Our master data management approach

mdm methodologyMaster data management is a discipline for creating and maintaining a trusted source of reference data. While technology is an important enabler, it should not be the driver.

We recommend a four step process for initiating and implementing master data management.


    Business Alignment

    Business engagement and alignment are critical to ensuring that the investment in master data management, which can be expected to run into several years and millions of dollars for a large enterprise, delivers on business expectations.

    In our experience, master data programs that are IT driven and do not have strong business engagement end up perceived as failures, irrespective of what is delivered.

    Business vision and goals can be used to identify the accountable business executives that must be engaged in order to define the master data scope and objectives.

    We recommend that these executives form the core of a strategic data governance program to determine and prioritise the financial benefit that data brings to the organisation and identify and mitigate the business risks associated with poor data practises and quality

    Canonical data model

    Master data should be defined in terms of a best practise set of attributes that represent and ideal data set able to support the business goals.

    This ideal data set can be described as a canonical data model and should be defined to allow the organisation to map current and future data requirements. Industry standard models may be sourced and adapted, or a model may be built based on defined business requirements.

    Existing data sources should be mapped to this logical model to ensure completeness and viability, and to support the implementation.

    In a large environment we would recommend starting with a small subset of two to five systems that will provide the key data. Additional systems can be mapped and added later.

    Data standards, terms and definitions

    From a data governance perspective it is valuable at this time to define definitions of key data terms, identify and prioritise existing sources for key data, and define and identify categories such as sensitive or protected data that may need special handling during implementation. Data standards, validation rules and match rules should also be defined and agreed with the accountable business users.

    A master data readiness assessment is a valuable tool to both discover de facto rules and standard that may exist within the data, and also to identify data risks that must be mitigated in order to deliver master data management.

    These data governance and data quality steps are important prerequisites for successful master data management.

    Technology identification and implementation

    The three stages above create the foundation for a successful master data implementation. We are now ready to assess technologies to support the master data implementation.

    We should have a clearly defined and business driven scope that includes:

    • Business goals and priorities
    • A canonical data model with source to target mapping
    • Data standards, validations, match rules and risks that must be catered for during implementation.


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