This section contains brief background descriptions of the three knowledge work domains used as examples throughout this book: architecture, clinical research, and financial trading. These example domains show the 100 envisioning ideas “in action” in specific contexts. By including three domains instead of one, each envisioning idea presents an opportunity to illustrate useful parallels and commonalities that can be drawn across very different types of work practice.
The following background content is greatly simplified when compared to the complexity of real work in any one of these three fields. The same can be said for the related examples found throughout the 100 envisioning ideas themselves.
Specialists in these professions will likely find this book’s descriptions of their vocations to be lacking in important specifics. They are. Please note that these omissions are intentional.
This text is a fast access reference to key ideas that can improve application envisioning of knowledge work tools, not a comprehensive sourcebook for any one profession.
Architects and their firms, generally speaking, seek to profitably create well designed drawings for buildings that address complex criteria. These criteria can be set by diverse stakeholders such as clients, civil engineers, government regulators, and the general public. Architects also set many criteria themselves, based on their training and their personal perspectives on what constitutes good design. To reach these aims, architects frequently transition between synthetic creativity and highly analytical problem solving. The process of arriving at agreed upon building designs, and carrying them forward through construction, can involve many different types of activities and work processes. For this and other reasons, teams of architects and consultants, rather than a single individual, are often
responsible for the design of any given project.
Visions of interactive applications in architectural practice began relatively early in the history of computing and continue to hold remarkable promise for future expansion (see the earlier mention of Douglas Englebart’s landmark application concept on page 16). These technological possibilities have been tempered by the established professional cultures in many architecture firms, which have historically been relatively slow to adopt available computing tools. At the time of writing, for an important range of reasons that are likely to persist for some time, a considerable amount of architectural practice is still being
accomplished outside of computing environments.
During the intervals of a project where architecture firms do frequently turn to interactive applications, they may use a variety of products, including computer aided drafting (CAD) and other tools for exploring, visualizing, simulating, presenting, revising, detailing, and communicating design possibilities. While some of a firm’s applications are usually tailored specifically for architectural practices, architects also employ standard productivity tools and other general purpose products as part of their technological repertoires.
The generations of architects working today have varying
desires and expectations for their own use of interactive applications. Some of the more experienced, senior architects have remained reticent about using computing in tasks that the
majority of architects now exclusively accomplish on screen. These experienced professionals often focus on how computing tools can limit the expressiveness and clarity of architectural outputs, while at the same time adding a high degree of learning, abstractness, and complexity to their own work practices. This reticence is in stark contrast to new practitioners in the field, who are expected to have a standard set of skills that includes effective operation of many of the latest computing tools. In between these two extremes are practitioners that are highly skilled at using “their” favored, proven products, and can make these chosen tools fit a wide variety of situations.
At the time of writing, a subset of leading architecture studios has a strong interest in adopting new technologies to accomplish their aims. Some even consider their use of advanced computing applications as one of their key differentiators in the marketplace. Many of the expressive, curvilinear, and asymmetrical geometries found in contemporary architecture would be effectively impossible to resolve without the type of interactive explorations that are available within contemporary computing. Additionally, some cutting edge architects have become interested in how certain tools can programmatically generate novel forms and based on iteratively defined rules and constraints.
A key, recent development in the industry has been the introduction of Building Information Modeling (BIM), a term that encompasses an emerging class of computing applications that is beginning to drive radical changes in architectural practice. In BIM, the entire design of a building is stored as a collaborative virtual model that can be modified and referenced by different contributors to a project, purportedly improving communication and reducing representational misunderstandings. Since BIM inherently presents many of the challenges that can occur when attempting to support collaborative work with interactive applications, a hypothetical “building modeling application” appears throughout the architectural examples included in the 100 envisioning ideas.
The fictional architect in this book’s examples works at a medium sized, cutting edge studio with a robust computing infrastructure. She is still in the relatively early phases of her career, though she already has her eye set on becoming a partner some day or starting a similar practice elsewhere. At her level of seniority, she is a generalist, with responsibilities that range from client workshops to iteratively developing design and construction documents. She is part sketchbook dreamer, part diplomat, and part detail oriented workhorse. Her workplace goals include:
Surpass, or at least meet, client expectations
Create appealing, functional, high quality designs
Incorporate compelling ideas and ”good design” into building drawings
Collaborate effectively to meet project budgets and timelines
Contribute to award winning work that impresses
partners in her firm
Clinical research scientists, generally speaking, want to make applied discoveries related to human health. These scientists adopt diverse methods and technologies to attack their research problems, depending on the nature of the topic under study and researchers’ own areas of expertise. Different research questions and methodological approaches are often funded and staffed at different levels, though these levels can change drastically when promising results appear. Ad hoc procedures can quickly become established protocols as a clinical lab’s efforts progress from minimally staffed explorations to a larger,
production workforce of experimentation.
Life scientists, a larger category to which clinical researchers can be said to belong, were relatively early users of computing, and they have continued to drive some of the most exciting progress in the application of interactive tools to knowledge work. Although time spent at the laboratory bench has remained a staple of many clinical research activities, extensive onscreen work has also become part of the essential character of these scientists’ working lives.
Clinical research labs differ in their adoption of specialized computing tools, based in large part on their budgets and the character of their research. Labs with limited computing infrastructure often focus on storing experimental data in a central repository and providing laboratory staff with typical productivity applications, which they may then supplement with a variety open source tools. At the time of writing, clinical labs with more extensive computing infrastructure have the option to adopt technologies for nearly every stage of experimental workflow, ranging from sample preparation robotics and automated
instrumentation, to specialized analysis software for data
mining, to electronic laboratory notebooks for keeping track of experimental progress. To the uninitiated, stepping into a large, well funded lab can feel something like stepping into some futuristic version of an industrial production line, with many
stations and the buzz of human and machine activity.
