Technology and Policy in Decision Support Systems

Sumber : www.noumenal.com

by Marc Demarest,
CEO of Noumenal, Inc.

Abstract
Data warehousing, data marting, online analytical processing (OLAP) – these terms dominate discussions of enterprise decision support systems (DSS). At the same time, it is becoming increasingly clear that none of these product categories has a particularly stellar track record for improving the quality or quantity of commercial decisions. This is so because none of the technological answers to the commercial decision-making problem focuses on decision-making processes themselves -- on the entirety of the decision-making problem – which are as much matters of policy as they are of technology. This article discusses the evolution of the data warehousing marketplace from data infrastructure to enterprise decision support systems, focusing on the decision-making process as the design center for effective DSS environments.

Introduction: Plumbers or Decision Process Designers?
Examining the terms a community uses to describe its work can tell us a great deal about what that community believes about its mission, focus and objectives.

Consider the “data warehousing market”. In 1995, a survey of key terms used in well over 75,000 articles from approximately 150 IT trade press publications in Computer Select revealed the following statistics for that database. Over a 12 month period, data warehouse averaged 21 articles per month, data warehousing, 20 articles per month, decision support, 66 articles per month, DSS, 12 articles per month, and OLAP, 7 articles per month. A similar analysis, run on the same database in December 1997 for the prior 12 months, revealed different statistics. In that search, the term data warehouse averaged 72 articles per month, a 246% increase, data warehousing, 89 articles per month, a 336% increase, decision support, 64 articles per month, a 3% decrease, DSS, 20 articles per month, a 59% increase and OLAP, 31 articles per month, a 339% increase.

In the second analysis, we see that technology terms -- data warehousing and OLAP -- have increased greatly in terms of their use in our collective discussions, but that decision support and DSS were used at about the same frequency. This trend has continued and new terms like business intelligence are now also common.

From this, we can draw a simple conclusion that is borne out by nearly every firm’s experience in the marketplace: the “data warehousing” marketplace is concerned largely with plumbing – with technology associated with data movement and storage – rather than business value: with the building of decision support systems that materially effect the quality and quantity of commercial decisions. In other words, from a bright beginning in the early 1990s, when “DSS” meant the promise of real returned business value from open systems technology, we have retreated to a largely technical, largely insular state in which the objective of DSS is not decision support, but dumping data on the Windows desktop in hopes that the person using that desktop knows what to do with that data.

It’s easy to understand why the DSS marketplace has retreated to a position where plumbing is the order of the day. Decision-making processes, like any other kind of business process, are difficult to model, difficult to support, somewhat variable and often deeply submerged in an organization’s soft, anecdotal “standard operating procedure.” By contrast, technical infrastructure, data management, data transformation and transport are crisp technical topics that have – or purport to have – right answers, clean lines, and definitive shapes. Clearly, faced with a choice between dealing with the prickly realities of decision-making processes and the cool, clean lines of a technical architecture, one would be mad to choose the more intractable problem area.

Unfortunately, in order to make good on the promise of decision support systems – in order to actually deliver material returned business value to the organization – DSS designers and implementers have no such choice. Every day, as numerous studies have shown, customer and shareholder value are created – or destroyed – at each point in the organization where a decision is made. Not at the point where data is dumped into an n-dimensional pivot table on a Windows desktop.

In fact, if we examine decision theory with an eye to returned business value, we’ll see that the data dumping that is the objective of so many in-house DSS projects is in fact the beginning of the decision-making process, and that the majority of the decision-making process takes place today in an environment that DSS technology simply ignores.

Modeling Decision-Making
Decision support systems (DSS) disciplines deal with the use of information technology to support human decision-making processes. Michael Scott-Morton, who virtually invented the discipline in the early 1970s, offered this definition of DSS:

Decision support systems couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions. It is a computer-based support system for management decision-makers who deal with semi-structured problems.[1]

Even this founding definition – like all definitions – presents us with problems: who is the decision-maker? What kinds of data serve as inputs to the decision-making process? What does the decision-making process itself look like? What kinds of risks and constraints are associated with the decision-making process? How is the output of the decision-making process – a decision – evaluated, implemented and tracked?

