Information Technology Challenges

There are a number of substantial challenges associated with use of information technology in terms of enhancing the productive efforts of an individual, a group, and an organization. Many of these are challenges that affect managing both people and knowledge.

The command- and-control model of leadership is a poor fit for managing organizations where the participants are not bound to the organization by traditional incentive and reward systems. A collaborative effort has to continue to make sense and to provide value to participants for it to be sustained. Otherwise, knowledge and skills are quite portable and the loss of knowledgeable workers is a major potential downside risk for organizations today.

There are three keys to organizations prospering in this type of environment: speed, flexibility, and discretion. Speed means understanding a situation (e.g., a market opportunity), formulating a plan for pursuing this opportunity (e.g., a joint venture for a new product), and executing this plan (e.g., product available in stores) quickly, in this case all within a few weeks at most.

The need for flexibility is obvious; otherwise, there would not be any advantage to speed. However, flexibility is also crucial for reconfiguring and redesigning organizations, and consequently reallocating resources. Functional walls must be quite portable, if they exist at all.

Discretion is what transforms flexibility into speed. Distributed organizations must be free to act. While they may have to conform to established protocols, or play by the rules of the game—at least the rules of the moment—they need to be able to avoid having to wait unnecessarily for permission to proceed.

Similarly, they need to be able to alter courses of action, or pull the plug, when things are not working. In this way, resources are deployed quickly and results are monitored just as quickly. Resource investments that are not paying off in the expected time frame can be quickly redeployed elsewhere.

A major determinant of these organizational abilities is the extent to which an organization possesses intellectual capital, or knowledge capital, such that it can create and use innovative ideas to produce productive results (28-30) and the ability to manage in a time of great change.

We would add communications, collaboration, and courage to the formulation of Ulrick representing intellectual capital to yield:
Intellectual capital = Competence x Commitment x Communications x Collaboration x Courage

What matters very much today is the ability to make sense of market and technology trends, to quickly decide how to take advantage of these trends, and to act faster than competitors, or other players. Sustaining competitive advantage requires redefining market-driven value propositions and quickly leading in providing value in appropriate new ways.

Accomplishing this in an increasingly information-rich environment is a major challenge, both for organizations experiencing these environments and for those who devise and provide systems engineering and management methods and tools for supporting these new ways of doing business.

Thus we see that the network age of information and knowledge is an integrated age in which the mutually reinforcing influences of information technology, organizations, and people have led to major challenges and major opportunities in which the new integrated system takes on global proportions.

A key issue now is how systems engineering and management can support business, government, and academia to address these major issues associated with productive use of information technology innovations in a successful manner.

The remainder of this article discusses ten challenges, discussed in more depth in Ref., that this integrative discipline must pursue and resolve if we are to support continued progress through information technology. Addressing these challenges will require much continued effort.

1. Systems Modeling. Our methods and tools for modeling, optimization, and control depend heavily on exploiting problem structure. However, for loosely structured systems, behavior does not emerge from fixed structures. Instead, structure emerges from collective behaviors of agents.

The distributed, collaborative, and virtual organizations that information technology enables are such that the system elements are quite fluid. Distinctions between what is inside and outside the system depend on time-varying behaviors and consequences. Satisfying this need will significantly challenge typical modeling methods and tools.

2. Emergent and Complex Phenomena. Meeting this modeling challenge is complicated by the fact that not all critical phenomena cannot be fully understood, or even anticipated, based on analysis of the decomposed elements of the overall system. Complexity not only arises from there being many elements of the system, but also from the possibility of collective behaviors that even the participants in the system could not have anticipated.

An excellent example is the process of technological innovation. Despite the focused intentions and immense efforts of the plethora of inventors and investors attracted by new technologies, the ultimate market success of these technologies almost always is other than what these people expect and new technologies often cause great firms to fail.

In other words, many critical phenomena can only be studied once they emerge and the only way to identify such phenomena is to let them happen, or to create ways to recognize the emergence of unanticipated phenomena through modeling and simulation. An important emergent phenomena is that of path dependence.

