The IBM Data Governance Unified Process

I recently published a book called the “IBM Data Governance Unified Process.”  As I state on the back cover, Data Governance can be like the blind men and the elephant.  Depending on which part of the elephant you touch, you will equate Data Governance with one or more of metadata, business glossaries, master data governance, analytics governance, security and privacy, and information life-cycle governance.

All these definitions are correct, but they are incomplete. Data Governance is about setting policy around data to treat is as an enterprise asset.  Your enterprise needs to protect, leverage, and optimize its data in much the same way as it would any tangible asset such as a building or equipment.

The book walks through a series of 14 steps and 93 sub-steps to implement a Data Governance program.

About sunilsoares

Data Governance Practitioner
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2 Responses to The IBM Data Governance Unified Process

  1. matthew tan says:

    hi Sunil, regarding the reference data, which are usually fixed and can be used as reference standard, segment seems to be quite volatile and ephemeral in some industries practice to be used as reference data. The distinction between Master Data (which does not change frequently, e.g. name, location, contact, DOB, etc) and transactional data (which is frequently changed, updated, e.g. sales amount, revenue, etc) also need to be made clear. The concept of Big Data seems to be evolving towards the domain of unstructured data and semi structured data, and hence Big Data Governance is putting controls and enhancing the integrity of unstructured data from which the consumers of such Big Data does not have influence or control at the input sources.

    • sunilsoares says:

      Thanks Matthew, I agree that the governance of big data is becoming increasingly important but the domains of metadata, data quality, information lifecycle management, and privacy still apply. In addition, organizations are starting to integrate the governance of their big data, master data, and reference data. A classic example would be enriching a customer master data profile with Facebook data (whether that consitutes acceptable use is a different matter).

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