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Building your own data warehouse sounds simple obtain an ETL tool and use it to move data from JD Edwards databases into a data warehouse In practice, doing so turns out to be far more complicated A significant percentage of those who have tried to do it themselves have either failed or spent far more money and time than planned Many companies spent more than a million dollars over the course of a year or more and are still not satisfied with the result The most difficult thing about creating a good data warehouse is the design of the data model around which it will be built Decisions need to be made as to the names to give each field (since the names in the JD Edwards database can be obtuse), whether each data field needs to be reformatted, and what metadata fields should be calculated and added It is also necessary to decide what, if any, data items from sources outside of the JD Edwards database should be added to the data model

years old Whatever benefits the former spouse receives has no bearing on the amount of benefits the working person will receive Taxation of Benefits Approximately 20 percent of people claiming social security benefits must pay taxes on what they receive Benefit taxation only affects those people with substantial income in addition to their benefits To help determine if your benefits would be taxable, the social security Administration sends out a benefit statement to every social security recipient This mailing shows the amount of benefits you have received Then, when you are (or your CPA) is preparing your taxes, you will be able to determine if you will need to pay taxes on your benefits If you find that you will, in fact, have to pay taxes on your benefits, you may realize that you will also have to pay taxes on you benefits in years to come You can either have taxes taken out of your monthly checks, or you can opt to pay quarterly estimates Generally, it s easier to have a set percentage taken out of your benefit checks

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Once a data warehouse is made operational, it is important that the data model remain stable If it does not, then reports created from that data will need to be changed whenever the data model changes New data fields and metadata need to be added over time in a way that does not require reports to be rewritten The bottom line is that the do-it-yourself approach to data warehousing is risky business The first alternative to building a data warehouse yourself is to buy frameworks from which a data warehouse can be created This approach involves less time, cost, and risk than building your own warehouse In theory, frameworks can be customized to your special needs However, doing so is not always easy, inexpensive, or foolproof Success is highly dependent on the skills of those doing the customization As such, the experience of those using the framework approach is better than those building their own from scratch; however, this is still not ideal Fortunately, it is no longer necessary to deal with the risks associated with building all or much of a data warehouse yourself Complete, prebuilt JD Edwards solutions are available that make it possible to have a data warehouse up and running quickly Oracle recently announced plans to revamp its now obsolete JD Edwards enterprise performance management (EPM) data marts to make them compatible with OBIEE Details about this offering, which will be called Oracle Fusion Intelligence for EnterpriseOne, were not available when this book went to press

Those exploring the options for obtaining a data warehouse need to be aware that custom creation is sometimes strongly advocated by vendors that hope to provide assistance for doing so The available prebuilt options should always be examined before trying to build it yourself The value of a data warehouse increases over time It therefore pays to start putting one in place as soon as practical Those who delay starting down this path could remain at a disadvantage versus competitors that begin sooner Payback in terms of business benefits tends to come rapidly in many forms: Hard savings come from things like discovering lost discounts in payables or finding that salespeople are offering discounts beyond approved limits Real-time consolidation of financial data becomes practical Debates cease over which source of data is correct The IT costs and staff dedicated to report development and maintenance are significantly reduced

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