gee, you know, the brain seems to have some of the properties that one would need for real stability.”2
The only way to design such a system would be to break each commu- nication into short, fixed-length pieces that could be addressed to another station and routed through the network to their ultimate destination. That meant digital, not analog, technology, as digital technology allowed infor- mation to be moved from one place to another in packets, which were then reassembled. “By dividing each message into parts,” Hafner and Lyons write,
“you could flood the network with what [Baran] called ‘message blocks,’ all racing over different paths to their destination. Upon their arrival, a receiving computer would reassemble the message bits into readable form.”3
Although Baran eventually convinced the Air Force to pursue the idea, American Telephone & Telegraph (AT&T) was unimpressed. “After I heard the melodic refrain of ‘bullshit’ often enough, I was motivated to go away and write a series of detailed memoranda papers to show, for example, that algorithms were possible that allowed a short message to contain all the information it needed to find its own way through the network.”4Five years after starting the project, Baran finally hit the wall when the newly created Defense Communications Agency was put in charge of the effort. Conclud- ing that the new agency would “screw it up,” Baran moved on to other work.
Baran’s idea lived on, of course. With funding from the Defense Department’s Advanced Research Projects Agency (ARPA), Baran’s “packet- switching” system was eventually built in 1967. Called ARPANET, it had seven nodes, the first at UCLA and the seventh at RAND. By the 1980s, ARPANET had become the Internet, and the rest, as they say, is history.
focused on firing overcompensated, underperforming chiefs—the rate of CEO dismissals increased by 170 percent from 1995 to 2003.” Unfortunately for CEOs, the departures bore almost no relationship to performance, cre- ating even more pressure for change. “At some juncture, the prospect of forced dismissal will seem so likely that it will hang like a cloud of misery over a chief executive, undermining his or her ability to perform,” the Booz Allen study argued. “Concerned about their mortality, CEOs will try to get even more done quickly, emphasizing quick fixes at the expense of com- pany transformation.”6The problem is not too little change, but too much.
RAND might argue that the best way to counter the pressure is to ask what matters most to high performance, which is exactly how its five- member team developed the organizational blueprint for a state-of-the-art human tissue bank.7Recognizing that progress in the fight against cancer depends on access to a network of high-performing organizations, the team began looking for best practices at 12 of the nation’s leading tissue banks.
Although some of these banks were operated by federal agencies, such as the National Cancer Institute, some by universities, such as Duke University, and some by private firms, such as Ardais Corporation and Genomics Col- laborative, Inc., all of them shared a common demand for organizational capacity, including the technology to register and track specimens (bioinfor- matics), storage facilities, inventory control, shipping procedures, and quality assurance systems.
The one thing tissue banks do not share is national standards for col- lecting and storing specimens, which can undermine confidence in the resulting research. In a market in which samples must match protein for protein, banks must be more alike than different, especially when it comes to collecting, identifying, and storing tissue. Hence, RAND has recom- mended a new kind of bank built around a network of geographically dispersed organizations that follow the same protocols covering all facets of the process.
The list of best practices includes everything from sample collection to freezer maintenance and backup; there are nine recommendations on sample collection, nine on processing and annotation, five on storage and distribution, five on information management, six on identifying consumer/user needs, five on business planning and operations, six on pri- vacy and ethical systems, five on intellectual property and legal issues, and two on public relations, marketing, and education.
On sample collection and processing, for example, the team urges standard operating procedures for collecting all samples, clear guidelines for processing each sample within one hour of collection, bar codes for tracking individual shipments both to and from each tissue bank, a quality
assurance process for ensuring compliance, around-the-clock monitoring of storage conditions, and frequent employee training. On business planning and communications, it recommends close contact with collection staff at all sites, continuous review of new technologies for improving performance, a cost-accounting system for tracking each stage of the process, a market- ing plan for increasing access, and outreach to donors and researchers alike on the benefits of a standardized system. In a sentence, high-performing tissue banks must be alert to changing research demand, agile in collecting and distributing samples, adaptable to new technologies and research, and aligned around a core set of operating procedures that assure quality.
It is impossible to implement these best practices without some minimal organizational capacity, however. Tissue banks cannot create standard oper- ating procedures without at least some business planning, expand their donor networks without a communication plan, track their operating costs without a decent accounting package, track their samples without scanning technol- ogy, or learn new languages without training.
As RAND’s team notes, all of the existing banks follow at least some of the best practices, but none follow all 52. Ardais is a leader in quality assurance, for example, but it is not at the top in creating broad networks of academic and community medical centers, whereas Genomics Collabo- rative is a leader in setting high ethical standards on everything from informed consent to patient confidentiality, but it is not at the top in review- ing researcher submissions and credentials.
The question for this chapter is not whether R AND has learned a great deal about designing a high-performing tissue bank, however, but whether there is a set of core organizational characteristics that can make high performance more likely. It is one thing to compile a long list of best practices, and quite another to identify the underlying infrastructure that is needed if an organization is to succeed in a rapidly changing environment.
Of all the things that organizations can do to improve performance, which matter most in producing the high performance that RAND has observed over the years?
This chapter uses a statistical winnowing process to provide a first set of answers. If the voluminous literature on organizational change is right, high-performing organizations are practically perfect in every way. They have clear goals, adequate funding, strategic plans, flat hierarchies, talented leaders, rigorous metrics, powerful incentives, a focus on results, and tight management systems.
But as the rest of this chapter suggests, these characteristics do not demonstrate equal statistical power in explaining success. When character- istics are tested against one another through an ever-tougher set of statistical
tests, some turn out to be surprisingly weak, while others show surprisingly strong staying power as the winnowing progresses. Moreover, as this chapter shows, RAND researchers share some clear agreements on what matters most for predicting high performance in the organizations they know best.