SGML, HyTime, and Organic
Information Management Models

Derivatives: A parable for information technologists.

The financial derivative market is getting a lot of attention. Kodak, Orange County, Procter & Gamble, Metallgesellschaft, and other high-profile organizations have suffered major losses or become bankrupt as a result of speculating on derivatives. As the Harvard Business Review points out:

...if not managed correctly, [derivatives] create bigger problems than the ones that they purport to solve.

[Tufano, page 35]

The problem results, in large part, from complexity. Derivatives have evolved so quickly and become so complex that few can explain or claim to understand exactly how many of these investments will behave in the market. Understanding, and thus much of the decision making, has shifted to a small group of technicians. As a result many senior managers have effectively abdicated their policy-making roles.

After first singing the praises of the new breed of financial analysts dubbed Rocket Scientists, organizations that choose to trade in derivatives are starting to realize that

Financial decisions that were previously designed and implemented by specialists need to be monitored more closely from the very top of organizations.

[Using Derivatives, page 33]

Risk Management: The missing link in information technology initiatives

A similar situation exists in the information technology arena. There too, senior managers have largely turned decision making over to technologists. By selecting and implementing technologies in a relative vacuum, these technologists also implicitly define corporate policies. This is unfortunate, because the stakes of bad information technology investments are just as high, if not higher, than bad financial investments. By some estimates, information and decision making are more valuable than capital, which is becoming just another commodity in the international markets.

Unlike their financial counterparts, however, the impact of poor information management policies and investments isn't as observable or as measurable. The harm done by inappropriate solutions can remain unrealized for years or decades, hampering organizational performance and competitiveness long after the important actors are gone. CIO turnover, outsourcing, lost opportunities, and downsizing are just a few of the results of poor information investment strategies. In addition, an increasing number of organizations and analysts are failing to find any relationship between IT investments and organizational success. Cost savings and improved productivity can no longer be assumed, as these promises routinely fail to be realized.

The solution will not be found in volumes of information technology plans and strategy documents.

Computer staff can safely deliver superior results only if they are executing an unambiguous information policy. Fuzzy objectives and ill-defined means lead to confused execution. Under such conditions, it may be a matter of luck by blundering into success if what is delivered matches what management alleges that they always wanted.

[Strassman, page 6]

The world is beginning to realize that technology alone does not guarantee success. It is within this context that SGML investments are being considered.

SGML and HyTime: The derivatives of the information technology market?

If information management is generally ignored at the policy level, what does it mean to information standards that derive much of their benefits from the ability to support long-range policy objectives? If the risks associated with traditional information technologies are beyond the management abilities of most organizations, where does that put SGML and HyTime?

For most organizations, the transition from proprietary, page-based document production and management architectures to SGML and HyTime (hereafter assumed to be included within references to SGML, unless otherwise noted) will be one of the most destabilizing efforts ever attempted. The adoption of these standards means significant changes to tools, processes, responsibilities, and even the way people think about information. To its credit, most of the SGML community is sensitive to these issues, but with the appearance of SGML tools from mainstream software vendors, many more individuals and organizations will choose to go it alone.

Put yourself in the position of a manger who doesn't really understand all that computer stuff, having effectively abdicated your decision making role to a some expert: an in-house technology manager or developer, a vendor's sales rep, a consultant, or even the popular computing magazines. When confronted about a decision to use SGML, will you stop to educate yourself on the policy issues, or will you hand over virtually all learning and decision making to someone else? Will your organization make wise investments? How will you know?

Some notable examples of failed SGML initiatives have already occurred. How many more are likely to happen in the future? What will the impact be when magazines like CIO and InformationWeek publicize some failures as high-profile case studies?

Sure, similar articles have appeared about failed client-server projects—remember the Denver airport—but there is an army of hundreds, if not thousands, of database developers, account managers, and other technical personnel that exist to sing the praises of client-server database technologies and protect their jobs. The SGML industry is less mature and more vulnerable to a few splashy setbacks.

SGML is a powerful standard. Its flexibility allows organizations to do things that could not be done as easily any other way. That same flexibility also gives organizations the ability to do things that are not in their best interests. Like derivatives, SGML is a strategic technology that allows organizations to make big gains and big mistakes.

