Business intelligence (BI) is a set of theories, methodologies, architectures, and technologies that transform raw data into meaningful and useful information for business purposes.

1. Business Intelligence
overview
1

2. What is BI?
Business intelligence (BI) is a set of theories, methodologies,
architectures, and technologies that transform raw data into
meaningful and useful information for business purposes.
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3. 3
Different systems
3
Data repository
Reports
Business Intelligence

4. reporting
4
data discovery capabilities

5. 5
Information Delivery
Reporting
Dashboards
Ad hoc report/query
Microsoft Office integration
Mobile BI
Analysis
Interactive visualization
Search-based data discovery
Geospatial and location intelligence
Embedded advanced analytics
Online analytical processing (OLAP)
Integration
BI infrastructure and administration
Metadata management
Business user data mashup and modeling
Development toolsEmbeddable analytics
Collaboration
Support for big data sources
BI and analytics
(Gartner, 2014)

6. • Gartner is the world's leading information technology research and
advisory company.
“We deliver the technology-related insight necessary for our clients to
make the right decisions, every day”
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7. 7

8. Business intelligence and analytics vendors:
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LEADERSCHALLENGERS
NICHE PLAYERS VISIONARIES
Magic
Quadrant
report for 2014
(Gartner, feb 2014)
http://www.gartner.com/technology/reprints.do?id=1-1QLGACN&ct=140210&st=sb

9. Business management issues
• “We have mountains of data in this company, but we
can’t access it.”
• “We need to slice and dice the data every which way.”
• “You’ve got to make it easy for business people to get at
the data directly.”
• “Just show me what is important.”
• “It drives me crazy to have two people present the same
business metrics
• at a meeting, but with different numbers.”
• “We want people to use information to support more
fact-based decision making.”
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10. Data Warehouse
• The data warehouse:
– must make an organization’s information easily accessible
– must present the organization’s information consistently
– must be adaptive and resilient to change
– must be a secure bastion that protects our information assets
– must serve as the foundation for improved decision making
– the business community must accept the data warehouse if it
is to be deemed successful
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11. Basic Elements of the Data Warehouse
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Ralph Kimball, Margy Ross, The Data Warehouse Toolkit, 2nd Edition, 2002

12. Operational Source Systems
• capture the transactions of the business
• queries against source systems are narrow
• stovepipe application
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13. Data Staging Area
• a storage area
AND
• a set of ETL processes
(extract-transform-load)
• it is off-limits to business users and does not
provide query and presentation services.
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14. Data Staging Area - ETL
• EXTRACTION
– reading and understanding the source data and
copying the data needed for the data warehouse
into the staging area for further manipulation.
• TRANSFORMATION
– cleansing, combining data from multiple sources,
deduplicating data, and assigning warehouse keys
• LOADING
– loading the data into the data warehouse
presentation area
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15. Data Presentation Area
• where data is organized, stored and made available for
direct querying by users, report writers, and other
analytical applications
• it is all the business community sees and touches via data
access tools
• dimensional data modeling
– user understandability
– query performance
– resilience to change
• detailed, atomic data
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16. Data Access Tools
• tools that query the data in the data
warehouse’s presentation area
• the variety of capabilities that can be provided
to business users to leverage the presentation
area for analytic decision making.
– prebuilt parameter-driven analytic applications
– ad hoc query tools
– data mining, modeling, forecasting
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17. Microsoft SQL Server
• SQL Server Integration Services (SSIS)
– tool for the ETL process
• SQL Server Analysis Services (SSAS)
– tool for multidimensional modeling
• SQL Server Reporting Services (SSRS)
– tool for reporting
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18. What BI technologies will be the most important to
your organization in the next 3 years?
1. Predictive Analytics
2. Visualization/Dashboards
3. Master Data Management
4. The Cloud
5. Analytic Databases
6. Mobile BI
7. Open Source
8. Text Analytics
TDWI Executive Summit – August 2010

19. Advanced Analytics / Predictive Analytics
• Data Mining
• Regression
• Monte Carlo Simulation
• “Statistically Significant”
• Predicting Customer Behavior
– Churn/Attrition
– Purchases
– Profiling

20. BI Today vs Tomorrow
• “BI today is like reading the newspaper”
– BI reporting tool on top of a data warehouse that
loads nightly and produces historical reporting
• BI tomorrow will focus more on real-time
events and predicting tomorrow’s headlines

21. Collegiate Admissions Criteria
• Test Scores: SAT, ACT, AP Exams
• Grade Point Average
• Class Rank
• High School “Strength”
• Extracurricular Activities: Band/Choir, Clubs, Sports
• Non-School Activities: Work, Volunteer, Community Groups
• Area of Focus – Intended Major
• Family legacy
• Home State or Country
Regression Outcome = Graduation (binary) + GPA (linear)
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22. Retail Analytics
• Market Basket Analytics
• Text Analytics
• Customer Segmentation/Clustering
• Tailored Product Assortments
• Inventory Forecasting

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Amazon.com and NetFlix
Collaborative Filtering tries to predict other items a
customer may want to purchase based on what’s in their
shopping cart and the purchasing behaviors of other
customers

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What Is Text Analytics?
…turning unstructured customer comments into actionable
insights
…finding nuggets of insight in text data that will improve our
business
From Wikipedia:
… a set of linguistic, statistical, and machine learning
techniques that model and structure the information
content of textual sources for business intelligence,
exploratory data analysis, research, or investigation

25. 25
Customer Sat
Survey
Comments
Unstructured Text Processing
Facebook
Page
Blogs
Competitors’
Facebook
Pages
Public Web Sites,
Discussion Boards,
Product Reviews
Alerts,
Real-time
Action
Twitter
Page
Services
Quality Cost Friendliness
Email
Adhoc
Feedback
Call
Center
Notes,
Voice

29. What is Information Governance?
Information Governance
•Data Stewardship
•Data Quality
•Data Governance
•Master Data Management
•Data Stewards for Master Data “Hubs”
•Customer, Vendor, Product, Location, Employee, G/L Accounts
PREVENTS
Garbage
In
Garbage
Out
BY
ENCOMPASSING
•Report Governance
•Metric Governance
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CREATING SIGNIFICANT
BUSINESS VALUE

30. BI Technologies
•Analytic Databases
•BI is a consolidating industry
– Oracle: Siebel, Hyperion, Brio, Sun
– SAP: Business Objects, Sybase
– IBM: Cognos, SPSS, Coremetrics, Unica, Netezza
– EMC: Greenplum
– HP: Vertica
– Teradata: Aster Data
•Independent vendors: MicroStrategy, Informatica, SAS
•Reporting standards determined mainly by Microsoft, Apple and
Adobe
Teradata
Netezza
DB2
Oracle
SQL Server
Vertica
Aster Data
Par Accel
Greenplum
Semantic Databases
(TIDE)

31. BI Technologies (cont’d)
•If you want to learn more about Analytic Databases:
http://hosted.mediasite.com/mediasite/Viewer/?peid=120d6b7
ba227498b96a8c0cd01349a791d
•If you want to learn more about BI in the Cloud:
http://hosted.mediasite.com/mediasite/Viewer/?peid=e6d9114
8a71a47969824c22b3b20d6221d