Data Model Patterns: Conventions of Thought

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Rob Ferguson

Worse than that is the case where one alias applies to more than one asset type. Typically, a company will offer a core set of products with specifications to cover a wide range of characteristics. An individual customer, then, may have a more specific set of requirements, which the selling organization can satisfy by mixing its standard products in various ways or by modifying its production processes.

Introduction to Data Model Patterns: Conventions of Thought

It causes no end of troubles to have the same name refer to more than one thing. For example, a customer may want a product with a pH of between 7. In our pH example, since neither of our grades is a perfect match, the customer may pick one, or it may be necessary to use a combination of the two products to meet the customer's specification. Aside from the buyer and seller, there may be someone who is the shipper in, the recipient in, the payee for, and so forth.

The business rules controlling the ability of PARTIESto change are not documented in the model, and will vary from enterprise to enterprise. This can be shown on the model, and this relationship appears as a pig's ear in Figure 6. This is a matter of policy in any particular organization, and may ,vell vary from organization to organization.

It is not uncommon for this to be a one-to-many or a many-to-many relationship. The following sections discuss some issues relative to these topics. Marketing and sales departments are frequently organized geographically and by product groups. Indeed, the department managing a given region is often referred to as being synonymous to the region itself.

In a data model, we would rather not make this mistake: To discuss the Eastern Region is not to discuss real esta te east of the Mississippi River, but rather the group of people responsible for selling to the people who live there. The data model provides a way of precisely representing this view.

That is, the delivery site may be specified in advance.

Strictly speaking, "quantity" is a derived field, computed from the sum of all CO"'TR. Tn addition to Figure 6. The impracticality of summarizing the company's history of im'entory transactions, however, plus o':envhelming popular opinion, has bent the "no computed i1ttributes" rule a bit in this case.

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Data Model Patterns: Conventions of Thought, Hardback - David C Hay - Bok () | Bokus

Price for I I bought vial J. Ultimately, they are the reason a company is in business. The movement in American industry toward Total Quality Management is placing new demands on our understanding of contracts and their structures. As in the previous three chapters, we have seen here that it is possible to make many assertions about contracts in models that apply quite generally. We continue to add to our toolbox of patterns..

We have now established the four basic elements of any organization: the players; what they manipulate; the things they do; and their relationships with the outside world. Now, we move into more specialized arenas. First, we will address accounting-itself a model of the business-and then, we will explore several more specific areas ofinterest. Its purpose is to provide information that can be drawn upon by those responsible for decisions affecting the organization's future. It represents the of organization's activities and resources in a highly structured way, as does any model.

It is in fact better at representing certain kinds of situations than is data modeling. To try to draw a data model of the accounting model is fraught with problems. This is not only because of the problems inherent in representing the abstract accounting model in an even more abstract data model, but also because many of the dimensions of a company's accounting data are not inherent in the nature of the business; rather, they are the results of particular attitudes and objectives held by company management.

Bookkeeping, on the other hand, has more specific rules, and can therefore be modeled more easily, but bookkeeping is not accounting. That is the function of bookkeeping, and the rules under which accounting history is written permit the delegation of this task to clerks and to an increasing extent to giant electronic computers. The accountant, on the other hand, designs the system and deals with the nonroutine items, the transactions that cannot be handled without the exercise of judgment. Furthermore, as a member of the management team, his analysis and interpretation of financial statements and other accounting data contribute to an understanding of the past and the prediction of the..

The first level, which asserts only that all accounting transactions consist of debits and credits, is not robust enough to be very informative. The second level approximates the way bookkeepers and accountants see transactions, but it is cumbersome because it requires creating a separate model for nearly every type of transaction-and there are an almost unlimited number of these.

Only by moving to a third level of abstraction can accounting be represented in a single, simple model, but this is more abstraction than most people are accustomed to seeing. The problem is that even if we document bookkeeping successfully, the resulting model is not of interest to most people in the organization concerned with "accounting" as defined above. The goal of this book-to identify fundamental structures-will fail when discussing summarized data.