Many clinical research labs study the genetic properties of
samples in order to understand the presence or absence of characteristics that may be pertinent to their research problems. Making confident conclusions in these types of studies can require a massive number of experiments, resulting in volumes of data that are difficult to manage outside of computing
The most frequently used application in many clinical labs is the Laboratory Information Management System (LIMS). LIMS, at its most extensive, keeps track of all stored data about a laboratory, from the stock on the shelves to the results of genetic tests. Many of these systems also provide functionality for defining and monitoring laboratory workflow, allowing scientists to design and distribute experimental protocols for lab technicians and automated instruments to follow. Since LIMS are often open to integration with other applications, they can become a central hub for connecting all of a laboratory’s computing infrastructure.
Applications for analyzing clinical data are an important class of technologies that may be connected to a LIMS. The analysis tools designed for the scientific market represent some of the most advanced examples of interactive applications currently available to knowledge workers. These tools can take seemingly countless pieces of laboratory data and present them in ways that allow scientists to understand trends, uncover anomalies, and make decisions. Robust visualization functionality can allow researchers to sift through experimental results from a variety of perspectives based on emergent wayfinding approaches. In clinical research areas where certain established analyses are often useful for understanding data, highly tailored functions can automate known, well characterized tests and present
their results in clear and actionable information displays.
The fictional scientist in this book’s examples conducts clinical research, largely funded by government grants, on populations with a deadly hereditary disease. She has had many years of academic training and experience and is valued for her intelligence, depth of knowledge, insights, and personal drive. She has recently become the Principle Investigator of her own research lab, with responsibility over all of its clinical programs and personnel. Her new facility has extensive computing infrastructure, and she has been able to select LIMS and analysis applications that present the best available fit for her planned research approaches. Her workplace goals include:
Make discoveries that lead to improvements in
Design innovative studies and protocols
Mentor students and staff
Ensure that lab technicians have what they need to
Analyze experimental data as thoroughly as possible
Publish leading findings in reputable journals
Manage lab resources wisely
The many specializations of financial trading are, generally speaking, about the exchange of financial instruments to maximize returns for traders, their firms, and their clients. The teams that accomplish these goals are composed of distinct roles and established hierarchical structures that help ensure strict accountability. One important distinction in financial firms’ personnel is the pervasive separation between trading and “back office” groups. While traders make decisions about actions in their markets, the back office completes the detailed work that makes deals happen, such as billing, accounting, and any reconciliation of specifics that might be needed.
The history of financial trading has strong ties to advanced
applications of communication technologies. Traders are communicative people, and ongoing relationships based on stable interchanges have traditionally been a necessity in order to secure favorable transactions in markets over time. The desire for the most current market information possible has driven successive generations of traders to rapidly adopt new technologies. For example, one of the first applications of the telegraph was the transmission of market data, and in a similar vein, many financial organizations were relatively early adopters of
communication via computer networks.
Computing automation and interactive applications have had profound impacts on professional practice in financial trading. Although contemporary traders may still be vocal participants in their markets, at the time of writing, many types of trading transactions are typically accomplished without any face to face or phone conversation. Instead of verbal interaction, communication in these specialties now commonly involves the exchange of textual information on computer screens. These networked exchanges have created opportunities for trading automation based on predefined, quantitative rules set within and executed by computing tools. In situations where this sort of automation is used extensively, actual conversations outside of one’s own firm may occur only in special cases, such as negotiations over large deals, or as an intentional means of building specific
business relationships through personal connection.
Real time market information feeds, as well as a wealth of online research functionality have created the potential for information overload and excessive cognitive burdens in
traders’ work. Successful traders, having adapted to this potentially overwhelming context, become skilled at knowing when to invest time to research a transaction and when it is more beneficial to simply execute a deal based on immediately available information. These choices of time and attention are made, in part, based on the input and visible activities of other traders. Onscreen tools for supporting collaboration are often supplemented with shouts to colleagues across the room or via a global “squawk box” intercom system.
While the use of computing is universal in modern financial organizations, individual firms have varying attitudes about providing new technologies to their workforces. Some firms conduct updates to their computing infrastructure in long, safe cycles, while others are continually attempting to improve the productivity of their staff by providing them industry leading applications.
The main drivers for adopting new technologies into trading activities have been promised increases in efficiency and volume, reductions in errors, warehousing of useful data, and freeing workers from menial actions so that they can spend more time conducting “smarter” business. Financial firms often develop their own specialized computing tools internally, and when they purchase applications from niche product vendors, they may substantially customize them during their system integration processes. Outside of domain specific products, both traders and back office workers make extensive use of typical, off the shelf productivity applications and communications
The fictional financial trader in this book’s examples works in the flagship building of a leading global financial firm. His company is known for making significant investments in computing infrastructure for its highly sought after staff. He has been in financial services for a few years, but is still at a point in his career where he wants to stay focused on day to day trading. He is motivated by monetary rewards, but he also enjoys the responsibility, risk taking, rapid decision making, and intensive, moment to moment focus of market transactions. He is a highly social person, and is known by coworkers and other traders as
a wit and conversationalist. His workplace goals include:
Work fast and smart, making decisions quickly
Exceed, or at least meet, financial targets
Maintain business relationships and have good
Be honest and fair with counterparties while
advancing organizational goals
Keep current on relevant market news and trends
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