Slide Master Layout Useful for revisions and projector test

Presentación del tema: "Slide Master Layout Useful for revisions and projector test"— Transcripción de la presentación:

1 Slide Master Layout Useful for revisions and projector test
First-level bullet Second levels Third level Fourth level Fifth level Drop body text block to if more than two lines of text in title Next horizontal guide set at +1.17 Left vertical guide set at -4.58 KEY MESSAGE: SLIDE BUILDS: None SLIDE SCRIPT: SLIDE TRANSITION: ADDITIONAL INFORMATION FOR PRESENTER:

2 Colors In This Template Useful for testing projectors during setup
KEY MESSAGE: SLIDE BUILDS: None SLIDE SCRIPT: SLIDE TRANSITION: ADDITIONAL INFORMATION FOR PRESENTER:

3 TNT1-57 KEY MESSAGE: SLIDE BUILDS: None SLIDE SCRIPT:
SLIDE TRANSITION: ADDITIONAL INFORMATION FOR PRESENTER:

4 Construyendo Negocios Inteligentes con Office System y SQL 2000 Analysis Services
KEY MESSAGE: Introduce the session. SLIDE BUILDS: None SLIDE SCRIPT: Hello and Welcome to this Microsoft TechNet session on Building Business Intelligence with Office XP and SQL 2000 Analysis Services. My name is {insert name} SLIDE TRANSITION: Let’s look at what this session will cover. ADDITIONAL INFORMATION FOR PRESENTER:

5 Que veremos Trabajando con Analysis Services Data Mining
Trabajando con datos BI en Excel Análisis de datos con Data Analyzer KEY MESSAGE: Review the topics that will be covered. SLIDE BUILDS: None SLIDE SCRIPT: In this session we will cover working with SQL Server Analysis services, including working with OLAP cubes in the Analysis manager. We’ll look at the data mining feature in Analysis Services that allows you to analyze data and formulate predictions. We’ll see how Excel can access and work with Business Intelligence data. We will see how the Microsoft Data Analyzer can be used to quickly analyze data for trend, opportunities, and issues. Finally we’ll look at how MapPoint can access Business Intelligence data, to display it and perform geo-spatial analysis of the data. SLIDE TRANSITION: Now let’s look at the session prerequisits. ADDITIONAL INFORMATION FOR PRESENTER:

6 Agenda SQL Server 2000 Analysis Services Integrando BI con Office
Analizando datos con data Analyzer KEY MESSAGE: Review the agenda, and introduce the first agenda item. SLIDE BUILDS: None SLIDE SCRIPT: This is the agenda for this session, the first topic we’ll be covering is SQL Server 2000 Analysis Services. SLIDE TRANSITION: As a first step, let’s look at the components used in a Microsoft Business Intelligence solution. ADDITIONAL INFORMATION FOR PRESENTER:

7 Análisis de ventas al por menor y finanzas
SQL Server 2000 Analysis Services La solución Microsoft para Inteligencia de Negocios Gestión de Proyectos Análisis de Sitios Web Análisis de ventas al por menor y finanzas Visualización Colaboración Análisis de datos Análisis Geoespacial KEY MESSAGE: Review the applications associated with a business intelligence solution from Microsoft. SLIDE BUILDS: 3 SLIDE SCRIPT: [BUILD 1] The core of Microsoft’s BI solution is Office XP which provides the front end for both basic data analysis and modeling through Excel and presentation through PowerPoint. SQL Server provides the back end data warehousing, and more advanced analysis capabilities with SQL Analysis Services. [BUILD 2] In addition, these other applications provide additional analysis can presentation capabilities. [BUILD 3] MapPoint can display data in geographic terms, and provide geo-spatial analysis. The Data Analyzer has an easy-to-use interface that provides robust analysis capabilities, without the need to know how to construct advanced data analysis queries. SharePoint Portal Server provides web base collaboration, that allows your company to share data and analysis collaboratively. Office Web Components can bring the data, or the analysis to the web, through a custom web site, or a Digital Dashboard site directly from Microsoft Office applications. Project 2002 provides project management analysis. Commerce Server 2000 allows clickstream analysis on the web for internal team members or even external partners. The BI Data, the SQL Accelerator for BI provides analysis of retail sales and marketing. SLIDE TRANSITION: Before any analysis can take place, or be published, you have to have data. Let’s take a look at what a data warehouse is, and why the concept of a data warehouse is central to business intelligence. ADDITIONAL INFORMATION FOR PRESENTER: Microsoft Business Intelligence Platform:

8 SQL Server 2000 Analysis Services Bodegas de Datos
Base de datos almacenando la historia del negocio Soporta el análisis de datos No diseñado para procesamiento de transacciones en tiempo real EL Análisis soporta las decisiones de negocio Ejemplo: Analizar ventas para enfocar promoción de producto a determinados clientes KEY MESSAGE: Explain that a data warehouse stores historical data used for analysis. SLIDE BUILDS: None SLIDE SCRIPT: A data warehouse is a database containing data that usually represents the business history of an organization. This can include historical sales data, customer data such as profiles, and other similar data. This historical data is used for analysis that supports business decisions at many levels, from strategic planning to performance evaluation of a discrete organizational unit. Data in a data warehouse is organized to support analysis rather than to process real-time transactions as in online transaction processing systems (OLTP). An example of the type of analysis one would perform on data in a data warehouse might be to analyze sales data so that products and brand advertizing is targeted to specific customer demographics. SLIDE TRANSITION: Now let’s look at how you access and manage data in a data warehouse. ADDITIONAL INFORMATION FOR PRESENTER: Article on Data Warehousing: Data Warehousing and OLAP: Overview of Analysis Services (good links):

9 SQL Server 2000 Analysis Services Administrando datos para inteligencia de negocios
OLAP – Online Analytical Processing OLAP brinda modelamiento de datos Las bases de datos OLAP se llaman cubos Estructura Multidimensional de los datos Definido por medidas y dimensiones Brinda una estructura intuitiva para los usuarios Los analistas solo ven la información que quieren analizar Las agregaciones brindan un rápido desempeño en las consultas KEY MESSAGE: Review the slide and explain the features of OLAP. SLIDE BUILDS: None SLIDE SCRIPT: OLAP is a term you hear when people talk about data warehouses and business intelligence. OLAP stands for Online Analytical Processing. OLAP technology enables data warehouses to be used effectively for online analysis, providing rapid responses to iterative complex analytical queries. OLAP creates multidimensional data models called cubes which contain a subset of data from the datawarehouse. Analysts build cubes using measures, which are the central numbers being analyzed, and dimensions, which are data elements related to the central numbers that they wish to explore. For example, an analyst may want to examine sales by product, the measure, and see how it relates to customer gender over time, dimensions. OLAP provides a natural, intuitive structure for users. It provides them with only the data elements they wish to analyze. This refines the large amount of data in the data warehouse down to only those elements and relationships the analyst is interested in. This allows data to be evaluated quickly using online analysis and graphical tools. OLAP also provides for data aggregation algorithms, which compute analysis data ahead of time. This offers fast query performance. SLIDE TRANSITION: Let’s look at the structure of an OLAP cube. ADDITIONAL INFORMATION FOR PRESENTER: Data Warehousing and OLAP: OLAP and Data Mining:

10 SQL Server 2000 Analysis Services Estructura de cubo – Esquema y Tablas
Data Warehouse Esquema Tablas relacionadas en la bodega Brinda datos para el cubo Tala de hechos Tabla central en un esquema Datos numéricos (hechos) Brinda información histórica sobre la operación Talas de dimensiones Talas adicionales unidas a la tabla de hechos KEY MESSAGE: Explain that a cube is built from table in the data warehouse that have relationships to each other. SLIDE BUILDS: 3 SLIDE SCRIPT: [BUILD 1] The schema for a cube provides information about how the tables in the cube are related to one-another. Remember, the tables are coming from a relational database. [BUILD 2] The fact table is the central table in the cube. It contains the numerical data that is to be analyzed. This is the historical information about operations in the organization. [BUILD 3] Dimension tables are related to the fact table, and provide insight into the facts being analzyed. That’s the real strength of OLAP cubes. They allow analysts to look at the numbers, and see how those numbers relate to and are affected by other data. This allows them to spot issues, trends, and opportunities. SLIDE TRANSITION: Once the tables the cube will pull data from have been defined, you can pick the data you wish to analyze by defining dimensions and measures. ADDITIONAL INFORMATION FOR PRESENTER: Dim Table Fact Table Dim Table Dim Table Dim Table