Unless you are about to build a general ledger system, you will probably have little direct use for the detailed bookkeeping models, but you are very likely to encounter people looking for better financial reports. Besides, dealing with the particular problems of modeling bookkeeping yields interesting insights into the modeling process itself.

This chapter, then, first presents tI,e general model of bookkeeping transactions, followed by specific models of the more common kinds of transactions; it then presents the bookkeeping meta-model the abstract one. The chapter concludes with a discussion of the thornier problenl of modeling accounting's manipulations to achieve management purposes. In this latter case, no standard models are possible, but the chapter presents some of the kinds of assignments and summarizations that can be found in typical organizations.

The basic rules and transactions for recording this flow are well-defined by the principles of double-entry bookkeeping. See Figure 7. More complex transactions are composed of combinations of the basic transactions. Available labor will be discussed in conjunction with below.

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Examples of this might be credit-card debt, notes payable, or employee withholding. This is usually either owner-held stock or retained earnings. This might be, for example, professional service revenue or product sales. Each dollar of income represents an increase in the organization's equity. This is the money spent for salary, hotel, airfare, purchase of equipment, and similar expenses. Each dollar of expense represents a decrease in the organization's equity.

It is half an attribute because, like the "quantity" of inventory described in Chapter Four, it is derived from all transactions against it. In theory, it is nothing but the sum of all additions to and subtractions from the account since it was established. Even doing this, then, still makes the "current balance" derived, if only from the transactions since the "beginning balance date," so the problem has not gone away. As a matter of convenience, then, "balance" maybe considered an attribute.

Because it is treated-as a stored attribute, each transaction must update "balance" when it occurs. They describe the value of the organization at a given point in time, and how much of that value is either owned by the stockholders or owed to others. That is, the total resources available to the company must equal the sum of those owed by the company plus those contributed by the company's owners. Table 7. For example, a transaction that debits an asset say, "cash" and credits a liability say, "short-term loans" increases both the asset and the liability.

On the other hand, a transaction that debits one asset say, "cash" again and credits another asset say, "inventory" has just added to one and subtracted from the other-converting one asset into another. Debits and Credits.

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As can be seen in Table 7. I L.. With this model, we find the first problem in using a data model to describe accounting: This model is correct, but it is inadequate. The problem is that it does not adequately describe the full range of rules that apply to accounting. This, data modeling was not intended to handle.

We can model the more specific rules. The following sections do just that. The problem, though, is that each transaction has its own model, and there are many more kinds of transactions than can be shown in this or any book. For the time being though, let's give it a try: Certain transactions are central to the operation of a business, and these are presented here. Moreover, once you see how these are modeled, you will be able to create a model of any transaction.

Describing the World: Data Model Patterns

Similarly, this book describes the general rules tha'""tapply to all companie:s. In Figure 7. Revenue First, let's look at how companies recognize revenue. In the second step, the customer pays the balance shown on the invoice. There are alternative points at which to recognize that revenue has occurred. Among them is the distinction between recording revenue on an "accrual basis," where revenue is recognized at the time the invoice is cut, and on a "cash basis," where revenue is not recognized until the invoice has been paid.

Data Model Patterns: Conventions of Thought Data Model Patterns: Conventions of Thought
Data Model Patterns: Conventions of Thought Data Model Patterns: Conventions of Thought
Data Model Patterns: Conventions of Thought Data Model Patterns: Conventions of Thought
Data Model Patterns: Conventions of Thought Data Model Patterns: Conventions of Thought
Data Model Patterns: Conventions of Thought Data Model Patterns: Conventions of Thought
Data Model Patterns: Conventions of Thought Data Model Patterns: Conventions of Thought
Data Model Patterns: Conventions of Thought Data Model Patterns: Conventions of Thought
Data Model Patterns: Conventions of Thought Data Model Patterns: Conventions of Thought
Data Model Patterns: Conventions of Thought Data Model Patterns: Conventions of Thought

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