Products - Metadata Crawler

Metadata Crawler uses various data profiling techniques to discover and catalog undocumented metadata associated with the global data landscape and build an integrated Enterprise Metadata Repository.

WHO SHOULD USE THIS PRODUCT?

  1. Enterprise Data Managers
    Who wish to get a comprehensive idea about the characteristics and patterns of their enterprise landscape.

  2. ERP / BI /CRM Project Managers
    Who need to create enterprise metamodels based on the quality and content of their enterprise landscape.

  3. CIOs
    Who wish to compile a full catalog of the characteristics and quality of their company's information assets.

WHAT PROBLEM DOES IT SOLVE?

Metadata Crawler is used for discovering and reporting undocumented metadata by profiling the data within the enterprise landscape. Data profiles provide accurate information about the content, quality and structure of the data

HOW IS IT USED?

Metadata Crawler is used for

  1. Profiling the information sources in global information landscape to discover undocumented metadata related to the following

    1. Content -duplicates, nulls, uniqueness etc
    2. Length and length variation
    3. Value based analyses
    4. Frequencies
    5. Patterns
    6. Domains
    7. Dependencies
    8. Relationships

  2. At a glance identification of discrepancies and errors in the data landscape which become visible as data is profiled.

  3. Classifying the information landscape based on the parameters discovered by data profiling

  4. Cataloging the various parameters discovered by data profiling in an enterprise metadata repository

  5. Presenting reports on the various characteristics of the data discovered by data profiling.

WHAT ARE THE BENEFITS?

Metadata Crawler can help companies get a comprehensive understanding of their company's data environment. It can help IT managers answer questions related to

  1. The hidden characteristics and the quality of their enterprise landscape.
  2. The relationships, dependencies and classifications in the data landscape.
  3. Discrepancies and anomalies in the data landscape


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