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Require 6-12 months experienced 2 web designers in ARTIS Technologies Pvt. Ltd based in Saltlake City, Kolkata.

Methodology
Disscussions
First Cofee! Discussion about the project scope.project type.nature of work.preferences and style..
This gives me a brief idea what client is really looking for.includes the suggestions,input & a few creative ideas for our side.
Defining Goals
Identifying the goals to achive and target a specific user group or catering genarel audience etc.
Scales.timelines technology to be used etc, are defined in this step,which finaly can lead to an efficient and effective internet / rich media solution.
Design & Development
Screen Designs are now converted into actual working program/ websites.
We use different softwares,language & technologies like Photoshop,Flash,XHTML,CSS,AJAX,javascript etc depending upon the project requirements.
Project Signoff
Final preview of the project is now presented to the client .
Alterations,additions,modifications can be done in this step before finally launching the project.
Launch!!
Deployment ,Testing & Launch!
Client): ARTIS):
 
complete website, perfect website, how to build website
 
Home » Service » data cleansing enrichment

Data Cleansing and Data Analysis Data Scrubbing and Data Enrichment Services

Data Quality: Standardize, validate and improve your information assets
  • Artis's data cleansing/scrubbing, 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  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.

  • Data cleansing : 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. Artis's data cleansing 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.

    Data scrubbing, also called data cleansing, is the process of amending or removing data in a database that is incorrect, incomplete, improperly formatted, or duplicated. An organization in a data-intensive field like banking, insurance, retailing, telecommunications, or transportation might use a data scrubbing tool to systematically examine data for flaws by using rules, algorithms, and look-up tables. Typically, a database scrubbing tool includes programs that are capable of correcting a number of specific type of mistakes, such as adding missing zip codes or finding duplicate records. Using a data scrubbing tool can save a database administrator a significant amount of time and can be less costly than fixing errors manually.

    The issues involved in data cleansing, formatting, converting and preparing for upload are so time consuming and so exacting that it makes sense to outsource select components of the project to an established firm with extensive experience in data migration.



  • Our capabilities for data cleansing/scrubbing and enrichment services include: Data aggregation, organization, and cleansing

    MS Word, RTF, TXT, PDF, HTML, SGML, XML, MS Excel, and others as per your requirement

    Enrichment of data with product attributes, images, and manufacturer specifications

    De-duplication: eliminate duplicate records which might be similar looking records,

    Identification of missing or incomplete data

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  • We offer a cleansing service to clean and tidy-up your data. Depending upon your requirements this may involve: The identification and removal of duplicated records

    The identification and tagging of similar records with subsequent manual review

    The removal of spurious and invalid records

    Data validation (for example using a post code checker to identify that addresses are correct)

    The removal of obsolete data

    The comparison and removal of records matching third party information, such as the opt-in and opt-out list




  
 

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