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OLAP DSS OLTP ERP o Islas Funcionales

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Presentación del tema: "OLAP DSS OLTP ERP o Islas Funcionales"— Transcripción de la presentación:

1 OLAP DSS OLTP ERP o Islas Funcionales
DATA WAREHOUSE OLAP DSS On Line Analitical Process DW OLTP ERP o Islas Funcionales On Line Transactional Process

2 DSS Sistema de Soporte a la Decisión
Fines de los 90: Ciclo de vida de productos y servicios: cada vez más cortos Más calidad a los proveedores Mayor rapidez en las entregas Mejora en los servicios Precios más bajos Planificar y desarrollar nuevos negocios Acelerar el proceso de desarrollo

3 Requerimientos Empresariales de un DSS
Amplitud Navegar a través de cualquier dimensión de negocio Oportunidad Cualquier consulta, en cualquier momento These criteria transcend any particular flavor of OLAP and can be used to determine application requirements and appropriateness of a given OLAP architecture. Optional talking points below -----> There are a variety of technical factors that differentiate MicroStrategy in the marketplace. We want to differentiate for Generic functionality, and the functionality of the enterprises OLAP architectures are measured by Data depth -- access to complete corporate data stores including detailed data --- because many times that is where the answer to the problem lies. Data breadth -- can the system support sophisticated industry business models and diverse applications? With data breadth you can ask all the questions of the decision support system that you are going to want to find the answer to your business problem. Reporting range -- Can I ask a complete range of questions against broad, deep data? These are the requirements for enterprise DSS. ((Optional) There is a lot of debate out there about Relational OLAP versus multi-dimensional cubes and query tools. There’s were the MOLAP/ROLAP wars which was based on two very different technical architectural arguments. Yet, it really didn’t talk about who was providing the answers to business problems in the marketplace. And it probably didn’t make a lot of sense to your business users for that reason. Let me just state this clearly: MicroStrategy’s goal is to solve your business problem, to help you understand your business better and leverage that valuable asset that you have in storage -- your database. We think your database is your databank -- you’ve made the deposits, now we want to be the key player in helping you get a full return on investment. With this in mind, what we would like to talk about today is the functionality of OLAP architectures. We’d like to move away from architectural differentiation at this point and talk about functionality. ) Profundidad Llegar hasta el último nivel de detalle de la información

4 Cobertura de un DSS Rentabilidad por sucursal durante el mes de enero
Oportunidad Profundidad Rentabilidad por sucursal durante el mes de enero Rentabilidad por sucursal y departamento durante el mes de enero Sin limitaciones dimensionales Amplitud Essentially, we have the ability to answer these kinds of business questions in the each industry and we believe other people hit data size complexity walls. Key point: No limits with MicroStrategies architecture, full relational OLAP. Hallmark, now has 250 users doing heavy-duty analysis against more than 250 GB day in, day out. Worldcom is going against a 3 Terabyte warehouse with a data model containing over 25 dimensions. Rentabilidad por sucursal y departamento de los 5 productos más vendidos durante el mes de enero, y perfil de los clientes que compraron estos productos, por edad, sexo e ingreso mensual

5 Qué es un DW Almacén de información temática orientado a cubrir las necesidades de aplicaciones de los sistemas de Soporte de Decisiones (DSS) y de la Información de Ejecutivos (EIS), que permite acceder a la información corporativa para la gestión, control y apoyo a la toma de decisiones. Transformación de datos orientados a una aplicación, en datos de soporte a las decisiones (datos que capturen la naturaleza básica del negocio)

6 DW es un conjunto de datos:
Temático Están almacenados por materias o temas, a diferencia de los sistemas operacionales en donde los datos están agrupados según las aplicaciones que los utilizan. Integrado Son de naturaleza heterogénea, provienen de diferentes sistemas heredados o de información no procesada (interna o externa a la empresa) y variables en el tiempo.

