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The challenges of efficiently managing data are significant in today’s in-organic data landscapes. In these landscapes, we can see many legacy data stores and processes that are yet to be discovered along with the data that they produce, distribute and apply. There are many political and adoption barriers that an organization needs to overcome to simplify, appraise these landscapes, better govern data to bring value and reduce risk to the organization. And an enabler of better Governance service operations is to understand the current nuances of data, as it exists in the organization today.
Governing data in today’s world is also about having to naturally “Manage it as a Business Meaning” as rightly put by EDM Council. Most folks feel that Governance activities are a tad over and above regular Data Management. For example, the first intuition that a business analyst gets when a data-mapping artifact needs to be produced is that it would impact the time to market of a critical business change. This is where continuing awareness in organization, while enabling sponsors, data owners, analysts, data stewards and other stakeholders, brings a cultural adoption of Governance. The other way is to standardize this necessary information as organization process assets that bring immediate and cumulative benefits to the organization.
As Data Governance divisions, the need is now stronger to assist organizations make themselves aware of the value that these activities bring forth when embedded tactfully into Business Analysis, Data Analysis & Management, and Architecture disciplines. It is also about rediscovering the importance of producing artifacts like Metadata directories, Data Flow diagrams that have a direct impact on end goal of managing data with excellence. These disciplines of architecture are required to operate with excellence and achieve Data Governance objectives and goals.
Likewise, Governance can be rightly embedded into all phases of Architecture encompassing Business architecture, Information architecture, Process architecture, Systems architecture, and Technology architecture. Unraveling these disciplines informs how data should be Identified, Defined, Modeled, Related, Created, Distributed, Maintained, Applied and Decayed.