BI 2008 Simple

Business Intelligence
Lynn Langit/MSDN Developer Evangelist Southern California
http://blogs.msdn.com/SoCalDevGal

Session Prerequisites – Session One
Working SQL Server 2005 Developer –OR-
Working .Net developers

Session One Objectives and Agenda
Understand what, why, when & how of SQL Server 2008 Business Intelligence
Examine the core functionality SSAS
OLAP cubes and Data Mining Models

What and Why BI? Presentation Exploration Discovery Passive Interactive Proactive Role of Software Business Insight Predictive Analytics Canned reporting Ad-hoc reporting OLAP Data mining

SQL Server 2008 BI & Tools
OLTP – SQL Server Engine
SSMS / Profiler and other mgmt tools
Reporting – SSRS
No need for IIS, BIDS / Report Manager to design
Integrates with SharePoint
ETL – SSIS
Part of SQL Server, BIDS to design
OLAP – SSAS
Multidimensional Cubes, BIDS / SSMS
Data Mining – SSAS
Algorithm-based models – BIDS / Excel / SSMS

Why BI?
Faster reports
OLAP can be 1,000% faster
Flexible
click to query using pivot tables, add calculated members, create custom views
Proactive
‘ discover’ patterns in data, ‘predict’ future
Reduce load
on OLTP source systems
Scalable
no manual index tuning, data de-normalization

SQL Server 2008 Languages
OLTP – SQL Server Engine
T-SQL, .NET (CLR), XML
Reporting – SSRS
RDL + queries
ETL – SSIS
XMLA metadata + queries, .NET extendable
OLAP – SSAS
MDX, XMLA
Data Mining – SSAS
DMX, XMLA, PMML

Cubes vs. Data Mining

Where do I start?
Understand OLAP modeling
Star schema + grain statements
Review AdventureWorks DW sample
From www.CodePlex.com
Realistically access source data quality
Plan for ETL, learn SSIS
Leverage Excel
Light-weight data mining designer and client
OLAP cube pivot table client

BI baked into the MS platform Enterprise – Performance Point Individual – Excel Project – Share Point

But, I want to build my own app… Don’t under-estimate the learning curve Embed extended controls Consider buying extendable components

Microsoft’s Predictive Analytics Data Mining SQL extensions (DMX) Application Developer Data Mining Specialist Microsoft Dynamics CRM Analytics Foundation SQL Server 2005 Business Intelligence Development Studio Microsoft SQL Server 2008 Analysis Services Information Worker Data Mining Add-ins for the 2007 Microsoft Office system Microsoft SQL Server 2008 Data Mining BI Analyst Custom Algorithms

Demo 1 – SSAS Cubes

Data Mining Add-ins for Office 2007 Table Analysis Tools for Excel 2007 Data Mining Template for Visio 2007 Data Mining Client for Excel 2007 Information Worker BI Analyst Data Mining Specialist

Demo – Data Mining

Microsoft Data Mining Lifecycle CRISP-DM SSAS (Data Mining) Excel SSAS (DSV) Query Excel SSIS SSAS SSRS Excel Your Apps SSIS SSAS Excel Data www.crisp-dm.org Business Understanding Data Understanding Data Preparation Modeling Evaluation Deployment

DM - From Scenarios to Tasks

From Tasks to Techniques

Understand & Prepare specifics

Modeling Specifics

New to SQL Server 2008
Microsoft Time Series algorithm improved
ARIMA plus ARTxp method, and a blending algorithm = better results
New prediction mode allows adding new data to time series models
Holdout Support added
Easily partition data into training and test sets that are stored in mining structure & available to query after processing
Ability to build mining models based on filtered subsets added
Results in less structures, i.e. can just filter existing
Drillthrough functionality extended
makes all mining structure columns available, not just columns included in the model
allows you to build more compact models
Cross-validation added
allows users to quickly validate their modeling approach by automatically building temporary models and evaluating accuracy measures across K folds. The feature is available through a new cross-validation tab under Accuracy Charts in BIDS, in addition to being accessible programmatically via a stored procedure call.

