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Each of these hierarchies allows us to add or dis aggregate information. Being able to use different dimensions while we are using the https://myspace.com/lilaalvarez functionality of.
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At this point we should highlight a fundamental concept: Each data model allows to answer a limited number of questions, and each https://id.theguardian.com/profile/myramorton model answer different questions.
In the example we used, the questions that will help us answer the model of the tickets sales are not the same that allow us to answer http://www.codeproject.com/Members/wandacook the lines of ticket sales, the difference being more significant in the latter we product-related information, while the former does not.
When you are building different models is very important that the terminology used and the definition of the terms are the same for http://www.dnnsoftware.com/activity-feed/userid/3047155 everyone. When the models are complex queue management and often build a Metadata Data Dictionary that we all attributes explicit tables, systems from which and the definition of each of these attributes.
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The dimensions should help us to answer the following questions: what? And when? In the case that interests us have different levels http://bbs.boingboing.net/users/brettreyes/activity of aggregation resort to hierarchies, allowing us to add the information at different levels, such as family, subfamily or product.
The level of detail at which we build our model depend on the queue management questions we want to answer from it. Usually http://www.sbnation.com/users/derekwolfe better to build the highest level of detail, as long as the number of records that we loaded into the data warehouse allows.
Each time we add lose some information that we provide the data. In this chapter we have used an example of ticket sales for a very http://www.kongregate.com/accounts/jeremywashington simple supermarket, but more often than not we find ourselves in situations such as queue management the ejemplo presented below.
As we see, the information contained here is much greater and questions. To answer them we build a model of more complex data, with more dimensions; however, the procedure that we follow is the same as we saw in chapter example.
In our example we can therefore analyze the sales of an item for days, months, quarters or years. In our model there are other http://www.thestudentroom.co.uk/member.php?u=1615141 hierarchies: the family, subfamily and durable, the areas and centers, and to the families and employees.
Each of these hierarchies allows us to add or dis aggregate information. Being able to use different dimensions while we are using the https://myspace.com/lilaalvarez functionality of.
Allows us to analyze the information for different dimensions together. For example, if we want to analyze the sales of an item, yet http://www.livestrong.com/profile/brucedaniels/ wish to do so by central or month.
At this point we should highlight a fundamental concept: Each data model allows to answer a limited number of questions, and each https://id.theguardian.com/profile/myramorton model answer different questions.
In the example we used, the questions that will help us answer the model of the tickets sales are not the same that allow us to answer http://www.codeproject.com/Members/wandacook the lines of ticket sales, the difference being more significant in the latter we product-related information, while the former does not.
When you are building different models is very important that the terminology used and the definition of the terms are the same for http://www.dnnsoftware.com/activity-feed/userid/3047155 everyone. When the models are complex queue management and often build a Metadata Data Dictionary that we all attributes explicit tables, systems from which and the definition of each of these attributes.
You can also enter if the fields are recalculated or transformed and the detail of the changes that have taken place. Summarizing data http://in.linkedin.com/pub/cathy-griffin/a6/401/734 modeling, the first thing to do is to define what is the business model that we are preparing for the data to be analyzed.
Once we have the context, we must determine what we want to measure-the facts-and how we want to analyze-the dimensions of https://www.librarything.com/profile/dwightgeorge queue management analysis.
The dimensions should help us to answer the following questions: what? And when? In the case that interests us have different levels http://bbs.boingboing.net/users/brettreyes/activity of aggregation resort to hierarchies, allowing us to add the information at different levels, such as family, subfamily or product.
The level of detail at which we build our model depend on the queue management questions we want to answer from it. Usually http://www.sbnation.com/users/derekwolfe better to build the highest level of detail, as long as the number of records that we loaded into the data warehouse allows.
Each time we add lose some information that we provide the data. In this chapter we have used an example of ticket sales for a very http://www.kongregate.com/accounts/jeremywashington simple supermarket, but more often than not we find ourselves in situations such as queue management the ejemplo presented below.
As we see, the information contained here is much greater and questions. To answer them we build a model of more complex data, with more dimensions; however, the procedure that we follow is the same as we saw in chapter example.
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