Harvard Business Review - January 2009 by Harvard Business School Publishing

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Some details of our approach are presented in sections 3 and 5. 2. r to reduce uncertainty. Uncertainty is the driving force for information processing requirements26 • Tushman and Nadler basically distinguish two types of uncertainti7 • The first type is uncertainty in the sense that needed information is absent. The second type is called equivocality. Equivocality means that there may be a couple of possibly conflicting interpretations of a situation. In the case of uncertainty, more information is beneficial in any case since it reduces the degree of uncertainty.

Decision Sciences Vol. 22, 1, Winter 1991, pp. 104-119. Lederer, A. ; Sethi, Lim, E. : Entity Identification in Database Integration. In: Proc. 9th Conference on Data Engineering, Vienna 1993, pp. 294-301. : Information Engineering. Book I: Introduction. Englewood Cliffs, NJ. (1989). : Infonnation Engineering. Book II: Planning and Analysis. Englewood Cliffs, NJ. (1990). : Database Schema Design: A Perspective from natural Language Techniques to Validation and View Integration. , Kouramajian, V.

We argue that even in heavily decentralized organizations there is a need for data sharing. 2s. Companies will have to deal with heterogeneous database systems and will have to use information 'distributed' over various such systems. g. , 1986 or Sheth, Larson, 1990. cf. Coulson, 1982. cf. Seitz, Stickel, 1992 and Ktlh1mann, Stickel, 1990. cf. Lederer, Sethi, 1990. cf. Sprague, McNurlin, 1993, p. 106. cf. O'Brien, 1990, p. 22. cf. Huber, 1990. cf. , 1992b. cf. Sheth, 199 I. A Business Process Oriented Approach to Data Integration 37 We agree on the argument that top down development of enterprise-wide views is a rather complex and expensive task and that other strategies should be developed and investigated.

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