The essence of this phenomenon begins with a supposedly minor advantage or inconsequential head start in the marketplace for some technology, product, or standard. This minor advantage can have important and irreversible influences on the ultimate market allocation of resources even if market participants make voluntary decisions and attempt to maximize their individual benefits.

One of the potential characteristics of information technology products, especially software, is that of increasing returns to scale. Also, there may be a network effect, or ‘‘network externality,’’ which occurs because the value of a product for an individual consumer may increase with increased adoption of that product by other consumers and this, in turn, raises the potential value for additional users.

An example is the telephone, which is only useful if at least one other person has one, and for which the utility of the product becomes increasingly higher as the number of potential users increases. These are major issues concerning information technology today and are at the heart of the concern in the late 1990s with respect to the Windows operating system and Internet Explorer, each from Microsoft.

3. Uncertainties and Control. Information technology enables systems in which the interactions of many loosely structured elements can produce unpredictable and uncertain responses that may be difficult to control. The challenge is to understand such systems at a higher level and success in doing this may depend much more on efficient experimentation and simulation than on mathematical optimization due to the inherent complexities that are involved.

4. Access and Utilization of Information and KnowledgeInformation access and utilization, as well as management of the knowledge resulting from this process, are complicated in a world with high levels of connectivity and a wealth of data, information, and knowledge. Ubiquitous networks and data warehouses are touted as the information technology based means to taking advantage of this situation.

However, providers of such ‘‘IT solutions’’ seldom address the basic issue of what information users really need, how this information should be processed and presented, and how it should be subsumed into knowledge that reflects context and experiential awareness of related issues.

The result is an effort to obtain massive amounts of data and information, and associated large investments in information technology with negligible improvements of productivity. One of the major needs in this regard is for organizations to develop the capacity to become learning organizations and to support bilateral transformations between tacit and explicit knowledge. Addressing these dilemmas should begin with the recognition that information is only a means to gaining knowledge and that information must be associated with the contingency task structure to become knowledge.

This knowledge is the source of desired advantages in the marketplace or versus adversaries. Thus understanding and supporting the transformations from information to knowledge to advantage are central challenges to enhancing information access and utilization in organizations. This requires the contingency task structure of experiential familiarity with previous situations and understanding the environmental context for the present situation.

5. Information and Knowledge RequirementsBeyond adopting a knowledge management perspective, one must deal with the tremendous challenge of specifying information requirements in a highly information-rich environment. Users can have access to virtually any information they want through modern information technology, regardless of whether they know what to do with it or how to utilize the information as knowledge.

While information technology has evolved quite rapidly in recent decades, human information processing abilities have not evolved significantly. These limited abilities become bottlenecks as users attempt to digest the wealth of information they have requested. The result is sluggish and hesitant decision making, in a sense due to being overinformed.

6. Information and Knowledge Support SystemsInformation technology based support systems, including decision support and expert systems, can potentially provide the means to help users cope with information-rich environments. However, these support systems are difficult to define, develop, and deploy in today’s highly networked and loosely structured organizations due to inherently less well- defined contexts and associated tasks and decisions.

Effective definition, development, deployment, and use of these systems are also complicated by continually evolving information sources and organizational needs to respond to new opportunities and threats. In part, these difficulties can be overcome by adopting a human-centered approach to information and knowledge support system design.

This approach begins with understanding the goals, needs, and preferences of system users and other stakeholders—for example, system maintainers. This approach focuses on stakeholders’ abilities, limitations, and preferences, and attempts to synthesize solutions that enhance abilities, overcome limitations, and foster acceptance. From this perspective, information technology and alternative sources of information and knowledge are enablers rather than ends in themselves.

7. Inductive Reasoning. Prior to the development of agent-based simulation models and complexity theory, most studies involved use of linear models and assumed time-invariant processes (i.e., ergodicity). Most studies also assumed that humans use deductive reasoning and techno-economic rationality to reach conclusions.

