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So far in this three-part series on data ownership, I have discussed what data ownership is, why it is important, what the key requirements of enterprise data management (EDM) are and how companies can address the data management problem by standardizing on a suite of technologies, which I referred to as an EDM suite. In this, the third and final paper in this short series, I want to look at what needs to be done from a strategy perspective to be able to establish personnel and procedures for enterprise data management, and what needs to be done in order to leverage the technologies available in an EDM suite to get maximum return on investment.
In the first paper of this series, we outlined three key requirements for enterprise data management. These requirements are:
Having looked at the first of these already in the second paper, we now turn our attention to organizational structure and data governance – concepts that are fundamental to any data management strategy.
One of the key appointments any company can make to help get their data under control is the position of a Chief Data Architect. This is often a position overlooked in IT and sometimes not well understood by business. If it does exist, this person must have a business mandate to cause change so that data can be brought under control. Fundamentally, the job of a Data Architect is to understand how data is used in business on an enterprise-wide basis and to formally define the data used. This individual is also responsible for setting policies and procedures for the use of that data, for maintaining data quality, and for ensuring a common consistent understanding of what data means. Ideally, a Data Architect should have extensive experience in the vertical industry that he or she works so that they can clearly discuss data in the context of its business use. Data Architects must also have expertise in data management skills such as:
Ideally, data architects should have an enterprise-wide remit in the sense that they need to operate across all lines of business when managing data. This is especially important in setting strategy and patterns (best practices) around specific data management processes such as:
Many companies are starting to create centralized IT expertise in business integration by creating Integration Competency Centers so that IT professionals responsible for different types of integration are able to coordinate their work. The data architect is at the center of data management, data quality and data integration and should be a key member of any integration competency center initiative. Figure 1 shows five levels of business integration. Data and metadata integration (and management) underpin and are a key piece of any business integration initiative.
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