Summary
Data Mining in SQL Server 2008 is mature, powerful and accessible
Can use Excel 2007
Familiar client for BI – OLAP cubes AND Data Mining models
Model Creators / Users
Excel Data or Server Data
SSAS and Excel both support the full DM Cycle
Data Understanding
Data Preparation
Modeling
Validation
Deployment

DM Webcasts Fri, 02 Nov 2007 MSDN Webcast: Build Smart Web Applications with SQL Server Data Mining (Level 200) Thu, 08 Nov 2007 MSDN Webcast: Building Adaptive Applications with SQL Server Data Mining (Level 300) Mon, 19 Nov 2007 MSDN Webcast: Extending and Customizing SQL Server Data Mining (Level 300) Fri, 30 Nov 2007 MSDN Webcast: Creating Visualizations for SQL Server Data Mining (Level 300) Thu, 01 Nov 2007 TechNet Webcast: Deliver Actionable Insight Throughout Your Organization with Data Mining (Part 1 of 3): Your First Project with SQL Server Data Mining (Level 200) Thu, 15 Nov 2007 TechNet Webcast: Deliver Actionable Insight Throughout Your Organization with Data Mining (Part 2 of 3): Understand SQL Server Data Mining Add-ins for the 2007 Office System (Level 200) Thu, 29 Nov 2007 TechNet Webcast: Deliver Actionable Insight Throughout Your Organization with Data Mining (Part 3 of 3): Use Predictive Intelligence to Create Smarter KPIs (Level 200)

BI Resources from Lynn Langit 1. “Foundations of SQL Server 2005 Business Intelligence” (published by APress in April 2007) 2. http://blogs.msdn.com/SoCalDevGal 3. “Building Business Intelligence Solutions with SQL Server 2008” (MSPress Fall 2008)

DM Resources Technical Communities, Webcasts, Blogs, Chats & User Groups http://www.microsoft.com/communities/default.mspx Microsoft Developer Network (MSDN) & TechNet http://microsoft.com/msdn http://microsoft.com/technet Trial Software and Virtual Labs http://www.microsoft.com/technet/downloads/trials/default.mspx Microsoft Learning and Certification http://www.microsoft.com/learning/default.mspx SQL Server Data Mining http://www.sqlserverdatamining.com http://www.microsoft.com/bi/bicapabilities/data-mining.aspx

END – END - END
End of first set

Business Intelligence
Lynn Langit/MSDN Developer Evangelist Southern California
http://blogs.msdn.com/SoCalDevGal

Session Prerequisites – Session Two
Working SQL Server 2005 Developer
Understanding of OLAP concepts
Working SQL Server Analysis Server 2005 Developer
Interest in or basic knowledge of Data Mining concepts

Session Two Objectives and Agenda
Understand what’s new SQL Server 2008 Business Intelligence
SSAS OLAP cubes
SSAS Data Mining Structures

Demo – Simplified Cube / Dim Wizards

Demo – New Aggregation Designer

Data Mining
Are you using it now?

Data Mining – Logical Model algorithm Mining Model Mining Model Training Data DB data Client data Application data Data Mining Engine To Predict Predicted Data Mining Model DB data Client data Application data “ Just one row ” Data Mining Engine

Evaluation Specifics

Data Mining - Physical Model Analysis Services Server Mining Model Data Mining Algorithm Data Source Your Application OLE DB/ ADOMD/ XMLA Deploy BI Dev Studio (Visual Studio) App Data

Data Mining Interfaces – APIs XMLA Over TCP/IP XMLA Over HTTP Analysis Server (msmdsrv.exe) OLAP Data Mining Server ADOMD.NET .Net Stored Procedures Microsoft Algorithms Third Party Algorithms OLEDB for OLAP/DM ADO/DSO Any Platform, Any Device C++ App VB App .Net App AMO Any App ADOMD.NET WAN DM Interfaces

Configuration
Model Creation/Management
Database Administrators
Session Mining Models
Model Application
Permissions on models
Permissions on data sources

Deployment
Browse
Copy to Excel
Drillthrough
Query
Default
Advanced
Excel Services
Manage models and structures
Export/Import
Rename
Connection
Database
Trace

Excel Functions*
DMPREDICTTABLEROW ( Connection, ModelName, PredictionResult, TableRowRange [, string CommaSeparatedColumnNames] )
DMPREDICT ( Connection, Model, PredictionResult, Value1, Name1, [...,Value32, Name32] )
DMCONTENTQUERY (Connection, Model, PredictionResult [, WhereClause])

