1. Business Intelligence
Lecture 15

2. Outlines Why integrate BI into your smart client application?
Integrating Data Mining into your application
Integrating Reporting into your application

3. What Is Business Intelligence?
“Business intelligence (BI) is a broad category of business processes, application software and other technologies for gathering, storing, analyzing, and providing access to data to help users make better business decisions”
Why integrate BI into your app?
BI makes applications smarter!
Your customers get more value from their data
Big payoff with small amounts of coding

4. Data mining life cycle

5. What Is SQL Server Data Mining?
Data Mining platform for analysis and deployment of data mining solutions
Nine algorithms developed in conjunction with Microsoft Research
Tools for the creation and comprehension of models
Familiar API’s and query language for embedding data mining into intelligent applications

6. What Does Data Mining Do? Illustrated
To Predict
Training Data
Mining Model
DB data Client data Application data
DB data
Client data
Application data
“Just one row”
DM Engine DM
Engine
Mining Model
Mining Model
Predicted Data

7. Intelligent Applications
Make decisions without coding
Data Mining algorithms learn business rules directly from the data, freeing you from trying discover and code them yourselves
Customized for each client
Data Mining learns the rules from the client’s data – resulting in logic that is automatically specialized for each individual client
Automatically update themselves
As your client’s business changes, so do the factors that impact their business. Data Mining allows your application logic to be automatically updated through a simple processing step. Applications do not need to be rewritten, recompiled or redeployed, and are always online – even during processing

8. Integrated Enterprise BI Platform
Information Delivery
Source Systems
Enterprise Reporting
Client
Portal
Data Marts
ERP
Business Performance Management
Familiar, Powerful BI Tools
Reporting & Analysis
CRM
Third Party Applications
LOB
Enterprise ETL
Data Warehouse
Data Analysis (OLAP, Data Mining)
Devices
Data Visualization
Geospatial
Project management
Business Analysis
Enterprise Reporting
Performance Management
Integrated Enterprise BI Platform

9. Integrate Analyze Report SQL Server 2005
Data acquisition from source systems and integration
Data transformation and synthesis
Data enrichment, with business logic, hierarchical views
Data discovery via data mining
Data presentation and distribution
Data access for the masses
SQL Server 2005 launches November 7, 2005

10. Example: before Integration Services
Alerts & escalation
Data mining
Call centre data: semi structured
Legacy data: binary files
Application database
Hand
coding
Staging
Text Mining
ETL
Cleansing
&
ETL
Staging
ETL
Warehouse
Reports
Mobile
data
Integration and warehousing require separate, staged, operations.
Preparation of data requires different, often incompatible, tools – difficult to audit.
Reporting and escalation is a slow process, delaying smart responses – sequential operations.
Heavy data volumes make this scenario increasingly unworkable.

11. Example: with Integration Services
Alerts & escalation
Warehouse
Reports
Mobile
data
SQL Server Integration Services
Text mining
components
Custom
source
Standard
sources
Data cleansing
Merges
Data mining
Call centre:
semi-structured data
Legacy data: binary files
Application database
Integration and warehousing are a seamless, manageable, operation.
Sourced, prepare and load data in a single, auditable process.
Reporting and escalation can be parallelized with the warehouse load.
Scales to handle heavy and complex data requirements.

12. Enterprise ETL platform
High performance
High scale
More trustworthy and reliable
Best in class usability
Rich development environment
Source control
Visual debugging of control flow and data
Great range of transforms out-of-the-box
Highly extensible
Custom tasks
Custom enumerations
Custom transformations
Custom data sources

14. Enterprise BI Today Data Source Data Model Tool data (DW) warehouse
MOLAP
Online Analytical
Processing (OLAP)
Browser (1)
MOLAP
OLAP
Browser (2)
Datamart
Reporting
Tool (3)
Reporting
Tool (2)
data
warehouse
(DW)
Reporting
Tool (1)

15. Enterprise BI – A Messy Reality
Data Source
Data Model
Tool
Duplicate
Data
Duplicate
Models
MOLAP
OLAP
Browser (2)
Browser (1)
MOLAP
OLAP
versus
Reporting
Datamart
Reporting
Tool (3)
Reporting
Tool (2) DW
Reporting
Tool (1)

16. Relational Versus OLAP Reports
Feature
Relational
OLAP
Flexible schema
Real time data access
Single data store
Simple management
Detail reporting
High performance
End-user oriented
Ease of navigation and exploration
Rich analytics
Rich semantics

17. Enterprise BI – With A UDM
Data Source
Data Model
Tool
MOLAP
OLAP
Browser (2)
Browser (1)
MOLAP
Datamart
UDM
Reporting
Tool (2)
Reporting
Tool (1) DW
BI Applications

18. Enterprise BI – With A UDM
Data Source
Data Model
Tool
MOLAP
OLAP
Browser (2)
Browser (1)
MOLAP
UDM
Datamart
Reporting
Tool (2)
Reporting
Tool (1) DW
BI Applications
A single dimensional model for all OLAP analysis and
Relational reporting needs

19. Analysis Services Unified Dimensional Model
Integrating relational and OLAP views
Advanced Business Intelligence
KPIs, MDX scripts, translations, currency…
Web services
Native XML/A
Critical Server Abilities
Pro-active caching
Bringing the best of MOLAP to ROLAP

21. Value of Data Mining Business Knowledge Relative Business Value
Easy Difficult
Usability
Relative Business Value
SQL Server 2005
Data Mining
OLAP
Reports (Adhoc)
Reports (Static)
5 new algorithms
Graphical tools/wizards
12 embeddable viewers
Tightly integrated with AS, DTS, Reporting
Integration with Web/Office apps

