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    Knowledge Automation: How to Implement Decision Management in Business Processes
    Knowledge Automation: How to Implement Decision Management in Business Processes
    Knowledge Automation: How to Implement Decision Management in Business Processes
    Ebook290 pages3 hoursWiley Corporate F&A

    Knowledge Automation: How to Implement Decision Management in Business Processes

    By Alan N. Fish and James Taylor

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    A proven decision management methodology for increased profits and lowered risks

    Knowledge Automation: How to Implement Decision Management in Business Processes describes a simple but comprehensive methodology for decision management projects, which use business rules and predictive analytics to optimize and automate small, high-volume business decisions. It includes Decision Requirements Analysis (DRA), a new method for taking the crucial first step in any IT project to implement decision management: defining a set of business decisions and identifying all the information—business knowledge and data—required to make those decisions.

    • Describes all the stages in automating business processes, from business process modeling down to the implementation of decision services
    • Addresses how to use business rules and predictive analytics to optimize and automate small, high-volume business decisions
    • Proposes a simple "top-down" method for defining decision requirements and representing them in a single diagram
    • Shows how clear requirements can allow decision management projects to be run with reduced risk and increased profit

    Nontechnical and accessible, Knowledge Automation reveals how DRA is destined to become a standard technique in the business analysis and project management toolbox.

    LanguageEnglish
    PublisherWiley
    Release dateFeb 8, 2012
    ISBN9781118236796
    Knowledge Automation: How to Implement Decision Management in Business Processes

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      Book preview

      Knowledge Automation - Alan N. Fish

      CHAPTER ONE

      The Value of Knowledge

      WHAT COMES INTO YOUR HEAD when you hear the word knowledge? Dusty books in the libraries of ancient universities, perhaps, or men in white coats with big foreheads? My aim in this first chapter is to change the way you think about knowledge. It is not an abstract concept divorced from the world of business; it is a tangible corporate asset. You can manufacture it, own it, buy and sell it, build it into machines that make profits for you: It is real stuff that has value. This is a crucial concept in understanding the potential benefits of decision management.

      I'm going to start right at the top with a brief discussion of modern macroeconomic theories of knowledge before looking more practically at how organizations can measure and exploit the value of their own business knowledge.

      THE ECONOMICS OF KNOWLEDGE

      The past 50 years have seen a revolution in our understanding of the drivers of economic growth. Classical economic theory addressed labor, land, and capital. In much the same way, modern economic theory now addresses human knowledge, and its contribution to the growth of economies.

      Neoclassical Growth Theory

      Theories of growth proposed in the 1950s, such as those of Robert Solow¹ and Trevor Swan² were based squarely on physical capital: objects such as machinery and stock. These were called neoclassical growth models. According to neoclassical theory, physical capital is assumed to be subject to the law of diminishing returns: the principle that if you keep increasing any single factor of production, the output you achieve from each unit of input will eventually decrease. This assumption is mathematically convenient, because it results in a model economy that always converges to a unique steady state. In other words, under any constant conditions, growth will slow down and eventually stop.

      Figure 1.1 shows the attitude prevailing at the time. Bill Phillips built this hydraulic computer—MONIAC—in 1949, using water flowing around pipes and tanks to simulate the U.K. economy. This was a sophisticated and accurate model, but the message is very clear: the economy finds its own level.

      Neoclassical models could not explain long-term growth, and were not intended to. In this approach, long-term growth is accounted for by external influences. Robert Solow introduced technical knowledge as such an exogenous variable.³ He assumed that technology was improving steadily without any influence from the economy being modeled; it simply happened, providing the external stimulus that kept the economy growing. Such a model allows you to measure the effects of technological progress, or to decide, for example, the optimal savings rate given a certain rate of progress. Because of this, Solow's model became widely used in economic analysis and earned him the 1987 Nobel Prize for Economics. What his model cannot do, however, is help you to determine economic policy to achieve technological progress.

      New Growth Theory

      The thrust of more recent theories of growth has therefore been to endogenize knowledge: to bring it within the terms of the model as an internal variable and use it to explain the observed growth of economies. Robert Lucas explains why such models are so important:

      Is there some action a government of India could take that would lead the Indian economy to grow like Indonesia's or Egypt's? If so, what, exactly? If not, what is it about the nature of India that makes it so? The consequences for human welfare involved in questions like these are simply staggering: Once one starts to think about them, it is hard to think about anything else.

      FIGURE 1.1 Bill Phillips with MONIAC

      This is what we need a theory of economic development for: to provide some kind of framework for organizing facts like these, for judging which represent opportunities and which necessities.⁴

      According to Charles Jones and Paul Romer, to account for the most important facts, a growth model must consider the interaction between ideas, institutions, population, and human capital. Two of the major facts of growth—the extraordinary rise in the extent of the market associated with globalization and the acceleration [in growth rates] over the very long run—are readily understood as reflecting the defining characteristic of ideas, their non-rivalry.⁵

      The concept of rivalry is central to understanding the importance of human knowledge in economic growth. Most physical goods are rival; only one person can make use of them at a given time. People have to compete for ownership of land, money, or physical goods. Ideas, however, are non-rival; people do not have to compete for them. A single idea may be used simultaneously by many people without being depleted in any way; in fact, the opposite applies: the value of the idea is proportional to the number of people using it.

