Enterprise Architecture

Enterprise Architecture is Not Dead

The death of Enterprise Architecture is grossly exaggerated. Some would have us believe that it is no longer needed, where on-the-fly approaches are inaccurately disguised as ‘Agile’ thinking and claimed to produce superior results compared to sound enterprise planning. No. Business process management, data management, application portfolio management and to a lesser extent technology still have a long way to go before graduating from the Science approach to the Commodity approach.

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Data Models

Why do we encourage the creation, documentation and distribution of data models? From the perspective of a business user that does their daily job in areas such as customer service or operational support, they don’t care how the data is designed they just use the tools and expect it to work. If that is the extent of data usage then data models are not needed. Most organizations desire to also do various types of data processing and analysis: business intelligence, predictive analytics, data integration, and data provisioning to name a few.

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The Many Faces of Data Management

‘Data Management’ is a term often used with very different meaning within an organization. Examples include: to describe someone that receives a call and updates customer information; a team that provides data to automated systems; or a person working with the Enterprise Architect to design data usage. The diagram below illustrates the usual functions within an organization, and where data management may be found. Identifying this within your organization will help reduce confusion, identify overlap of responsibilities, and help optimize the approach to data management.

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Data Value Chain

‘Data Management’ is a term often used with very different meaning within an organization. Examples include: to describe someone that receives a call and updates customer information; a team that provides data to automated systems; or a person working with the Enterprise Architect to design data usage. The diagram below illustrates the usual functions within an organization, and where data management may be found. Identifying this within your organization will help reduce confusion, identify overlap of responsibilities, and help optimize the approach to data management.

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