Senior managers are now beginning to learn that they need to provide oversight and define corporate policies for the use of derivatives to reduce and manage the risk associated with these investments. These same managers need to provide oversight of and policy-making for their information investments, especially investments involving SGML.

SGML technology suppliers and their clients need proven ways to lessen both real and perceived risks. As SGML moves more towards the mainstream, the industry will benefit by helping organizations to apply SGML and other information technologies more intelligently.

Organic Information Management Models and AtStake: Approaches for managing risk

This paper provides some new approaches for managing risk and improving the returns on information technology investments.

The first section describes many of the principles associated with Organic Information Management Models (OIMMs). OIMMs provide a framework for dramatically lowering the risks associated with major information technology initiatives, including those involving SGML and HyTime. In addition, SGML and HyTime are fundamentally enabling technologies that can simplify the implementation of OIMMs.

The second section describes the application of these information management concepts to AtStake, a stakeholder-focused strategic planning process. AtStake is a proven tool that reduces risk by helping to ensure that a project's strategies and tactics are politically valid. It is also an example of an OIMM, where the emphasis is shifted away from engineering complicated and comprehensive data structures to opting for simpler structures that serve as a framework for identifying and documenting important relationships.

The limitations of engineered information management models

During the first twenty years of computerization the approach to designing the flow of information came from Frederick Taylor's ideas of almost a century ago about factory efficiency. Accordingly, to overcome the chaos of error-prone paperwork, computer analysts saw their jobs not much differently than industrial engineers. They broke up paperwork steps into programmable sequences of transactions. Their objective in designing the computer system was to monitor the flow of the data. Whenever possible, the computer captured as many of the sequential processing steps as possible.

[Paul Strassman, The Politics of Information Management, page 179.]

Today, however, accelerating economic and political change is forcing many organizations to adapt and change faster than their information infrastructures allow them to do. Many of these organizations have begun to realize that software isn't very soft. The system won't let me do that is an increasingly common complaint. Strategic business initiatives are often delayed or hampered by trying to adapt or modify legacy information management systems that weren't designed for such changes.

David M. Upton provides some examples of this phenomenon in his Harvard Business Review article: What Really Makes Factories Flexible?. Upton studied the relationship between computer-integrated manufacturing (CIM) and operational flexibility within the fine paper industry. His conclusions may be surprising to some.

...there was little direct correlation between the degree of computer integration and the degree of operational flexibility.

[Upton, page 75]

Even worse, Upton found that in most cases, CIM actually reduced flexibility. The CIM systems operated the plants more conservatively than human operators in order to reduce paper breakage and associated downtime. This not only slowed changeovers but actually produced more paper breaks than the computer's human counterparts. In addition, operator skills atrophied from lack of use after CIM systems were installed. The biggest detriment to flexibility, however, involved management behavior. By dumping the problem on technologists, the managers used CIM to dodge their responsibilities for defining flexibility objectives and managing the organization to meet those performance goals.

Organic Information Management Models and their benefits

Unlike traditional information management, which is built around the concepts of engineered systems, Organic Information Management Models are designed around biological concepts, such as learning, adaption, competition, and chaos. Organic information management concerns itself less with mechanical efficiency and more with how organizations, groups, and individuals learn to respond to environmental changes.

OIMMs are built on the assumption that the tools and techniques used today will be replaced at an ever accelerating pace, evolving and adapting through time. They abandon the idea that analysts can identify and formalize every possible relationship between pieces of information. Instead, they allocate decision-making in such a way as to assure maximum flexibility to individuals and workgroups, within the context of enterprise and institutional requirements for information to be accessible and usable throughout its life cycle.

OIMMs reflect the way that people actually produce, deliver, and use information to effect change. They allow individual workgroups to work with their stakeholders to structure their information holdings based on process-specific requirements and provide a framework for individuals and groups to describe and document relationships as they are uncovered (or learned).

