Case Study: Roche's New Scientific Method & Google
2311 Words10 Pages
KEY TERMS business diamond (p. 34) business strategy (p. 26) cost leadership (p. 27) differentiation (p. 28) focus (p. 28) hypercompetition (p. 30) IS strategy (p. 37) Information Systems Strategy Triangle (p. 23) managerial levers (p. 36) mission (p. 25) organizational strategy (p. 34) shareholder value model (p. 29) strategy (p. 25) unlimited resources model (p. 30)
1. Why is it important for business strategy to drive organizational strategy and IS strategy? What might happen if business strategy was not the driver? 2. Suppose managers in an organization decided to hand out laptop computers to all salespeople without making any other formal changes in organizational strategy or business…show more content… When speciﬁc genes are activated in an experiment, they light up against the chip’s dark background. The genes that light up might be markers for disease. The GeneChip is a true innovation that must be used effectively throughout Roche. For example, computer capacity must be used effectively. Each sample run on a GeneChip set generates 60 million bytes of raw data. Basic analysis on each GeneChip set adds 180 million bytes of computer storage for each set. Given that Roche ran 1,000 GeneChip experiments in both 1999 and 2000, it is not hard to believe that the storage requirements were mind-boggling. ‘‘Every six months, the IT guys would come to us and say, ‘You’ve used up all of your storage,’’’ states Jiayi Ding, a Roche scientist. In early 1999, Roche’s computer-services experts at Nutley were already concerned that ten researchers working on GeneChip experiments (out of the 300 employees at the site) were hogging 90% of the company’s total computer capacity.
Fail Fast, So You Can Succeed Sooner
One of the biggest challenges in drug research— or in any ﬁeld—is to let go of ideas that are no longer promising and to move on to brighter prospects that aren’t being given enough attention. When new hire Lee Babiss arrived from archrival Glaxo to head preclinical research, he preached a simple message: Fail fast. He knew that the best hope of ﬁnding the right new drugs was to spend less time on dead-ends. Screening was needed to sift though