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Part II
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The Business Side of Data Warehousing
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database designs, but become disorganized over time, such as when the application is modi ed to meet changing business needs Systems professionals don t design systems or make changes to intentionally produce bad data Intense demands are placed on these systems to provide functionality that was never part of the original design, and the changes must be done in a timely manner As this happens repeatedly, even the cleanest database design can be degraded If there are long-standing problems with the company s data, then it will take a combined effort of IT and business professionals to correct these de ciencies This includes business decisions about how to handle the data, changes to the production application systems, changing business processes, and adding cleansing and transformation rules to the data warehouse ETL processes The data warehouse team may be at the center of it all The bigger the problems, the longer it typically takes to correct them It will take time
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Does the Business Place Value on Analysis
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An organization s readiness for a data warehouse re ects the value that is placed on fact-based decision-making and reliance on the results from business analysis A data warehouse is a mechanism for the organization to better leverage data and support more sophisticated analyses How can you tell if your organization is ready Several characteristics indicate the value that an organization places on analysis, including the following: There are clearly de ned roles and/or jobs for business analysis There is a career path for business analysts, which ensures more senior positions without going into management The people who perform business analysis are well respected throughout the company If an organization does not place a lot of value on analysis, this can limit the willingness of the business community to participate in the design and development of a data warehouse Without this partnership, organizations often fail to realize bene t from their data warehouse Certain industries have developed a strong appreciation for fact-based decision-making and the value of analysis The nancial solvency of the insurance industry is highly dependent upon the accuracy of analyses performed by the actuarial groups This analysis helps the company to understand risks, determine the types of risks to handle, and set competitive rates Consumer packaged goods and retail industries have been investing and developing strong analytics to improve inventory management, identify customer purchase patterns, and develop effective promotions
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Some organizations do not have a strong track record of using data and may not have much experience performing sophisticated business analysis This does not mean these organizations should avoid building a data warehouse; it just means that additional effort must be spent on developing analytical skills This also means that it will take longer for the data warehouse to evolve to support the business because the business itself must evolve in its use of data and the types of analyses that will be performed
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Re ecting on Your Data Warehouse History
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In order to get a good understanding of the big picture, it is worth taking some time to re ect on the experiences that your organization has had with a data warehouse In many cases, more than one data warehouse or data mart are in place Questions that you can ask about each data warehouse include the following: Is this an enterprise data warehouse or a collection of non-integrating data marts Does IT consider the data warehouse to be a success If not, why not Does the business community consider the data warehouse to be a success If not, why not Were business goals set for the data warehouse Have these been met Who, from the business community, was involved in designing and building the data warehouse Was this active participation or periodic status updates Does the business feel that this is their data warehouse, or did IT build it with the hope that the business would nd it useful Is the data warehouse still being used today If not, why not From an overall perspective, does the organization have a broad data warehouse strategy in place today The objective is to get an overview of what has happened in the past If previous data warehouse projects have been successful, then the organization will collectively be more positively inclined toward any new projects If all previous data warehouse attempts are considered failures, then you are likely to run into resistance in getting another one started It is critical to understand the root causes of any failures so that new projects can develop speci c strategies to avoid past mistakes The project team needs to effectively communicate what is being done differently this time so that this effort will be a success Digging into the details about individual project success or failure is explored in the next section At this point in time, it is important to simply get an
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