For years, the Swiss pharmaceutical giant Roche Group worked hard to create an ultra-competitive culture that...

 

  

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For years, the Swiss pharmaceutical giant Roche Group worked hard to create an ultra-competitive culture that pitted scientific teams against one another in fighting for scarce resources. Roche had believed that this culture was instrumental in creating such blockbuster drugs as Valium and Librium. But, on the downside, this approach made it almost impossible for scientists to abandon faltering projects that they felt were pivotal for their careers. Rather, it led them to hoard their technical expertise and findings. In 1998, the company turned to a more collaborative style of teamwork-especially for its teams working in the new field of genomics. Roche began running ads in Science magazine for a young new breed of researchers who could reinvent themselves as their job opportunities rapidly changed. It was the new breakthroughs in genomics and molecular biology that pushed Roche to change the way it hunted for drugs. Roche knew it had to speed up the discovery process for new drugs and size up toxicity risks earlier than ever. Projects needed to be managed in a totally different way. Roche can now chum out 1 million genomics experiments a day. Whereas research teams once spent years looking for a single good idea, they now must consider hundreds or even thousands of candidates daily. The data that is generated is overwhelming not only for the researchers, but also for Roche's large infrastructure of computers. Despite the daunting task, the potential is too great for Roche to ignore. At a media briefing, Roche Group chairman and CEO Franz Rumer declared, "Look at this revolution of genetics, genomics, and proteomics. It's becoming ever clearer that we will be able to identify early the predisposition of people to disease-and to monitor and treat them more effectively. We'll develop markers for cancer. That will lead to better test kits and to new pharmaceuticals." Thus, Roche's U.S. pharmaceuticals headquarters is making adjustments to deal with having too much data, too fast.' Roche's management has recognized that it needs to rethink the best ways to build teams, hire people, and create a culture where failure is all right, as long as it is fast. Roche has had to embrace an organizational revolution to accommodate the technological revolution. Learning to Swim in a Deluge of Data At the heart of the genomics explosion is the GeneChip. This carefully mounted piece of darkened glass, about the size of a thumbnail, can contain up to 12,000 tiny marks. Each mark represents a human gene-one amino acid at a time. 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 researcher 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 through the massive number of drugs to find the few promising drugs that offered the greatest likelihood of success. To solve its screening bottleneck, Roche installed a ultra-high-throughput machine made by Carl Zeiss at a cost of more than $1 million. "We can test 100,00 compounds a day," says Lamie Myer, the technical robotics expert who runs and maintains the screening machine. Though most of those compounds don't work out, identifying just a few hits within several weeks of testing can speed up Roche's overall efforts. The Zeiss machine ultimately has led to changes in the entire research process. Change Everything-One Piece at a Time Genomics could dramatically change things at Roche: In Palo Alto, researcher Gary Peltz built a computerized model of the mouse genome that allows him to simulate classical lab studies in a matter of minutes. In Iceland, Roche teamed up with Decode, a company which researches Icelandic genealogical records. Decode used the data it had collected to identify and locate genes that are associated with stroke and schizophrenia. In Nutley, genomic data is being used to size up a drug's side effects before embarking on lengthy animal experiments. Each of these initiatives runs on a different timeline. Some parts of Roche will see dramatic business changes in a year or two, while others will not see changes for much longer "This isn't just a matter of turning on a light switch," says Klaus Lindpaintner, Roche's global head of genetics research. Discussion Questions 1. Read chapter 3. How does the business strategy affect information systems and organizational decisions (in general)? What is the relationship between the three strategies (in general)? You don't have to answer this question in relation to what was happening at Roche. 2. What business strategy (business goals and how to get there) at Roche is supported by the information systems described above and how? For years, the Swiss pharmaceutical giant Roche Group worked hard to create an ultra-competitive culture that pitted scientific teams against one another in fighting for scarce resources. Roche had believed that this culture was instrumental in creating such blockbuster drugs as Valium and Librium. But, on the downside, this approach made it almost impossible for scientists to abandon faltering projects that they felt were pivotal for their careers. Rather, it led them to hoard their technical expertise and findings. In 1998, the company turned to a more collaborative style of teamwork-especially for its teams working in the new field of genomics. Roche began running ads in Science magazine for a young new breed of researchers who could reinvent themselves as their job opportunities rapidly changed. It was the new breakthroughs in genomics and molecular biology that pushed Roche to change the way it hunted for drugs. Roche knew it had to speed up the discovery process for new drugs and size up toxicity risks earlier than ever. Projects needed to be managed in a totally different way. Roche can now chum out 1 million genomics experiments a day. Whereas research teams once spent years looking for a single good idea, they now must consider hundreds or even thousands of candidates daily. The data that is generated is overwhelming not only for the researchers, but also for Roche's large infrastructure of computers. Despite the daunting task, the potential is too great for Roche to ignore. At a media briefing, Roche Group chairman and CEO Franz Rumer declared, "Look at this revolution of genetics, genomics, and proteomics. It's becoming ever clearer that we will be able to identify early the predisposition of people to disease-and to monitor and treat them more effectively. We'll develop markers for cancer. That will lead to better test kits and to new pharmaceuticals." Thus, Roche's U.S. pharmaceuticals headquarters is making adjustments to deal with having too much data, too fast.' Roche's management has recognized that it needs to rethink the best ways to build teams, hire people, and create a culture where failure is all right, as long as it is fast. Roche has had to embrace an organizational revolution to accommodate the technological revolution. Learning to Swim in a Deluge of Data At the heart of the genomics explosion is the GeneChip. This carefully mounted piece of darkened glass, about the size of a thumbnail, can contain up to 12,000 tiny marks. Each mark represents a human gene-one amino acid at a time. 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 researcher 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 through the massive number of drugs to find the few promising drugs that offered the greatest likelihood of success. To solve its screening bottleneck, Roche installed a ultra-high-throughput machine made by Carl Zeiss at a cost of more than $1 million. "We can test 100,00 compounds a day," says Lamie Myer, the technical robotics expert who runs and maintains the screening machine. Though most of those compounds don't work out, identifying just a few hits within several weeks of testing can speed up Roche's overall efforts. The Zeiss machine ultimately has led to changes in the entire research process. Change Everything-One Piece at a Time Genomics could dramatically change things at Roche: In Palo Alto, researcher Gary Peltz built a computerized model of the mouse genome that allows him to simulate classical lab studies in a matter of minutes. In Iceland, Roche teamed up with Decode, a company which researches Icelandic genealogical records. Decode used the data it had collected to identify and locate genes that are associated with stroke and schizophrenia. In Nutley, genomic data is being used to size up a drug's side effects before embarking on lengthy animal experiments. Each of these initiatives runs on a different timeline. Some parts of Roche will see dramatic business changes in a year or two, while others will not see changes for much longer "This isn't just a matter of turning on a light switch," says Klaus Lindpaintner, Roche's global head of genetics research. Discussion Questions 1. Read chapter 3. How does the business strategy affect information systems and organizational decisions (in general)? What is the relationship between the three strategies (in general)? You don't have to answer this question in relation to what was happening at Roche. 2. What business strategy (business goals and how to get there) at Roche is supported by the information systems described above and how?

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1 In general the business strategy of an organization can have a significant impact on its information systems and organizational decisions Heres how ... View the full answer