Established analytic approaches like CRISP-DM stress the importance of understanding the project objectives and requirements from a business perspective, but to date there are no formal approaches to capturing this understanding in a repeatable, understandable format. Decision Requirements Modeling closes this gap. Decision Requirements Modeling is a successful technique that develops a richer, more complete business understanding earlier. Decision Requirements Modeling results in a clear business target, an understanding of how the results will be used and deployed, and by whom. Using Decision Requirements Modeling to guide and shape analytics projects reduces reliance on constrained specialist resources by improving requirements gathering, helps teams ask the key questions and enables teams to collaborate effectively across the organization, bringing analytics, IT and business professionals together. Using Decision Requirements Modeling to document analytic project requirements enables organizations to:
– Compare multiple projects for prioritization, including allowing new analytic development to be compared with updating or refining existing analytics.
– Act on a specific plan to guide analytic development that is accessible to business, IT and analytic teams alike.
– Reuse knowledge from project to project by creating an increasingly detailed and accurate view of decision-making and the role of analytics.
– Value information sources and analytics in terms of business impact.
There is an emerging consensus that Decision Requirements Modeling is the best way to specify decision-making. It is also central to a forthcoming standard, the Object Management Group’s Decision Model and Notation, which will give adopters access to a broad community and a vehicle for sharing expertise more widely.
Decision Requirements Modeling for Analytic Projects

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