Data Mining Extensions (DMX) CREATE MINING MODEL CreditRisk (CustID LONG KEY, Gender TEXT DISCRETE, Income LONG CONTINUOUS, Profession TEXT DISCRETE, Risk TEXT DISCRETE PREDICT) USING Microsoft_Decision_Trees INSERT INTO CreditRisk (CustId, Gender, Income, Profession, Risk) Select CustomerID, Gender, Income, Profession,Risk From Customers Select NewCustomers.CustomerID, CreditRisk.Risk, PredictProbability(CreditRisk.Risk) FROM CreditRisk PREDICTION JOIN NewCustomers ON CreditRisk.Gender=NewCustomer.Gender AND CreditRisk.Income=NewCustomer.Income AND CreditRisk.Profession=NewCustomer.Profession

DMX Column Expressions
Predictable Columns
Source Data Columns
Functions - Predict
“ Workhorse”
Discrete scalar values
Continuous scalar values
Associative nested tables
Sequence nested tables
Time Series
Overloaded to
PredictAssociation
PredictSequence
PredictTimeSeries
PredictProbability
PredictSupport
PredictHistogram
Cluster
ClusterProbability
GetNodeId
IsInNode
Arithmetic operators
Stored Procedure
Subselect
Select from nested tables

Data Mining Interfaces – XMLA ++ XMLA Over TCP/IP XMLA Over HTTP Analysis Server (msmdsrv.exe) OLAP Data Mining Server ADOMD.NET .Net Stored Procedures Microsoft Algorithms Third Party Algorithms OLEDB for OLAP/DM ADO/DSO Any Platform, Any Device C++ App VB App .Net App AMO Any App ADOMD.NET WAN DM Interfaces

New to SQL Server 2008
Microsoft Time Series algorithm improved
ARIMA plus ARTxp method, and a blending algorithm = better results
New prediction mode allows adding new data to time series models
Holdout Support added
Easily partition data into training and test sets that are stored in mining structure & available to query after processing
Ability to build mining models based on filtered subsets added
Results in less structures, i.e. can just filter existing
Drillthrough functionality extended
makes all mining structure columns available, not just columns included in the model
allows you to build more compact models
Cross-validation added
allows users to quickly validate their modeling approach by automatically building temporary models and evaluating accuracy measures across K folds. The feature is available through a new cross-validation tab under Accuracy Charts in BIDS, in addition to being accessible programmatically via a stored procedure call.

Summary
Data Mining in SQL Server 2008 is mature, powerful and accessible
Can use Excel 2007
Familiar client for BI – OLAP cubes AND Data Mining models
Model Creators / Users
Excel Data or Server Data
SSAS and Excel both support the full DM Cycle
Data Understanding
Data Preparation
Modeling
Validation
Deployment

DM Webcasts Fri, 02 Nov 2007 MSDN Webcast: Build Smart Web Applications with SQL Server Data Mining (Level 200) Thu, 08 Nov 2007 MSDN Webcast: Building Adaptive Applications with SQL Server Data Mining (Level 300) Mon, 19 Nov 2007 MSDN Webcast: Extending and Customizing SQL Server Data Mining (Level 300) Fri, 30 Nov 2007 MSDN Webcast: Creating Visualizations for SQL Server Data Mining (Level 300) Thu, 01 Nov 2007 TechNet Webcast: Deliver Actionable Insight Throughout Your Organization with Data Mining (Part 1 of 3): Your First Project with SQL Server Data Mining (Level 200) Thu, 15 Nov 2007 TechNet Webcast: Deliver Actionable Insight Throughout Your Organization with Data Mining (Part 2 of 3): Understand SQL Server Data Mining Add-ins for the 2007 Office System (Level 200) Thu, 29 Nov 2007 TechNet Webcast: Deliver Actionable Insight Throughout Your Organization with Data Mining (Part 3 of 3): Use Predictive Intelligence to Create Smarter KPIs (Level 200)

BI Resources from Lynn Langit 1. “Foundations of SQL Server 2005 Business Intelligence” (published by APress in April 2007) 2. http://blogs.msdn.com/SoCalDevGal 3. “Building Business Intelligence Solutions with SQL Server 2008” (MSPress Fall 2008)

DM Resources Technical Communities, Webcasts, Blogs, Chats & User Groups http://www.microsoft.com/communities/default.mspx Microsoft Developer Network (MSDN) & TechNet http://microsoft.com/msdn http://microsoft.com/technet Trial Software and Virtual Labs http://www.microsoft.com/technet/downloads/trials/default.mspx Microsoft Learning and Certification http://www.microsoft.com/learning/default.mspx SQL Server Data Mining http://www.sqlserverdatamining.com http://www.microsoft.com/bi/bicapabilities/data-mining.aspx

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BI 2008 Simple

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