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Value, in terms of growth potential, is based on earnings and earnings growth. Analysis of earnings and news about a company can give some insight into the quality of earnings. If management has increased earnings by firing half the company s personnel, or the increase is derived from closing several facilities, the quality of the increase is not as valuable as it would be if it reflected improved sales and other revenues. Slash-and-burn strategies can lead to a further decline in productivity, resulting in additional weakness in earnings and eventually lower prices for the stock. On the positive side, drastic cuts can force companies to become more efficient, thereby increasing the quality of earnings, which may lead to higher stock prices. The investor must analyze the company s growth and observe the stock price in action. From the analysis, the investor can determine whether the value of a stock is more likely to increase, remain flat, or begin to decline. The analysis can be difficult at times because a winner can temporarily take on the appearance of a loser. Three situations daily price fluctuations, market declines, and price advances followed by weaknesses can make a winner appear to

In order to understand dimensional design, it is important to clear up this confusion To do so requires a brief look at data warehouse architecture This chapter groups data warehouse architecture into three categories The first two are often called enterprise data warehouse architectures, and are closely associated with W H Inmon and Ralph Kimball, respectively The third does not have a well-known figurehead but is equally common While these architectures differ in fundamental ways, there is a place for the star schema in each of them By understanding these approaches, we can avoid misunderstandings in terminology and develop a clear understanding of the capability of the star schema If you are looking for an answer to the question, What is the best data warehouse architecture you will not find it here There is no discussion of pros and cons Nor will you find comprehensive specifications for each architecture.

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Tough talk in the public and governmental sectors should sound a warning to companies and raise the specter of additional regulations. Quite frankly this is the last thing that companies need, particularly in the wake of the Sarbanes-Oxley Act of 2002, which was created to protect investors by improving the accuracy and reliability of corporate disclosures. The Sarbanes-Oxley Act is the most important and sweeping securities legislation since the 1930s. Among a host of other provisions, it requires that CEOs and CFOs sign off on their financial statements, making a public declaration that the facts and figures are legitimate and verified. This particular requirement was passed because the former CEO and CFO of Enron, in testimony to Congress, stated that they could not be responsible for their own financial statements, and that their auditors were to blame. This so deeply offended members of Congress that they didn t want a top executive to ever be able to say that again. The Act also includes an array of provisions requiring better auditing and oversight by boards. At one point during the Senate debate on Sarbanes-Oxley, two senators who have championed stock-option accounting reform Sens. Carl Levin (D-Mich) and John McCain (R-Ariz) attempted to offer amendments to address this issue. The Levin amendment would have directed the FASB to review and take appropriate actions on stock option accounting within a year. The McCain amendment would have directed the FASB to require companies to treat stock option compensation as an expense on their financial

Instead, the objectives for this chapter are simple: 1 To understand each approach at a high level 2 To understand the place of the star schema in each 3 To eliminate some common misconceptions.

The reaction: The Dow Industrials drifted lower, down 200 points by October 19. Finally came the turn in the trend, as the Dow Industrial Average fell 508 points.

If you have a data warehouse or analytic application in production, don t expect a direct match with one of these archetypes. Each real-world implementation is different. Yours may contain elements from one or more of these architectures. You should make an effort to understand the alternatives, however. This will give you a better grasp of what is and what is not true about dimensional design.

In May of 2000, a well-known thought leader in data warehousing had this to say about dimensional design: if I had to design a data mart tomorrow, I would not consider using any other approach No, it wasn t Ralph Kimball Those words were written by WH Inmon, in an article that appeared in DM Review magazine Although it is not a surprise to people who follow Inmon s approach to data warehousing, these words never cease to astound adherents of other approaches I am not sure how we reached this state of affairs, although I suspect that the trade press s desire to contrast Inmon with Kimball is partly to blame So I will begin by shining some light on Inmon s approach Bill Inmon is a prolific writer and contributor to the data warehousing community.

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