Ultimately, no matter where we ground ourselves in a discussion of decision support systems, we have to develop and employ a model of decision-making: the set of activities that DSS environments support. The key elements of this model are fairly common, and include:


a decision-maker: an individual or group charged with making a particular decision
a set of inputs to the decision-making process: data, numerical or qualitative models for interpreting that data, historical experience with similar data sets or similar decision-making situations, and various kinds of cultural and psychological norms and constraints associated with decision-making
the decision-making process itself: a set of steps, more or less well-understood, for transforming the inputs into outputs in the form of decisions,
a set of outputs from the decision-making process, including the decisions themselves and (ideally) a set of criteria for evaluating decisions produced by the process against the set of needs, problems or objectives that occasioned the decision-making activity in the first place.


As soon as we look at this model, which is deceptively simple in itself, we realize that talking about decision support systems outside of a particular domain of decision-making is not particularly useful. If we considered only the timeframe in which a given decision has to be made and the risks and constraints associated with the decision-making process, we would recognize that there is a great deal of qualitative and quantitative difference between governmental agencies, not-for-profit (NFP) organizations, and commercial firms. Put simply, commercial decisions, in the aggregate, have the shorter timeframes and higher associated risks (including extinction) than either public sector or not-for-profit decisions, and as such would presumably require the most assistance from information technology.

For this reason alone, this essay limits its scope to commercial decision support systems: IT infrastructure designed to support the decision-making processes in publicly-held and private firms that compete in open markets for customers, revenue and market share.

Commercial Decision-Making
Who makes commercial decisions?
There is a deeply-seated and pervasive notion among DSS practitioners that decision-making in commercial settings is an individual activity: that isolated managers or analysts, often using personal computers, make business decisions. This notion confuses responsibility or accountability with process and activity; although it is quite common for a single, named individual to be responsible or accountable for the effects of a decision, almost all commercial decisions worth talking about are made by groups of people.

John Kenneth Galbraith, in The New Industrial State, describes this group-based decision-making process this way:

The modern business organization, or that part which has to do with guidance and direction, consists of numerous individuals who are engaged, at any given time, in obtaining, digesting or exchanging and testing information. A very large part of the exchange and testing of information is by word of mouth – a discussion in an office, at lunch or over the telephone. But the most common procedure is through the committee and the committee meeting. One can do worse than think of a business organization as a hierarchy of committees….Decision in the modern business enterprise is the product not of individuals but of groups. The groups are numerous, as often informal as formal, and subject to constant change in composition.[2]

Similarly, Peter Drucker notes that:

Most businessmen still believe that decisions are made by top management. Indeed, practically all textbooks lay down the dictum that “basic policy decisions” are “the prerogative of top management.” As most, top management “delegates” certain decisions. But this reflects yesterday’s rather than today’s reality, let alone that of tomorrow. It is perfectly true that top management must have the final say, the final responsibility. But the business enterprise of today is no longer an organization in which there are a handful of “bosses” at the top who make all the decisions while the “workers” carry out orders. It is primarily an organization of professionals...exercising autonomous, responsible judgment. And every one of them -- whether manager or individual expert contributor -- constantly makes truly entrepreneurial decisions, that is, decisions which affect the economic characteristics and risks of the entire enterprise.[3]

Once said, this fundamental characteristic of commercial decision-making is obvious. No one who works in a commercial environment can point to many significant decisions she has made as an individual, and everyone who works for or with commercial firms can supply ample evidence of the communal nature of commercial decision-making. So why do we persist in behaving, as IT and business professionals, as if individuals make commercial decisions? There are at least two technical reasons. The first is historical; contemporary decision support systems derive many of their fundamental models from an earlier technological genre, executive information systems (EIS) that adopted the decision-making model Galbraith and Drucker argue against, and focused on providing data in highly graphical forms to individual senior managers and executives. The second is contemporary; most DSS environments today end, from a technological perspective, at the personal computer, after data is dumped onto “the desktop” of individual knowledge workers. All work that occurs after that data dumping operation is outside the bounds of conventional DSS technologies, and so, from a technical perspective, doesn’t exist.

