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Data Quality Assurance and Control Guidelines
Data Quality Assurance Management Guidelines, is the central reference point to understand the Data Quality Assurance mechanisms and controls. These are the mechanisms,  one needs to incorporate, while designing the IT systems and business processes. This document forms and annexure to the Data Quality Policy.
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    • Abstract

    Header Information;

    • Tool-Code: DQ01WT30
    • Product-Code: BIPM-DQPACK02
    • Help File (excluding FAQs and Tips): 8 pages
    • Relevant FAQs- 2
    • Relevant TIPs- 4

    Table of Contents:

    • Back-ground
    • Purpose and Objective
    • Document Description
    • When and how to Use?
    • Interface Controls
    • Batch-Process Control Guidelines
    • Input Control Guidelines
    • Domain and Data Standards Controls
    • Data-Model Controls
    • Business Process Controls
    • Business Partner Controls

    Overall Usage Guide

    Purpose and Objective

    Data Quality Assurance Management Guidelines, is the central reference point to understand the Data Quality Assurance mechanisms and controls. These are the mechanisms,  one needs to incorporate, while designing the IT systems and business processes. This document forms and annexure to the Data Quality Policy.

    Who uses it?

    The DQ Assurance Guidelines documents is owned by the enterprise data steward. Ideally it should be only updated by the Data Steward or a designate. The users of this document are the analysts and designers of the systems and process initiatives. The document is reviewed by the CIO, CFO and other executive stakeholders.

    When is it used?

    The document has not specific check-point or phased based usage. It is a central reference (like Data-Group Master, Data Management Standards for Data Entities, Data Quality Policy.

    Linked Practice Tools:

    Relevant FAQ:

    • How do we measure the adherence to the Data Quality and Control guidelines?
    • Can we have different insert, update and delete rules be different for the same entity (because of valid reasons) in two different systems?

    Relevant TIPs:

    • Evolve your data quality assurance and control guidelines
    • Cascade your standards and guidelines (to business partners and Vendors)
    • Data Management Standards for Data Entities will be a mix of collaboration and top-down
    • Data Management standards for data entities are not for IT systems
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