In reality, an effective computer system is a balance between engineered and organic elements. Numerous information management components will need to be designed and engineered very carefully for this approach to work, but OIMMs are characterized by intentionally limiting such engineering to the component and subsystem levels. This leaves much of the final integration to be done by humans in the form of defining relationships between components or executing some of the behaviors that would be automated in more fully engineered solutions. Such an approach is more expensive than fully automated solutions when comparing individual transactions, but is imperative to maintain flexibility and resiliency.

Guidelines for Organic Information Management Models

In Out of Control: the rise of neo-biological civilization, Kevin Kelly describes the impact of organic approaches on science, technology, and organizational theory. He summarizes is findings in what he calls The Nine Laws of God:

1. Distribute being
2. Control from the bottom
3. Cultivate by increasing returns
4. Grow by chunking
5. Maximize the fringes
6. Honor your errors
7. Pursue no optima; have multiple goals
8. Seek persistent disequilibrium
9. Change changes itself

[Kelly, page 648]

The following sections summarize a few of these concepts, as they apply to the development of the knowledge-based systems that are needed for organizations to adapt to and flourish in the face of organizational uncertainty. First, politics cannot be avoided, especially when dealing with information standards. When properly channeled, politics is the primary mechanism for assuring that technical solutions meet individual and organizational needs. Second, comprehensive, ideal solutions are almost impossible to realize, and are probably detrimental if they are actually achieved. Third, the relationships between information components are becoming more important than the components themselves, as they reflect the value-added human understanding and knowledge that provides competitive advantage.

SGML and HyTime and their relationships with Organic Information Management Models

SGML and HyTime developers should understand the principles of OIMMs, both to protect their own projects from political disruption, and because much of the importance of SGML and HyTime results from their ability to balance engineered and organic definitions of value.

Just as traditional information management approaches have been based on engineering concepts, the classic view of SGML has been as an interchange standard. This use of SGML promotes mechanical efficiency, minimizes data transformations, and emphasizes resource optimization. Value is measured in terms of cost, time, and fixed measures of quality. Strategic value results from improving the efficiency of existing processes and beating existing competitors.

Viewed from an engineering perspective, information life cycles tend to be more expensive than they need to be because the desire for optimization in one arena has often resulted in overall suboptimization. SGML can reduce overall information life-cycle costs by formally defining information structures that meet the needs of a wider variety of information producers and consumers, thus reducing the barriers and costs associated with sub-optimization. SGML can reduce the need for duplicate systems and costly data conversions and make it easier to retrieve, recycle, repurpose, reformat information.

The dynamics of an engineering-oriented SGML project are to pick a document type, stake out as much of the information life cycle as possible, and try to get everyone to agree on a single acceptable structure. On the systems side, information producers and consumers will use the resulting SGML DTD as an interface specification, and build or adapt information technologies to understand and produce and interact with conforming documents. Because of the magnitude of associated investments, these interchange DTDs are meant to be stable artifacts that are used as fixed points of reference for years, if not decades.

Unfortunately, such engineering efforts are susceptible to the same risks as other engineered information technology initiatives. Developers can protect themselves from risk by adopting organic approaches.

First, they can clearly differentiate policy from technical issues to help promote the active involvement of senior management. Second, they can perform political assessments to identify and map stakeholder interests. For organizations that do not distribute decision making along the lines that Strassman describes, the AtStake process can provide a framework for the required political analysis. Third, developers can be sensitive to the differences between engineered and organic definitions of value to help ensure that the solutions will meet the organization's needs for flexibility.

To support requirements for local flexibility, organic DTDs are likely to differ from interchange DTDs in a number of ways:

1. Organic DTDs are built with the expectation that they will change. Change will become necessary as the organization learns and adapts to new environments.

2. Organic DTDs may avoid the use of standardized generic identifiers. Different workgroups may need to retain their own vocabularies. The architectural forms construct allows these workgroup-specific vocabularies to be mapped back to corporate and industry standards, while retaining local flexibility.

3. Organic DTDs support a wide variety of compatible structures. Just as receptor sites accept a variety of compatible chemicals, organic DTDs contain a mixture of standard and non-standard structures. The standard structures are designed to bond with standard systems and non-standard structures are tuned to exploit localized systems. Such an approach is critical to maximize the fringes [Kelly, page 470] and promote experimentation.

