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  • Components Involved In PIM Data Quality

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    Overview :

    Data Quality with respect to PIM is the Central components that is Involved in Managing Product Information. Data Quality can be defined as the degree of Quality of Products Data. If the Quality of Product Information is high which implies that Product's Information provided by any Sources is fit for Use in Various Operation in PIM system.

    There are various Component's involved in PIM Data Quality whether that information deals in PIM-Desktop or PIM Media Manager or PIM Web. PIM Data Quality aims in implementing Consistent ,re-usable and de-duplicate Information throughout the Product's Life Cycle. Major Components Involved In PIM are :

     

    • Attribute Extraction
    • Match and Merge
    • Auto Classification
    • Attribute Completeness Checks
    • Terminology checks
    • Field Value Standardization.

    Lets See in Details about It.

    Attribute Extraction

    In PIM, this Attribute Extraction uses Natural Language Processing (NLP) technology. It helps in identifying and Pulling up feature of Products . Also helps in identifying and Pulling up attributes of Items. PIM Supports in scanning the unstructured texts like Media Assets. Automatically Extraction is Possible in Informatica-PIM. It allows for making changes in the information of Products.

     

    Match and Merge

    Match and Merge is used making the information more accurate and consolidating into one Golden Records by Implementing Data Quality Rules.

     

    Field Value Standardization

    The information that is being supplied by Various suppliers needs to standardized so that particular information supplied can be put into Golden Records (Master Data ). After performing Data Quality that data can said to BVT form ie Best Version of Truth.

    While performing Import Operation, Various fields and it's values are transformed into One Form Like “Rohan sharma “ “ R. Sharma “ “R-sharma “ all these Values will be converted to Single Name say “Rohan Sharma “. Now this records can said to be consistent across all the fields of PIM.

     

    Terminology Checks

    This types of checks is usually performed to ensure that Information is not Violating Enterprise Rules and Guideline. Various Profanity checks are implemented in it for conformity check on Products Information. Also, various Rules are set for the Wordings used in the Short and Long Products Descriptions. Due to NLP in PIM enables to scan whole descriptions (short or Long) and based on the rules denied in the guidelines Rejects that Information. Various Reference tables and Look up Tables are maintain in order to check against rules.

    Allowed Value Checks

    Automatic functionality of Look-up ensure attribute / data values match with defined Default values. In some cases, Certain values may be Valid between a specific range for Example :

    • Only valid if “year” is between 1996 and 1998
    • Lead time may not exceed 15days

    Some Attributes of Items values need to provide a default value that can be treated as Predefined Dictionary for Look-up Operation. After defining these predefined values, now PIM Data Quality is responsible for matching the rules against these default Values.

    Attribute Completeness Checks

    Automatic functionality of Look-up ensure the completeness of attributes. Based on the Grouping or Hierarchy we have maintained in PIM System Data Quality helps to perform this check on each data field and its relevant attributes.

    Auto Classification

    With the help of Natural Language Processing technology, It is possible to automatically make group like Tree Like Structure of Product's Data. Grouping or Classification is done on the basis of features, Attributes or descriptions.

     

    Hence, PIM provides the Central Engines to Execute any Data Quality Checks. Based on the Rules these will be executing in PIM Server.

     

    Thanks for Reading the Blog...

     

     

     

     

    Data Quality Data Quality Compoents Usage of Data Quality What are the components involved in Data Quality

 1 Comment(s)

  • I would like to add that the quality of data in the PIM system can also be influenced by the ability to form attribute groups, adaptation of data in accordance with the requirements of a particular channel, mass update function, and also built-in functions of DAM systems, for example, as in the TreoPIM system: https://treopim.com/features
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