What is the objective of commercial decision-making?
Ultimately, all commercial decisions are supposed to produce, directly or indirectly, positive changes to the firm’s balance sheet or income statement. That means, generically, that all interesting commercial decisions are in one way or another monetary decisions, and all have, in some combination, three fundamental business objectives:


revenue enhancement: the production of more and/or better revenue for the firm, whether through new product or service introduction, product or service line refinement or retirement, or customer care.
cost control, containment or reduction: the limitation or control of expenditures related directly or indirectly to the firm’s market activities and infrastructure.
risk management, including both the risk to the firm’s capital and market (external, competitive) risk.

In short, one of the conclusions we can draw about all decision-making in commercial settings is that the decisions in question are financially-informed: financial data serves as an input to the decision-making process, a set of constraints on the decision-making process itself, and/or the target output-for-change of the decision-making process. Ultimately, commercial decision-making is about “the numbers”.

Of course, not all particular decisions can directly affect any of these top-level objectives. In fact, one of the most complicated aspects of commercial decision-making is that many things can interfere between the intent of a decision-maker (to control cost, raise revenue, mitigate risk) and impact the decision’s effects, particularly:


Multiple stakeholders: in commercial environments, multiple constituencies have stakes in decisions, and have different agendas, desired outcomes, and criteria for evaluation and judgement
Far-reaching consequences: unlike decisions studied in game theory and decision science, commercial decisions have far-reaching, non-local effects, some (or many) of which cannot be known in advance. Who would have forecast, for example, that Apple’s decision not to license its operating system technology would have led Oracle to make a bid for Apple (because Microsoft established OS dominance in the vacuum Apple left, Ellison rose to power, and Ellison dislikes Gates and Microsoft)?
Dynamic decision-making environments: inputs to the decision-making process, and the structure of the decision itself, can change several (or many) times during the decision-making process.
Uncertainties: some of the critical input factors are either unknown, incompletely known or unreliable, requiring “finesse” or “insight” or “intuition”
Multiple objectives: the constraints on any given commercial decision-making environment are (a) very tight , (b) often conflicting and (c) normative (we ‘know’ what the answer is supposed to be before we engage in the decision-making process).

In other words, decision-making in commercial environments is a nested phenomenon. A firm’s top-level decision to enter or exit a market, for example, is typically broken down into a host of more granular decisions that can be characterized more easily, and the effectiveness of the resulting market entrance or exit strategy is almost completely dependent upon:


the quality and effectiveness of each decision made as a part of the larger decisional frame
the implementation of those decisions completely, properly and in the correct sequencing
the stability of the overall external market environment during the process of making and implementing the set of relevant decisions
the way in which other factors influence, and interfere with, decisions and decision-makers.

What does the commercial decision-making process look like?
Once we accept that commercial decisions are made by groups of people within commercial firms, and not be individuals, we immediately feel the need for a model of the typical commercial decision-making process. Who does what when? Using what tools, methods and practices?

Decision theorists have, over the last decade, developed a fairly useful generic model of decision-making that makes sense, as a normative model, for commercial decision-making, Any decision, these theorists argue, can be understood as a six-phase process:


decision preparation, which is initiated in response to the desire for a change in the state of the business, and during which the individual or collective decision-maker gathers the inputs required to structure, evaluate and make a decision
decision structuring, during which the decision is framed, modeled and examined for implications, dependencies and links to