• Data aggregation, organization, and cleansing
• Detection and Elimination Duplicates in any data
• Identify and Tag Similar Records
• Enrichment of data with product attributes, images and manufacturer specifications
• Referential integrity checks
• Iinterlink or consolidate multiple data sources
• Data validation (for example using a post code checker to identify that addresses are correct)
• Create reusable data quality business rules that are callable through custom exits, message queues and Web services
• Raw Data to MS-Word Conversion
• Remove Obsolete Data
• The removal of spurious and invalid records
• Identification of missing or incomplete data
• Page Maker to Adobe PDF Conversion
• Format conversions
• Grouping, process tracing