But information imperfections and limits on available time often suggest that rationality must be bounded. Other forms of rationality and inductive reasoning are necessary. We interpret knowledge in terms of context and experience by sensing situations and recognizing patterns. We recognize features similar to previously recognized situations. We simplify the problem by using these to construct internal models, hypotheses, or schemata to use on a temporary basis.

We attempt simplified deductions based on these hypotheses and act accordingly. Feedback of results from these interactions enables us to learn more about the environment and the nature of the task at hand. We revise our hypotheses, reinforcing appropriate ones and discarding poor ones. This use of simplified models is a central part of inductive behavior.

8. Learning Organizations. Realizing the full value of the information obtained from an IT system, and the ability to interpret this as knowledge is strongly related to an organization’s abilities to learn and its ability to become a learning organization. Learning involves the use of observations of the relationships between activities and outcomes, often obtained in an experiential manner, to improve behavior through the incorporation of appropriate changes in processes and products.

Two types of organizational learning are defined by Argyris and Schon. Single-loop learning is learning which does not question the fundamental objectives or actions of an organization. It enables the use of present policies to achieve present objectives. The organization may well improve but this will be with respect to the current way of doing things. Organizational purpose, and perhaps even process, are seldom questioned. Often, they need to be.

Double-loop learning involves identification of potential changes in organizational goals and approaches to inquiry that allow confrontation with and resolution of conflicts, rather than continued pursuit of incompatible objectives which usually leads to increased conflict. Double-loop learning is the result of organizational inquiry which resolves incompatible organizational objectives through the setting of new priorities and objectives.

New understanding is developed which results in updated cognitive maps and scripts of organizational behavior. Studies show that poorly performing organizations learn primarily on the basis of single-loop learning and rarely engage in double-loop learning in which the underlying organizational purposes and objectives are questioned.

Peter Senge has discussed extensively the nature of learning organizations. He describes learning organizations as “organizations where people continually expand their capacity to create the results they truly desire, where new and expansive patterns of thinking are nurtured, where collective aspiration is set free, and where people are continually learning how to learn together.’’

Five component technologies, or disciplines, enable this type of learning: (1) systems thinking; (2) personal mastery through proficiency and commitment to lifelong learning; (3) shared mental models of the organization markets, and competitors; (4) shared vision for the future of the organization; and (5) team learning.

Systems thinking is denoted as the ‘‘fifth discipline.’’ It is the catalyst and cornerstone of the learning organization that enables success through the other four dimensions. Lack of organizational capacity in any of these disciplines is called a learning disability.

It is important to emphasize that the extended discussion of learning organizations in this section is central to understanding how to create organizations that can gain full benefits of information technology and knowledge management. This is crucial if we are to transform data to information to insights to meaningful and effective programs of action.

9. Planning. Dealing successfully with the above challenges requires that approaches to planning and its effect on the other systems life cycles be reconsidered. Traditionally, planning is an activity that occurs before engineering production and before systems are placed into operation. However, for loosely structured systems, systems planning must be transformed to something done in an interactive and integrative manner that considers each of the other life-cycles.

10. Measurement and Evaluation. Successfully addressing and resolving the many issues associated with the information technology challenges described in this article requires that a variety of measurement challenges be understood and resolved. Systems associated with access and utilization of information, and knowledge management, present particular measurement difficulties because the ways in which information and knowledge affect behaviors are often rather indirect.

For this and a variety of related reasons, it can be quite difficult to evaluate the impact of information technology and knowledge management. Numerous studies have failed to identify measurable productivity improvements as the result of investments in these technologies. The difficulty is that the impact of information and knowledge is not usually directly related to numbers of products sold, manufactured, or shipped.

Successful measurement requires understanding the often extended causal chain from information to knowledge to actions and results. Transformations from information to knowledge also present measurement problems. Information about the physical properties of a phenomena are usually constant across applications. In contrast, knowledge about the context-specific implications of these properties depends on human intentions relative to these implications.

Consequently, the ways in which information is best transformed to knowledge depends on the intentions of the humans involved. The overall measurement problem involves inferring—or otherwise determining—the intentions of users of information technology based systems, both products and processes.