11 SQL Server 2000 Analysis Services Estructura de cubo – Dimensiones y medidas
Fact Table Dim Table Medidas Valores de la tabla de hechos Valores que son agregados y analizados Dimensiones Valores de las tablas de dimensión Describen un conjunto similar de miembros para ser analizados Niveles Jerarquías dentro de las dimensiones Fraccionamiento categórico Los niveles de tiempo pueden ser Año, trimestre, mes KEY MESSAGE: Explain how measures, dimensions and levels relate to the structure of a cube. SLIDE BUILDS: 3 SLIDE SCRIPT: [BUILD 1] In a cube, a measure is a set of values that are based on a column in the cube's fact table and are usually numeric. Measures are the central values of a cube that are analyzed, meaning measures are the numeric data of primary interest to end users browsing a cube. Measures are chosen based on what analysis needs to be done. Some common measures are sales, cost, expenditures, and production count. For each measure in a cube, the cube contains a value for every cell in the cube excluding the cells for the other measures. So, no matter which combination of members is used in a query, a measure value can be retrieved. [BUILD 2] A dimension is an organized hierarchy of categories, known as levels, that describes data in data warehouse fact tables. Dimensions typically describe a similar set of members upon which the user wants to base an analysis, and they are a fundamental component of cubes. For example, if a cube is designed to analyze sales by store, you might add dimensions for time, so you could evaluate store sales monthly, quarterly and yearly. You might add a dimension for product, so you could evaluate store sales by products sold. You might also add a dimension for customers, so you can see relationships between store sales and customer demograhics. Cubes can have multiple dimensions. [BUILD 3] A level is an element of a dimension hierarchy. Levels describe the hierarchy from the highest (most summarized) level to the lowest (most detailed) level of data. For example, in a time dimension, the levels might be from yearly, to quarterly, to monthly. SLIDE TRANSITION: So now that we know about OLAP and cubes, what are Analysis Services. ADDITIONAL INFORMATION FOR PRESENTER: SQL Server Books Online: Measure, Dimension, Levels overviews

12 SQL Server 2000 Analysis Services Donde está Analysis Services?
Antes OLAP Services (SQL Server 7) Es una capa media para OLAP y Data Mining OLAP – Acceso a los datos Simple de seleccionar, navegar y explorar datos KEY MESSAGE: Review the slide and explain how OLAP allows users to explore data. SLIDE BUILDS: 3 SLIDE SCRIPT: Analysis Services used to be called OLAP Services in SQL Server 7.0. Analysis Services includes a server that manages multidimensional cubes of data for analysis and provides client access over a network to cube information. Analysis Services organizes data from a data warehouse into cubes with precalculated aggregation data to provide rapid answers to complex analytical queries. PivotTable® Service, the included OLE DB compliant provider, is used by Microsoft Excel and applications from other vendors to retrieve data from the server and present it to the user, or create local data cubes for offline analysis. [BUILD 1] An example of exploring data might be to analyze a cube that contains data for a retail chain about sales by store. [BUILD 2] Analysis services can isolate customer data, and show demographics as they relate to store sales. [BUILD 3] Or, Analysis Services can find products by brand and customer related to store sales. SLIDE TRANSITION: These types of queries are complex, Analysis Services can optmize query performance by building aggregations. ADDITIONAL INFORMATION FOR PRESENTER: OLAP and Data Mining: Ejemplo de exploración de datos: Cubo de ventas por tienda Encontrar datos demográficos de los clientes Encontrar productos por marca y cliente