7 DW es un conjunto de datos:
No volátil Se cargan y consultan. La actualización de datos no forma parte de la operativa normal de un DW. Histórico Las bases de datos operacionales contienen los valores actuales de los datos. Un DW es una serie de instantáneas en el tiempo tomadas periódicamente. Los datos almacenados en un DW permanecen en él más tiempo que en una base de datos operacional.


9 Arquitectura Sistemas Operativos Data Warehouse DSS Engine EIS
Data Extraction Metadata Database Decision Support Metadata Database Decision Support Client-server based systems for data access and analysis Decision Support Topics Operational System Data Warehouse Data Extraction Metadata Database Decision Support Metadata Database DSS Engine DSS Applications Introduction to DSS and Data Warehousing Decision Support Overview I.2

10 Generador de Reportes Data Warehouse Usuarios Report Writers
Open system design Provides flexibility and scalability Limited analytic capability Not designed for multidimensional object views Executes only simple pre-defined data queries Direct interaction with data warehouse Allows storing data in a relational format Utilizes the capabilities of parallel-scaleable relational databases Data Warehouse Usuarios Introduction to DSS and Data Warehousing Decision Support Overview I.9

11 Análisis Multidimensional
Multidimensional OLAP Proprietary system design Intermediary data cube proprietary per application vendor Limits flexibility and scalability Sophisticated analytic capability Offers multidimensional object views Supports ad-hoc reporting and analysis Multidimensional data cubes leverage data warehouse interaction Requires storing data in a proprietary multidimensional cube format, as opposed to an open relational format Exhibits increasing ineffectiveness as data volumes and dimensions increase Exponential growth occurs with any addition to the multidimensional data cube Reduces potential data storage (normally 5-10 GBs) Reduces the potential number of dimensions (normally 8-10) Data Warehouse Multidimensional Data Cubes Clientes Introduction to DSS and Data Warehousing Decision Support Overview I.10

12 Modelo de Datos Data Model
Logical diagram of an organization’s entities Contains only those entities to be included in a DSS project Basis from which a data warehouse is designed Data Model Topics Dimensions Attributes Attribute Elements Attribute Hierarchies Attribute Relationships Metrics Introduction to DSS and Data Warehousing Data Modeling II.2

13 Dimensiones Geografía Producto Tiempo Dimensions
Subject areas or lines of business Comprised of attributes Separate and distinct from one another (i.e. do not share attributes) Not explicitly found in the data warehouse; only conceptual Means for multidimensional analysis Provide multiple data perspectives Introduction to DSS and Data Warehousing Data Modeling II.3

14 Atributos Producto Geography Division Department Class Item Attributes
Hierarchically arranged groupings within dimensions Comprised of elements Separate and distinct from one another (i.e. do not share elements) Uniquely assigned to dimensions Means for organizing multidimensional analysis Provide various summarization levels Introduction to DSS and Data Warehousing Data Modeling II.4

15 Elementos Producto Geography Division Department Class Item
Men’s Clothing Shoes Sporting Goods Attribute Elements Attribute values Atomic data model components Uniquely assigned to attributes Not explicitly included in a data model (contribute limited understanding); however, necessary to understand attribute relationships Means for focusing multidimensional analysis Provide values on which to qualify Men’s Dress Wear Men’s Casual Wear Men’s Accessories Men’s Shoes Women’s Shoes General Equip. Lic. Team Apparel Men’s Dress Shirts Men’s Dress Slacks Men’s Casual Pants Men’s Sweaters Men’s Ties College Jerseys Team Caps Pro Jerseys ... White Button-Down Blue Straight Collar Black Cuffed Brown Cuffed Navy Pattern Tie Red Solid Tie Stanford Jersey Washington Jersey ... Introduction to DSS and Data Warehousing Data Modeling II.5

