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Visibilidad de Atributos – Analysis Services
Visibilidad de Atributos – Analysis Services avatar

October 12, 2011 By Juan Alberto Vélez Casadiego Leave a Comment

En Analysis Services, al crear un cubo, desplegarlo y procesarlo, es muy común encontrar que las dimensiones asociadas se dejan con sus llaves subrogadas a la vista, expuestas a que los usuarios finales hagan uso de ellas. Por ejemplo:

Hagamos de cuenta que tenemos un cubo que lleva por nombre ‘FactTest’, este cubo tiene una serie de dimensiones y entre estas tiene una llamada ‘DimCustomer’, como toda dimensión tiene los atributos básicos, los cuales son..    ..una llave subrogada, una llave de negocio y un descriptivo de la dimensión.

En un reporte, no es útil tener a la vista la llave subrogada para que nuestros usuarios puedan arrastrarla al browser y visualizarla. De hecho confunde al usuario final y le daría a entender que se trata de un código de negocio de cliente (hablando específicamente de la dimensión ‘DimCustomer‘).

Esto siendo así se vería de la siguiente manera:

Para evitar que en el browser se nos despliegue la llave subrogada como en la anterior
imagen debemos acceder a las propiedades de la dimensión a la cual deseamos
ocultarle la llave subrogada. Eso se hace de la siguiente manera.

En el ‘Explorer Solutions’ de ‘Analysis Services’, en ‘Dimensions‘, damos doble
click izquierdo en la dimensión a la cual deseamos ocultarle el atributo.

Luego se abre una nueva pestaña donde debemos hacer click derecho sobre el atributo a ocultar, ingresamos a ‘Properties‘. Ahí en el panel de la derecha buscamos la propiedad ‘AttributeHierarchyVisible’. Esta aparece por defecto en ‘True’, lo cambiamos a ‘False’ y guardamos los cambios.

Posteriormente volvemos a hacerle ‘Deploy ’ y ‘Process’ al proyecto y accedemos al ‘Browser’ para visualizar los resultados.

Y como podemos observar ya no tenemos a la vista el atributo ‘IdCustomer’. El cual no deseábamos tener.

Muchas Gracias de nuevo por visitar mi blog!.

Filed Under: Analysis Services Tagged With: Analysis Services, BI, Business Intelligence

Have Data warehouse and Data mart got the same meaning?
Have Data warehouse and Data mart got the same meaning? avatar

October 12, 2011 By Ana María Orozco Zuluaga Leave a Comment

In my previous blog I exposed something about dimensions such as important part of BI. Now I am going to expose another essential part of BI: Data Mart and/or Data Warehouse making emphasis in the differences that Ralph Kimball and Bill Inmon have between those two definitions.

Gartner Group is an information technology research; it has a Business Intelligence definition that I particularly like: “Set of tools that provide the capacity to business users to analyze a huge amount of data and extract valuable information, which they can use to support fast decision in a highly competitive market”. There is a part that they doesn’t mention and I would like to add something to that definition, with BI we can create knowledge through the data analysis that exist in organizations and companies, that is call Data Mining (It will be my next blog).
Business Intelligence helps to resolve some paradigms existent:

• Users don’t have the correct information in the right time.
• They don’t have a unique true of the information.
• It is difficult to find, consolidate or have a specific detail of the information.

Then, when all those paradigms are resolved the organization gain competitiveness because of the decisions can be taken faster, making decision is much better in all organizations levels and they can make coordinated decisions alignedto all company processes. In the same way the value of BI consists on integration of data from multiple sources, share analyzes with different users, create a scalable infrastructure, so on.
The previous information is a general view about BI. Now I am going to focus the rest of the blog in the design phase because of my main interest is talk about Data Mart and Data Warehouse.

In the pictures below, we can notice easily the main differences between Ralph and Inmon definition.

 

 

 

 

Ralph’s definition says:  Data Warehouse is the result of the union of all small Data Marts inside the organization. For that reason the information is always store in dimensional models.

Bill’s definition instead says: Data Warehouse is only a part of all organization Business Intelligence system. An organization has a Data Warehouse and additionally has a Data Marts that take information from Data Warehouse. In the Data Warehouse the information is in third normal form. However the information in the Data Marts is non-normalized and multidimensional.

In this point I will introduce the two implementation strategies that the authors created, its advantages and disadvantages:

  • Top down by Bill Inmon
    • The first step is to build the Data Warehouse and then extract information needed to generate the Data Marts.
    • The Data marts are dimensional models
    • Advantages:
      • More flexibility
    • Disadvantages:
      • More time in building up
      • The cost is higher
  • Bottom up by Ralph Kimball
    • Data marts are built progressively align with the business priority, and like this you are building the Data warehouse. “Think global, act Local”
    • Advantages:
      • Solutions smaller and faster to build
      • Solutions easier to understand
    • Disadvantages
      • Complex maintenance
      • Synchronizing problems

 

Filed Under: Windows 8 Tagged With: Business Intelligence, Data Mart, Data warehouse

Data Mining, a useful tool in Business Intelligence
Data Mining, a useful tool in Business Intelligence avatar

October 6, 2011 By Ana María Orozco Zuluaga Leave a Comment

In many occasions we have heard about Data Mining but, what is it exactly and when do we have to use it?. Well, I am going to start with some basis definitions I have collected from different sources and authors and I have made a nice combination (from my point of view) that I will share in this post.

  • What is it?

Data Mining is an extraction activity and its objective is discovering facts which are in the data base. In the same way it enables you to deduce hidden knowledge by examining or training the data. The knowledge founded is expressed in patterns and rules.

  • When do we have to use it or when is it useful?
    • Systems partially unknown
    • Huge number of data
    • Powerful hardware and software

Data mining is very useful in many fields such as: Marketing, government, medicine, sales and production. Data mining algorithms are part important in this process, a key to data mining success is selecting an algorithm that is appropriate for your specific task.

In the figure below I show general information of how each algorithms work, its characteristics and the specifics cases when we use it in a particular case.

 

Filed Under: Windows 8 Tagged With: Business Intelligence, Data Mining

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