13 SQL Server 2000 Analysis Services Diseñado para desempeño
Desempeño para soportar análisis en tiempo real Los analistas exploran posibilidades y tendencias Las consultas llevan a consultas mas complejas Las agregaciones calculan valores por adelantado Contesta preguntas antes de formularse Aumenta el desempeño de la consulta Defina cuando el cubo es almacenado y es reconfigurable Contracara: Desempeño contra espacio Mas agregaciones toman mas espacio Analysis services brinda estimaciones KEY MESSAGE: Explain the cube performance can be optimized by creating aggregations. SLIDE BUILDS: None SLIDE SCRIPT: The answer to a query into historical data often leads to subsequent queries as the analyst searches for answers or explores possibilities. OLAP systems provide the speed and flexibility to support the analyst in real time using aggregations. Aggregations use algorithms to figure out what questions analysts will ask, and compute the answers in advance. This improves query performance for the cube. The aggregations are configured when the cube is saved, and can be reconfigured later. You trade performance for space when configuring aggregations. It is possible to compute all possible answers in advance, however, when dealing with the amounts of data in a cube built from a large data warehouse, the size of the aggregations can be considerable. If space isn’t an issue, you could do this. If space is an issue, you can reduce the number of aggregations, to save space. The goal is to aggregate the most frequently requested answers. Answers to questions that haven’t been aggregated can still be determined, those queries just take a little longer to complete. SLIDE TRANSITION: Now lets go to our first demonstration. ADDITIONAL INFORMATION FOR PRESENTER: Aggregations in Analysis Services:

14 Demostración 1 Trabajando con cubos OLAP Creando un cubo Diseñando el desempeño de un cubo
KEY MESSAGE: Review what the demonstration will cover. SLIDE BUILDS: SLIDE SCRIPT: In this demonstration we’ll be working with OLAP cubes, building a cube to analyze data, and designing cube performance. SLIDE TRANSITION: Now let’s look at another way Analysis Services can help us analyze our data. ADDITIONAL INFORMATION FOR PRESENTER:

15 SQL Server 2000 Analysis Services Analysis Services – Modelos de Data Mining
Modelos de Minería de datos Buscar patrones Hacer predicciones Almacenar resultados Columna de una tabla, dimensión de un cubo, Diagrama de minería de datos KEY MESSAGE: Explain the data mining models analyze data to find patterns and make predictions. SLIDE BUILDS: 4 SLIDE SCRIPT: [BUILD 1] A data mining model is the central object in data mining. It’s a new feature in SQL Server™ 2000 Analysis Services. A data mining model is a virtual structure that represents the grouping and predictive analysis of relational or multidimensional data. [BUILD 2] Data mining models find patterns in the data, and visually represent them for analysts. [BUILD 3] Analysts can use Analysis Services determine trends from those patterns, and make predictions about the future. This information is displayed graphically using Analysis Services tools. [BUILD 4] The output can be stored in tabular column formation, a cube dimension (sometimes called a trained cube) that can be reused, or a mining model diagram that visually illustrates the pattern determined. SLIDE TRANSITION: Let’s look at an example of how data mining is used. ADDITIONAL INFORMATION FOR PRESENTER: Introduction to Data Mining Models: Decision Trees: Data Mining Model Algorithms:

16 SQL Server 2000 Analysis Services Data Mining – Ejemplo de alquiler de películas
Alquiler On-line“Movie Lovers Club” Información de los miembros es conocida Demograficos Películas favoritas Objetivo: Aumentar las ventas! Predicciones que queremos hacer: Películas que prefieren los clientes Datos de entrada necesarios: Demográficos Otras películas que le gustan a los clientes Que algoritmo? Arboles de decisión - SQL Analysis Services KEY MESSAGE: Review the slide and explain the scenario behind the example mining model covered. SLIDE BUILDS: None SLIDE SCRIPT: This is an example of data mining. The data being analyzed is for an on-line movie rental company. The “Movie Lovers Club.” The data warehouse has lots of member information such as demographics, favorite movies, and so forth. The goal is to increase sales. To do that, we want to figure out what types of movies customers like so we can do things like make smart purchasing decisions, and target advertising. The data we need is the customer demographic information, and information from the data warehouse about favorite movies, and other movies they like. That information is already in an OLAP cube, we can create a data mining model the looks for patterns. In this case we’ll use a Decision Tree algorithm. A decision tree is a form of classification shown in a tree structure, in which a node in the tree structure represents each question used to further classify data. SLIDE TRANSITION: Now that we know what we want to do, we need to identify the data we’ll need to perform the analysis. ADDITIONAL INFORMATION FOR PRESENTER:

17 SQL Server 2000 Analysis Services Data Mining – Mezclar información relevante
Mezclar la información del cliente Con las películas favoritas Cust ID Edad Estado IQ 1 35 M 2 20 S 3 57 Películas favoritas Titulo Puntaje Star Wars 8 Pulp Fiction 9 Usual Suspects 7 Braveheart Dr. Strangelove 10 The Matrix Blade Runner KEY MESSAGE: Explain that the mining model is relating these two sets of data, and looking for patterns. SLIDE BUILDS: 2 SLIDE SCRIPT: [BUILD 1] The first set of data is the customer demographic data. This includes age, martial status and IQ information. The IQ information is related to what type of person they are, that is a “thinking” or “feeling” personality type. [BUILD 2] The customer information is combined with information about the movies they like. SLIDE TRANSITION: Now that the information has been added, we can analyze the data. ADDITIONAL INFORMATION FOR PRESENTER:

18 SQL Server 2000 Analysis Services Decision Trees – Primera clasificación
Marital Status Personality The Matrix Gone With the Wind Married Single Other Feeling Thinking True False Gender Male 809 556 104 711 758 295 1174 18 1451 Female 141 113 32 183 103 39 247 60 226 KEY MESSAGE: Explain that nodes in the decision tree questions used to classify data. SLIDE BUILDS: 2 SLIDE SCRIPT: You can see the information used in the table at the top of the slide. You can classify the data using key factors that you choose. In this case, the key factor is gender. [BUILD 1] The first classification is looking at all people. Of all members, 84% are mail, while 16% are female. [BUILD 2] Further classifying the data, the movie Gone with the Wind is analyzed. This provides information about what genders picked Gone with the Wind as on of their favorite movies. 43% were male, whil 57% were female. 85% of males, and 15% of females didn’t pick Gone with the Wind. SLIDE TRANSITION: You can further classify this information ADDITIONAL INFORMATION FOR PRESENTER: Gender: 84% Male 16% Female All People Gender: 23% Male 77% Female Picked ‘Gone With the Wind’ 85% Male 15% Female Didn’t Pick it

19 SQL Server 2000 Analysis Services Decision Trees – Segunda clasificación
Marital Status Personality The Matrix Gone With the Wind Married Single Other Feeling Thinking True False Gender Male 12 5 1 9 4 14 18 Female 30 22 8 50 10 52 60 KEY MESSAGE: Explain that additional nodes can be added to the decision tree, each node represents a question that further classifies the data. SLIDE BUILDS: 2 SLIDE SCRIPT: Additional nodes can be added to the decision tree, each node represents a question that further classifies the data. We have the same decision tree as before, but now another node can be added. In this case, a question about personality type might be in order. For example, of the people who listed Gone with the Wind, what percentage were “Feeling” personality types, and what percent were “Thinking” personality types? [BUILD 1] For “Feeling” personality types, 15% were male, while 85% were female. [BUILD 2] For “Thinking” personality types, 50% were male and 50% were female. So a pattern has emerged here. Gone with the Wind has the most appeal to females who are members of the club, and especially to females with the feeling personality type. Advertisements for movies like Gone with the Wind should be targeted at that audience. SLIDE TRANSITION: Now I’ll demonstrate building a data mining model, and analyzing data. ADDITIONAL INFORMATION FOR PRESENTER: Gender: 84% Male 16% Female All People 23% Male 77% Female 86% Male 14% Female Didn’t Pick it Picked ‘Gone With the Wind’ Gender: 15% Male 85% Female Feeling Gender: 50% Male 50% Female Thinking

20 Demostración 2 Data Mining Crear un modelo de Minería Leyendo un árbol de decisión
KEY MESSAGE: Review what the demonstration will cover. SLIDE BUILDS: SLIDE SCRIPT: In this demonstration we’ll be building a data mining model, and reading a decision tree. SLIDE TRANSITION: Now let’s move on to the next agenda item. ADDITIONAL INFORMATION FOR PRESENTER:

21 Agenda SQL Server 2000 Analysis Services Integrando BI con Office
Analizando datos con Data Analyzer KEY MESSAGE: Introduce the next agenda item. SLIDE BUILDS: None SLIDE SCRIPT: The next topic in the agenda is Business Intelligence integration with Office applications. SLIDE TRANSITION: First let’s look at the features in Office XP that enable you to access, manipulate, analyze, and publish Business Intelligence data. ADDITIONAL INFORMATION FOR PRESENTER:

22 Integrando BI con Office Integración de Excel
Hacer los datos de BI disponibles para todos los empleados Excel es la pieza central Provee capacidades de análisis robusto Características Real-Time Data (RTD) OLAP Tablas Pivote Asistentes de conexión Soporte XML Excel Tablas Pivote KEY MESSAGE: Review the slide and explain how Excel can access and manipulate business intelligence data. SLIDE BUILDS: None SLIDE SCRIPT: Office XP was designed to allow business intelligence data to be easily integrated into Office documents. One of the primary objectives was to make business intelligence data easily accessible to users. Excel is the centerpiece for connecting to, displaying, and analyzing business intelligence data. Excel provides informational views and querying, reporting, and analysis capabilities that let users work with the data inside their documents. Excel features that target BI Integration include: Real-Time Data (RTD) allows users to bring real-time data into Excel. OLAP PivotTables have many improvements including create custom groups and get visual totals. Data Connection Wizard and Office Data Connection Files make it easy to connect to enterprise data stores and restore connections using a wizard driven connection interface. XML support allows users to load and save generic, well-formed, and spreadsheet XML in Excel. Because XML is an open format, that is widely used making it easy to share data across organizations and platforms. Excel PivotTables have an improved UI that make them easier to work with. SLIDE TRANSITION: It’s also easy to publish data to the web using Office Web Components. ADDITIONAL INFORMATION FOR PRESENTER: Office BI information:

23 Integrando BI con Office Office Web Components
Office Web Components brindan: Hojas de cálculo Gráficos Tablas y gráficos pivote Cree páginas web interactivas desde Excel! Extensible KEY MESSAGE: Explain that Office Web Components allow users to save interactive data analysis spreadsheets, charts, PivotTables, and PivotCharts directly to the web. SLIDE BUILDS: None SLIDE SCRIPT: Office Web Components, which are included in Office XP, are ActiveX controls that deliver the functionality of Excel spreadsheets, charts, PivotTables and PivotCharts to a web environment, such as a corporate intranet. These controls can be embedded in Web pages, portals, dashboards, and Web-based applications. Office Web Components include: Spreadsheets: The Spreadsheet Web Component supports Excel workbook features, including loading spreadsheet XML files and supporting named ranges, array formulas, multiple worksheets, wrapped text, and publishing entire workbooks with interactivity to the Web. The Spreadsheet Web Component now also supports all the built-in functions in Excel. Charts: The Chart Web Component now supports 3-D charts using the DirectX API and Office Art Fill Effects. It has full support for Excel PivotChart dynamic views features, supports graphing multiple charts within the same Web Component, and data-driven conditional formatting. Users can use fully customizable drawing and layout controls to create completely new chart types and custom annotations within their charts. PivotTables and PivotCharts: The PivotTable Web Component supports the creation and display of custom member properties and custom groups. It also provides improved filtering capabilities, so users can more effectively analyze their important data. Users can also make edits directly to the data, and commit edits directly back to the database. The pages can be interactive, and are created directly from Excel with no additional coding required. Web Components are extensible. The object model has been extended to display all built-in keyboard and user interface (UI) commands through the programming model. Developers can now create sophisticated macros within the Web Components by using only built-in commands in Excel. SLIDE TRANSITION: Let’s look at these features in Excel. ADDITIONAL INFORMATION FOR PRESENTER: BI for Excel:

24 Demostración 3 analizando datos de BI con Excel Importando datos Publicando datos
KEY MESSAGE: In this demonstration we’ll be analyzing business intelligence data with excel by importing the data, performing some basic analysis, then publishing the data. SLIDE BUILDS: SLIDE SCRIPT: In this demonstration we’ll be analyzing business intelligence data with excel by importing the data, performing some basic analysis, then publishing the data. SLIDE TRANSITION: Now lets move on to the next agenda item. ADDITIONAL INFORMATION FOR PRESENTER:

25 Agenda SQL Server 2000 Analysis Services Integrando BI con Office
Analizando datos con Data Analyzer KEY MESSAGE: Introduce the next topic. SLIDE BUILDS: None SLIDE SCRIPT: The next topic in the agenda is analyzing data with Microsoft Data Analyzer. SLIDE TRANSITION: The Data Analyzer a simple interface that provides robust analysis capabilities, let’s look at why a tool like this is needed. ADDITIONAL INFORMATION FOR PRESENTER:

26 Analizando datos con Data Analyzer La necesidad de herramientas de análisis simples
Tool Offerings Actual Tool Offerings TODAY Tipo de usuario Necesidades Herramienta robusta - Reportes - Calculos avanzados - Rankeo, ordenamientos MS Socios --ProClarity --Cognos --Business Objects --y mas… MS Socios --ProClarity --Cognos --Business Objects --y mas… 5%-10% 70%-80% 15%-20% KEY MESSAGE: Review the graph and explain that most users need simple analysis tools that provide simple data access, data visualization, and filter, sorting, reporting and publishing. SLIDE BUILDS: None SLIDE SCRIPT: There are three basic types of users that analyze data. Analysts are the smallest percentage, but have the most demanding requirements. They need very sophisticated query construction and reporting tools, the ability to do advanced calculations, ranking and forecasting. Most analysts fall into the category called information explorers. These are managers, team leads and so on. They only require, and really desire, simple analysis tools that provide a visual representation of data, with simple filtering and sorting options. They also need easy reporting and publishing so they can sharit their data. These people often use Excel. Another small group are categorized as information consumers. These people need access to reports to perform their jobs. They need to see the reports published by the information explorers. SLIDE TRANSITION: To address this need, Microsoft has published the Data Analyzer tool. ADDITIONAL INFORMATION FOR PRESENTER: Data Analyzer: Data Analyzer Guide: Session Resources\DAGuide.doc Analista Excel 2003 Data Analyzer --Visualización de datos --Análisis simples --Publicar a Excel, PowerPoint Explorador Herramientas simples --Visualización --Ordenamientos y filtros simples --Fácil publicación Excel 2003 --PivotTables --PivotCharts Consumidor Acceso a reportes Compartiendo .xls Simple “Salvar a web”

27 Analizando Datos con Data Analyzer Data Analyzer : Carácterísticas
Enfocado a exploradores y consumidores La mayoría de los usuarios! Conectividad simple a orígenes OLAP Vistas que identifican oportunidades, tendencias y mejoras Filtro/Ordenamiento Filtrar por criterios – aislar un miembro Filtro en reversa– compara los otros miembros Extiende y personaliza vistas Análisis guiado a través de Business Center Medidas plantilla KEY MESSAGE: Review the analysis capabilities of the Data Analyzer. SLIDE BUILDS: None SLIDE SCRIPT: The Data Analyzer targets Information Explorers and consumers. It offers rich visualization and analysis capabilities with an intuitive user interface (UI) and predefined queries. It offers easy connectivity to OLAP data sources on the local machine, or on the network. Once connected you create views to display the data you are interested in. The graphical user interface is designed to let users rapidly identify opportunities and trends, find business anomalies, and review multiple sets of data in one interface for better decision-making. The interface offers simple point-and-click filtering, sorting, including reverse filtering and filter by criteria capabilities. The interface can also display multiple measures, such as gross profit, and unit sales, or for displaying relationships among unlimited business dimensions, such as customer, region, and product. Views can be customized and extended as well. Data Analyzer offers guided analysis through the Business Center. Business Center reviews the current data and offers answers to common questions, about the data, and graphically displays analysis. Template measures are supported to help you identify key performance indicators and create custom template measures that correspond to your organization's key performance indicators. SLIDE TRANSITION: It’s very easy to share data with the Data Analyzer as well. ADDITIONAL INFORMATION FOR PRESENTER: Data Analyzer: Data Analyzer Guide: Session Resources\DAGuide.doc

28 Analyzing Data with Data Analyzer Data Analyzer : Conectividad e Integración
Conectividad HTTP Support MultiDimensional Expression (MDX) API Extensible Soporte XML Support para soluciones. Publicación robusta Publicar a Excel, PowerPoint Fácil compartir datos KEY MESSAGE: Review the connectivity and publishing capabilities in the Data Analyzer. SLIDE BUILDS: 6 SLIDE SCRIPT: [BUILD 1] The Microsoft Data Analyzer offers HTTP connectivity to connect to data sources anywhere on the web. [BUILD 2] Multidimensional Expressions are supported. MDX is a syntax that supports the definition and manipulation of multidimensional objects and data, such as OLAP cubes. That means complex queries can be constructed to retrieve data from data sources so they can be analyzed with the Data Analyzer. [BUILD 3] An extensible API allows developers to customize the Data Analyzer UI. [BUILD 4] Data Analyzer files, views, and template measures are saved as XML-formatted files. Business Center questions are also formatted in XML. Although detailed schemas are not included with in the API, developers can easily modify the XML files, or generate XML programmatically to create custom views or template measures and Business Center questions for a specific line of business. [BUILD 5] The Data Analyzer supports publishing to Excel, PowerPoint and the Web, making it is to share data and develop collaborative solutions. SLIDE TRANSITION: Now let’s look at the Data Analyzer ADDITIONAL INFORMATION FOR PRESENTER: Data Analyzer: Data Analyzer Guide: Session Resources\DAGuide.doc