4. Organic DTDs allow a wide variety of metadata models to be developed. As the quality of the metadata largely determines the usefulness of the information, information objects effectively compete for attention based on the quality of their metadata. Metadata requirements are expected to change through time, and this evolutionary process, too, reflects organizational learning. High-quality metadata models support the shift from information management to knowledge management.

5. Organic DTDs rely on self-organization. Instead of organizing information through the centralized design of complex structures, the decentralized interaction of information providers, consumers, and software systems create and maintain order. Hypermedia links are a primary mechanism for organizing and integrating large bodies of information, allowing DTDs to become smaller, cheaper and more quickly developed. Comparatively simple chunks of information are used as shells to hang HyTime links. Like metadata models, sets of links (webs) will compete for resources based on their utility.

The challenge to system designers is to provide a framework where local experimentation and variety do not become barriers to information exchange and where investment decisions can be made at a tactical level and still meet strategic requirements. SGML is both flexible enough to meet local requirements and standardized enough to preserve long-term value by assuring that information is maintained in a form that is usable outside of the specific workgroups and applications that created it.

SGML and HyTime are important precisely because they allow local, tactical solutions to be integrated within a common information management architecture.

The ability to both standardize and customize is likely to become increasingly important as workgroups and organizations are pressured to quickly adapt to changing circumstances and are driven to pursue divergent information management strategies. By supporting both engineering and organic approaches, SGML and HyTime provide a framework for developing shared solutions that balance the competing drivers and help ensure organizational health in the face of uncertainty. SGML allows organizations and workgroups to emphasize either engineering or organic approaches to realize immediate benefits but does not preclude evolving to a more balanced approach in the future.

Origins

AtStake was developed by one of the authors to address the political issues associated with the development and implementation of environmental policy. The author also had extensive experience developing political strategies within the executive and legislative branches of the federal government, and these experiences helped to shape the facilitation process.

He developed AtStake as a response to the weaknesses of traditional strategic planning and its inability to appropriately utilize external and internal stakeholder input. In addition, traditional strategic planning methods routinely neglect to identify short-term and long-term strategies, implementation responsibilities, and success criteria. Neglecting to identify success criteria at the time of planning can be fatal since strategic planning is a long-term process and new participants, not knowing the original baseline, become responsible for its continuance.

AtStake is based on a pair of simple, yet powerful conceptual foundations. First, charting a path, like solving a maze, is easier if you start at the destination. Second, values are more important than facts when making choices, especially when trying select desirable or acceptable future states.

As Strassman points out, Politics is the art of the feasible. [Strassman, page 6] AtStake uses a predictability model called VIE to translate values, interests, and expectations into a range of acceptable options which defines the conceptual space for politically viable solutions. Each individual brings with them a set of unacceptable solutions. When those are combined at a group level, the resulting range of tolerance is smaller than for any given individual.

When VIE analysis is applied to a diverse group of stakeholders in a facilitated session, the resulting range of tolerance is broader than would be expected in political models that promote polarization. AtStake provides a collaborative framework for negotiating a shared consensus of how individual values should be weighed to identify a mutually desirable future state.

After achieving investment by the stakeholders in a shared vision, AtStake begins to identify mutual drivers and constraints, which are marked as key issues for navigating the development of future strategies. This is similar to marking land mines in order to navigate around them.

AtStake's relationship to Organic Information Management Models

AtStake relates to OIMMs in four ways:

First and foremost, AtStake reduces risk. It can be used to address many of the political issues associated with the allocation of decision making, the formulation of corporate information policy, and other shared decisions.

Second, AtStake is an example of an OIMM. It uses very simple structures to focus the participant's attention on the relationships between information components. These relationships are more complex and less easily formalized than the basic data structures, but do follow well-defined patterns.

Third, AtStake is expected to serve an important role in the development of OIMM-based performance management systems. The data and strategies which are collected and developed during the application of AtStake have long-term value and should be linked to other project management and implementation modules.

Fourth, because VIE is based on human decision making models it can help to add organic awareness to a variety of applications.