22. New Set of Algorithms Decision Trees Clustering Time Series
Introduced in SQL Server 2000
Sequence Clustering
Association
Naïve Bayes
All data mining tools, including Microsoft SQL Server “Yukon” Analysis Services, use multiple algorithms. Analysis Services, of course, is extensible; third party ISVs can develop algorithms that snap in seamlessly to the Analysis Services data mining framework. Depending on the data and the goals, different algorithms are preferred, and each algorithm can be used for multiple problems.
Analytical problem Examples Microsoft algorithms
Classification: Assign cases to predefined Decision Trees Naďve Bayes
Nueral Nets
classes such as "Good" vs "Bad“
Credit risk analysis
Churn analysis
Customer retention
Neural Net

23. Mining Model Algorithms
Microsoft Decision Trees
Microsoft Clustering
Microsoft Naïve Bayes
Microsoft Time Series
Microsoft Association
Microsoft Sequence Clustering
Microsoft Neural Network
Microsoft Linear Regression
Microsoft Logistic Regression

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

25. Programmatically Creating Models
Analysis Management Objects
Object model for administrative tasks
Data Mining Extensions (DMX)
CREATE MINING MODEL TargetMailDT (CustID LONG KEY, Gender TEXT DISCRETE, CommuteDist TEXT DISCRETE, Education LONG CONTINUOUS, … BikeBuyer LONG DISCRETE PREDICT ) USING Microsoft_Decision_Trees
INSERT INTO TargetMailDT (CustID, Gender, CommuteDist, Education, …, BikeBuyer) OPENQUERY ([My Data Source], ‘SELECT CustID, Gender, ComDist, Education, … BikeBuyer’ )

26. Programmatically Creating Models
Analysis Management Objects
Object model for administrative tasks
Data Mining Extensions (DMX)
Dynamic mining using client data
CREATE SESSION MINING MODEL TargetMailDT (CustID LONG KEY, Gender TEXT DISCRETE, CommuteDist TEXT DISCRETE, Education LONG CONTINUOUS, … BikeBuyer LONG DISCRETE PREDICT ) USING Microsoft_Decision_Trees
INSERT INTO TargetMailDT (CustID, Gender, CommuteDist, Education, …,

27. Local Mining Architecture
Your Application
App
Data
Retrieve Data
Data
Source
ADOMD.Net/OLE DB
Local Analysis Services (msmdlocal)
Model File
Mining Model
Decision Tree/Clustering algorithms

28. Why Integrate Reporting Services Into Your App?
Process data efficiently: filtering, sorting, grouping, aggregations, etc.
Present data in a variety of ways: lists, tables, charts, matrices
Add visual appeal to data: fonts, colors, border styles, background images, etc.
Enable interactive reports: collapsible sections, document map, bookmarks, interactive sorting, etc.
Support printing and print preview
Export to multiple formats (Microsoft Office Excel, PDF, XML)
Integrate with managed report server environment: security, caching, scheduling, delivery, etc.
Bottom Line: More functionality, less code

29. Reporting Services In Visual Studio 2005
Microsoft Visual Studio 2005 includes Reporting Services functionality in standard editions and above
Window Forms and ASP.NET report viewer controls make it easy to embed Reporting Services functionality in applications
Integrated report designer for designing and embedding reports in language projects
Freely redistributable with no run-time restrictions
Report Server mode requires a SQL Server 2005 license

30. Report Server Mode Publish Report Report Server
Report Designer
(Visual Studio)
Your Application
Report Viewer (Remote Mode)
Report Server
SQL Server Catalog (Metadata)
Web Services API
Report Processing and Rendering
Data Source

31. Report Viewer (Local Mode)
Your Application
Report Viewer (Local Mode)
Report Processing
Report Definition
Report Designer
(Visual Studio)
Embed
Report
Retrieve Data
Report Data
Data Source

32. Designing In Local Mode
Report creation is integrated with Windows or Web language project (Add New Item…)
Integrates with Visual Studio data tools
Build report from project data sources (XSD)
Supports Data Sources window
Supports Data Source Configuration Wizard
Supports TableAdapter Configuration Wizard
Full Interactivity
Expand/Collapse sections
Drillthrough reports, including drillthrough from charts
Interactive sorting

33. Reporting Services Introduced with SQL Server 2000
Open, extensible enterprise reporting solution
Report authoring, management, delivery
Office System integration
VS.NET development environment
SQL Server 2005 enhancements
Integration with AS, DTS, management tools
Developer enhancements
Improved report interactivity
Rich end-user reporting

34. Reporting Manager

35. Rich Client Printing
Multi-Valued Parameters
Date Picker Parameters
Sorting

36. Export to Excel, PDF
Delivery

37. Graphical Report Layout

38. Report Builder A new ad-hoc report design tool for Reporting Services
Targeted at business users who want to find and share answers to interesting questions
Driven from a business model of the data so users do not need to understand the underlying data structures
Fully integrated with Reporting Services and delivered in SQL Server 2005

39. Report Builder Client
Users download and launch the client from Report Manager (new or existing report)
Finished reports can be saved on the server

40. Report Builder Model Designer
For editing models used by Report Builder
Auto-generation of models from data sources

41. Application Examples Healthcare Marketing Traffic
Builds models predicting health degradation in citizens
Sends reports to physicians of top patients likely to get sick
Alert Governments with predictable decease
Marketing
Sales Forecast
Targeting client
Traffic
Real-time predictive traffic info delivered to web and mobile devices
Everything…………………………….

42. Resources msdn.microsoft.com/sql SQL Server Data Mining
SQL Server Forums