16 Jerarquías Producto Geography Main Attribute Hierarchy Division
Attribute Hierarchies Logical orderings of attributes within dimensions Incorporate attribute relationships Multiple hierarchies can exist within a dimension Main attribute hierarchy Hierarchy of primary concern Characteristic attribute hierarchy Descriptive hierarchy building from the main hierarchy Means for data navigation Define data drilling paths Department Class Characteristic Characteristic Color Item Style Introduction to DSS and Data Warehousing Data Modeling II.6

17 Relaciones Producto Geography Division Item Class Color Style
Department Many-to-Many One-to-Many Attribute Relationships Logical associations of attributes within hierarchies Attribute elements define attribute relationships Transitive within hierarchies Relationship types One-to-one One parent element may correspond to one child element One-to-many One parent element may correspond to one or more child elements Many-to-one One or more parent elements may correspond to one child element Many-to-many Further means for data navigation Further define data drilling paths Introduction to DSS and Data Warehousing Data Modeling II.7

18 Métricas Producto Geografía Tiempo Geography Metrics
Business measurements or variables Comprised of actual data values Exist at the intersections of dimensions and dimensional components Focus of decision support investigations Provide measures of business performance Introduction to DSS and Data Warehousing Data Modeling II.8

19 Ejemplo Producto Geografía Tiem´po Geography Geography Geography
Division Manager Year Department Region Month Class Design Goals Define the scope of the decision support project and, thereby, the data warehouse Build a model from which a data warehouse can actually be constructed Distinct dimensions Distinct attributes Defined attribute hierarchies Defined attribute relationships Market Week Color Item Style Store Day Sales Inventory Cost Price Introduction to DSS and Data Warehousing Data Modeling II.9

20 E-Mail de Alertas Automáticas
Mensajes en modo texto Análisis Avanzado: Informe de productos ….automatic alert to store managers. A number of broadcast media are possible, and for this type of notification, will probably be the most common output medium. The notification could be triggered either because of a set time schedule or because a critical threshold was reached. [The three highlighted features (Each Click opens the next) are quite self-explanatory]. Key points: · Ease of use (everybody has ) · Content is formatted · Plain English (natural language) The third feature highlighted here is the ability to embed hyperlinks. This way, not only are we notified of critical alerts, but we can also drill-down for more detailed information - in this case via a WWW report. Hyperlinks a DSS Web

21 Theme: This slide displays a typical DSSWeb interface of a retailing project. It is an optional slide but would be shown if it's important to sell DSSBroadcaster as an accelerator for DSSWeb sales. In addition, many retailers already have web applications in place and this provides an opportunity of demonstrating how DSSBroadcaster would fit into their existing infrastructure.


23 ¿Qué es Data Mining? La extracción de información oculta y predecible de grandes bases de datos. Las pequeñas organizaciones construyen relaciones con sus clientes CONOCIENDO sus necesidades, RECORDANDO sus preferencias y APRENDIENDO de las interacciones del pasado para mejorar el futuro. Un Data Warehouse permite RECORDAR lo que las organizaciones CONOCEN sobre sus clientes. Data Mining permite APRENDER sobre los datos recogidos de la interacción con los clientes.

24 ? ¿Qué es Data Mining? Datos Memoria Inteligencia DW
Sistemas Transaccionales Data Warehouse La INTELIGENCIA permite a través de la MEMORIA (DW) extraer patrones, reglas de negocio, nuevas ideas y hacer predicciones para el futuro.

25 ¿Qué es Data Mining? 1) Es exploración y análisis INTELIGENTE,
con procedimientos automáticos o semiautomáticos, de grandes cantidades de datos para descubir patrones y reglas de negocio. 2) En un sistema OLAP tradicional el usuario debe especificar las relaciones entre los datos. DATA MINING puede DESCUBIRIR las RELACIONES DESCONOCIDAS o INVISIBLES para el negocio.

26 Data Mining y Data Warehouse
DW Consultas Inteligentes.: 80 % Comunes….: 20 % Inteligentes.: 20 % Comunes….: 80 % Incremento Conocimiento DM Extracción de una MUESTRA INTELIGENTE Knowledge Engine Data Mining Relaciones Variables Atributos

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