29 Resumen de la sesión Analysis Services permite a los usuarios refinar los usuarios basados en sus necesidades de análisis Las aplicaciones de Office, pueden conectarse a orígenes OLAP y analizar datos de forma interactiva Data Analyzer ofrece una interfaz para análisis de datos rápido KEY MESSAGE: Review these key points the audience should remember from this session. SLIDE BUILDS: None SLIDE SCRIPT: The key points you should take away from this session are: Analysis Services allows users to refine data based on analysis needs. Office applications, can connect to OLAP data sources, and analyze data interactively. The Data Analyzer offers a quick, easy-to-use interface for data analysis. MapPoint provides Geo-Spatial analysis capabilities. SLIDE TRANSITION: For more information… ADDITIONAL INFORMATION FOR PRESENTER:

30 Mas información www.microsoft.com/office/business/intelligence
TechNet Web site Office Business Intelligence Web Site Office Web site KEY MESSAGE: For more information, see these web sites. SLIDE BUILDS: None SLIDE SCRIPT: For more information, see these web sites. TechNet Web site at: Office Business Intelligence Web Site at: Office Web site at: SLIDE TRANSITION: And these web sites… ADDITIONAL INFORMATION FOR PRESENTER:

31 Mas información Data Analyzer Web site Analysis Services Web site
Analysis Services Web site KEY MESSAGE: For more information, see these web sites. SLIDE BUILDS: None SLIDE SCRIPT: For more information, see these web sites. Data Analyzer Web site at: MapPoint Web site at: Analysis Services Web site at: SLIDE TRANSITION: You can get these MS Press books… ADDITIONAL INFORMATION FOR PRESENTER:

32 MS Press Para IT Professionals
Key Message: Talk about MS Press books and introduce the Build your own book feature SLIDE BUILDS: 1 SLIDE SCRIPT: [BUILD 1] The following books related to this topic are available from MSPress: Business Intelligence by Elizabeth Vitt, Michael Luckevich, Stacia Misner. SQL Server 2000 Analysis Services Step-by-step by OLAP Train and Reed Jacobson. Data Mining with Microsoft® SQL Server™ 2000 Technical Reference by Claude Seidman. SLIDE TRANSITION: Third party books available include… ADDITIONAL INFORMATION: Visite

33 Entrenamiento www.microsoft.com/traincert
Designing and Implementing OLAP Solutions with Microsoft® SQL Server™ 2000 Course Number: 2074 Availability: Current Detailed Syllabus: Implementing Business Logic with MDX in Microsoft SQL Server 2000 Course Number: 2093 Microsoft Training & Certification develops the courseware called Microsoft Official Curriculum (MOC), including MSDN Training courses. MOC offers comprehensive training courses for both IT professionals and developers who build, support, and implement solutions using Microsoft products and technologies. Please be sure to tell the audience that these training courses are related to the subject that was just covered in the slides, but they do not necessarily provide in-depth coverage of this exact subject as it may include other topics. Anyone interested in more information about the course(s) listed should visit the Microsoft Training & Certification Web site at and review the syllabus. All MOC courses are delivered by Microsoft’s premier training channel, Microsoft Certified Technical Education Centers and classes are taught by Microsoft Certified Trainers. Courses include: Course 2074: Designing and Implementing OLAP Solutions with Microsoft® SQL Server™ This course provides students with the knowledge and skills necessary to design, implement, and deploy OLAP solutions using Analysis Services. Course 2093: Implementing Business Logic with MDX in Microsoft SQL Server This course provides students with the knowledge and skills necessary to use MDX expressions to add calculated members and other dynamically calculated values to an Analysis Services cube. To locate a training provider, please access Microsoft Certified Technical Education Centers are Microsoft’s premier partners for training services

34