      Because of this, non-rival goods are not subject to the law of diminishing returns, as physical goods are; they are actually subject to increasing returns. For example, a programming language is a non-rival good. One programmer using the language does not prevent another from using it at the same time. On the contrary, the value to the programmer of using a particular language is increased if many other people use the same language, and the costs of using the language are decreased. It is these scale effects of non-rivalry that cause growth to accelerate, rather than find its own level. As Romer explains in his Stanford University biography:

      I wondered why growth rates had been increasing over time. . . . Existing theory suggested that scarcity combined with population growth should be making things worse, but they kept getting better at ever faster rates. New ideas, in the form of new technologies, had to be the answer. Everyone knew that. But why do new technologies keep arriving at faster rates? One key insight is that because ideas are nonrival or sharable, interacting with more people turns out to make us all better off. In this sense, ideas are the exact opposite of scarce objects.⁶

      The other important characteristic of goods in economic theory is whether they are excludable: whether it is possible to prevent people having access to them unless they have paid. The two dimensions of rivalry and excludability create four possible types of goods, as shown in Table 1.1.

      TABLE 1.1 Types of Goods

      Much of human knowledge is in the public ___domain and can be considered a public good. This presents problems for free market economies, because if goods are to be freely available (non-excludable) and shared without competition (non-rival), it is hard to capture enough revenue to provide them. Fundamental research is therefore often supported by government funding. But knowledge does not always have to be provided as a public good. Two mechanisms exist to allow those who generate knowledge to acquire revenue from it: intellectual property rights (patents and copyright), and secrecy (trade secrets and confidentiality agreements). An example we will encounter in the following chapters is the credit risk score, which is knowledge about a customer sold as a club good, like satellite television.

      By characterizing ideas and knowledge as non-rival, partially excludable goods, and describing them with endogenous variables in their economic models, Paul Romer⁷ and Robert Lucas⁸ have developed a New Growth Theory based on human capital rather than physical capital. In this theory growth is largely accounted for by two factors: (1) the general increase in the quantity of ideas, and (2) the extent of communication of those ideas, including the availability of knowledge to be bought and used by businesses in their operations. In other words, it is not labor or capital but rather the flourishing of human knowledge that is responsible for the growth of economies and the resulting improvements in human welfare.

      The Knowledge Economy

      The examples used by economists often characterize knowledge as manufacturing technology; they interpret the growth of knowledge as the ability to create new physical goods more efficiently. This is undeniably important, but an increasing proportion of businesses, for example the entire financial sector, do not process objects at all; they process only information. Such industries are built on information technology, which allows knowledge to be applied directly in making better decisions with the available information. No business sector is better placed to participate in global economic growth, provided businesses exploit the opportunities presented by the new knowledge-based decision-making technology. As Tom Debevoise has observed:

      Business Processes built entirely on the backs of obsolete models will not create future economic growth and profits. . . . More subtly, business process built without the benefit of knowledge-based decisions cannot keep their business profitable. Moreover, most processes are unstable and have been constructed with multiple outdated assumptions. This is little comfort for the many firms that seek to preserve the knowledge of the retiring baby boomer. Knowledge and the creation of new knowledge in all its domains and forms are the key critical success factors in all modern firms. Knowledge needs to be identified, defined, and incorporated into the decisions that create and maintain agile enterprise structures. In turn, these knowledge-driven business processes produce timely products, services, and profits.⁹

      So we already have a functioning knowledge economy in which knowledge may be bought and sold, information technology enables knowledge-based decisions in knowledge-driven business processes, and global economic growth is ultimately fueled by the accumulation and distribution of knowledge. But in one respect, considered as a saleable product, most knowledge is still as primitive as a flint arrowhead: It has to be created using labor-intensive human processes. Universities and research centers are not factories of knowledge; they are workshops, and the rate of production of knowledge is limited by the productivity of the individual knowledge craftsmen.

      But that is beginning to change, with the emergence of machine learning: a group of technologies including text mining, rule induction, and predictive analytics (to be discussed in Chapter 3). Machine learning is essentially a process for creating knowledge automatically from data. Until quite recently, this technology was highly specialized and used only by expert statisticians, but now it is available to the ordinary business user. Businesses now generate their own knowledge automatically and apply it in making automated business decisions. For example, a credit card company analyzes the behavior of its existing customers to produce predictive models that are then used in making automatic decisions on credit limits and interest rates for new applicants. Such models are a form of knowledge that is not off the shelf, but specific to particular markets, products, and operating methods. The competitive advantage this brings to the company is protected by keeping the knowledge secret.

      Turning back to Romer for a final word on the economics and a little stargazing:

      Perhaps the most important ideas of all are meta-ideas. These are ideas about how to support the production and transmission of other ideas. The British invented patents and copyrights in the seventeenth century. North Americans invented the modern research university and the agricultural extension service in the nineteenth century, and peer-reviewed competitive grants for basic research in the twentieth century. . . . We do not know what the next major idea about how to support ideas will be. Nor do we know where it will emerge. There are, however, two safe predictions. First, the country that takes the lead in the twenty-first century will be the one that implements an innovation that more effectively supports the production of new ideas in the private sector. Second, new meta-ideas of this kind will be

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