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Implementation Plan. It informs our Information Architecture Principles which further expand our vision. It influences our data governance goals. It frames our data management strategies. These are described in more detail in the sections that follow.
Guidelines for Aligning Information Management Concepts, Practice and ContextPage 24 of 65 Data Governance Framework Strategic Plan v2 11 January 2018DBHIDSINFORMATION ARCHITECTURE PRINCIPLES1.Information architecture is the reflection of the business; it is not just a technology domain. This principle is critical to both a successful data governance effort and to individual data management projects. When the business abdicates responsibility for information architecture and data governance to information technologists, it leads to the creation of data silos, disparate data, poor data quality and a focus on activity over value. The business must partner with technologists in data governance and information architecture efforts. (Seiner R. T., 2005) 2.The identification and definition of data attributes must involve the business. When the business does not lead this effort, there is a loss of understanding over time that can neither be fixed nor replaced through the efforts of information technologists alone. For business-critical data elements, the respective business units must identify data ambassadors within the organization that can maintain the integrity of data definitions and approve the appropriate use of data for the desired purpose. (Seiner R. , 2006) 3.Data is an organizational asset and must be managed with an enterprise perspective. Once the business has taken responsibility for its role in data governance and data ambassadors are identifying and defining data attributes, the data must be managed at an enterprise (central) level. Data management decisions cannot be made at the system or program level. Because the data is an enterprise asset, decisions regarding how it is managed must also be made at the enterprise level. (Council for Information Advantage, 2010) 4.Data that is common to more than one business unit must be defined through consensus by representatives of those business units. It is essential that data that is used by more than one business be defined by representatives of all of the business units. When units are not represented in decision making, their specific needs may not be reflected. This is what leads to units creating their “own” versions of common data, as they are unable to use the “official”data. This process of business participation in the definition of common data is called data governance. (Wilder-James, 2013) 5.The value of data to the enterprise is in its fitness for reusability, not its exclusivity. To process data and exploit only the result of the calculation is short-sighted. Even worse is to lock it away. The practices and tools of effective data management cannot stand alone in the data ecosystem. They must rely on and support the reusability of data. The organization benefits when both data management efforts and results form a platform for future discovery and innovation.