This document discusses business analytics using data science techniques. It provides an overview of topics like business intelligence, analytics, data mining, and the differences between data, information and knowledge. It also discusses database models, migrating data, visualization techniques, geographic information systems, web analytics, and the Pentaho business analytics platform. Pentaho supports features like dashboards, interactive reports, analysis, predictive modeling, and integrates with big data platforms like Hadoop and NoSQL databases.

1. Course : ISYE8015_Selected Topic in
Industrial Engineering
Period : June 2020
Business analytics using data
science techniques

2. Topic
1. What is business analytics ?
2. Data preparation for business analytics
3. Business analytics and intelligence application
framework

3. Business Intelligence and
Analytic
• Business intelligence
– Acquisition of data and information for use
in decision-making activities
• Business analytics
– Models and solution methods
• Data mining
– Applying models and methods to data to
identify patterns and trends

4. Data, Information, Knowledge
• Data
– Items that are the most elementary descriptions of
things, events, activities, and transactions
– May be internal or external
• Information
– Organized data that has meaning and value
• Knowledge
– Processed data or information that conveys
understanding or learning applicable to a problem or
activity

5. Data problems

6. Database Model
• Hierarchical
– Top down, like inverted tree
– Fields have only one “parent”, each “parent” can have multiple
“children”
– Fast
• Network
– Relationships created through linked lists, using pointers
– “Children” can have multiple “parents”
– Greater flexibility, substantial overhead
• Relational
– Flat, two-dimensional tables with multiple access queries
– Examines relations between multiple tables
– Flexible, quick, and extendable with data independence
• Object oriented
– Data analyzed at conceptual level
– Inheritance, abstraction, encapsulation

8. Migrating Data
• Business rules
– Stored in metadata repository
– Applied to data warehouse centrally
• Data extracted from all relevant sources
– Loaded through data-transformation tools or
programs
– Separate operation and decision support
environments
• Correct problems in quality before data
stored
– Cleanse and organize in consistent manner

9. Business Analytics and
intelligence to support
visualization
• Technologies supporting visualization and
interpretation
– Digital imaging, GIS, GUI, tables,
multidimensions, graphs, VR, 3D, animation
– Identify relationships and trends
• Data manipulation allows real time look at
performance data

10. Data Analytic System
• Real-time queries and analysis
• Real-time decision-making
• Real-time data warehouses updated
daily or more frequently
–Updates may be made while queries
are active
–Not all data updated continuously
• Deployment of business analytic
applications

11. Business Analytics : GIS
• Computerized system for managing and
manipulating data with digitized maps
– Geographically oriented
– Geographic spreadsheet for models
– Software allows web access to maps
– Used for modeling and simulations

13. Business Analytics: Web
• Web analytics
– Application of business analytics to Web
sites
• Web intelligence
– Application of business intelligence
techniques to Web sites

14. Business Analytics and Intelligence
Application using Pentaho

15. About Pentaho
• Recognized leader in business analytics & data integration
• Subscription-based business model
• Achieved critical mass:
• Over 1,200 commercial customers
• Over 10,000 production deployments
• Over 185 countries
• Stewardship of most important open source analytics
projects
INDUSTRY RECOGNITION OVER 160 PARTNERS GLOBALLY

16. Pentaho for Big Data Analytic
Big
Data
Mgmt
Hadoop
Java MapReduce, Pig
Pentaho MapReduce
NoSQL Databases Analytic Databases
Data Integration
Job Orchestration
Workflow
Scheduling
High Performance
Visual IDE
Data
Integration
Pentaho Business Analytics
•
R
•
3rd Party BI Tools
•
Applications
3rd Party Tools
Big
Analytics

17. Business Analytic Model
Advanced
Power Users
& Viewers
Data Science
Information
Consumers
Dashboards
Knowledge
Workers/
Business
Users
Analysis
Business
Users
Reporting
Power Users,
Developers &
DBAs
Data
Advanced
Predictive
Analysis
Self-service Interactive
KPI & Metrics and
Visualization
Self-service Interactive and
Ad Hoc Analysis
Ad hoc and
Operational
Reports
High Performance Data Integration,
BIG DATA, Cleansing
and Presentation
Components
are
independent

18. High Level Feature/Functions
Advanced
Power Users
& Viewers
Data Science
Information
Consumers
Dashboards
Knowledge
Workers/
Business
Users
Analysis
Business
Users
Reporting
Power Users,
Developers &
DBAs
Data
Advanced
Predictive
Analysis
Self-service Interactive
KPI & Metrics and
Visualization
Self-service Interactive and
Ad Hoc Analysis
Ad hoc and
Operational
Reports
High Performance Data Integration,
BIG DATA, Cleansing
and Presentation

19. Example for Dashboards

20. Dashboards & Interactive Dashboards
for Business Analytics

21. Dashboards – Geo Location-Based

22. High Level Feature/Functions
Advanced
Power Users
& Viewers
Data Mining
Information
Consumers
Dashboards
Knowledge
Workers/
Business
Users
Analysis
Business
Users
Reporting
Power Users,
Developers &
DBAs
Data
Advanced
Predictive
Analysis
Self-service Interactive
KPI & Metrics and
Visualization
Self-service Interactive and
Ad Hoc Analysis
Ad hoc and
Operational
Reports
High Performance Data Integration,
BIG DATA, Cleansing
and Presentation

23. Reports – Interactive, Static, Distributed

24. Reports – Reporting Pack & House Styles

25. Reports – Reporting Pack & House Styles

26. High Level Feature/Functions
Advanced
Power Users
& Viewers
Data Science
Information
Consumers
Dashboards
Knowledge
Workers/
Business
Users
Analysis
Business
Users
Reporting
Power Users,
Developers &
DBAs
Data
Advanced
Predictive
Analysis
Self-service Interactive
KPI & Metrics and
Visualization
Self-service Interactive and
Ad Hoc Analysis
Ad hoc and
Operational
Reports
High Performance Data Integration,
BIG DATA, Cleansing
and Presentation

27. Enhanced In-Memory Analytics
• Enhanced in-memory caching for speed of
thought visualization & analysis
– More re-usability of in-memory data
– Fewer trips to the database/disk
• Builds on existing unique extreme-scale in-
memory analytics
– Support for external data grids
• Infinispan / JBoss Enteprise Data Grid
and Memcached
• Scale to caching hundreds of GBs
(potentially TBs) of data in-memory
• Competition
– Java heap or C++ memory space (a few GB
at most (most BI products)
or
– Proprietary (hard to manage) in-memory
technology (e.g. Qlikview, Microstrategy)

28. Analyzer – Table format

29. Analyzer – Chart format

30. Analyzer: Geo Location-Based Analysis

31. High Level Feature/Functions
Advanced
Power Users
& Viewers
Data Science
Information
Consumers
Dashboards
Knowledge
Workers/
Business
Users
Analysis
Business
Users
Reporting
Power Users,
Developers &
DBAs
Data
Advanced
Predictive
Analysis
Self-service Interactive
KPI & Metrics and
Visualization
Self-service Interactive and
Ad Hoc Analysis
Ad hoc and
Operational
Reports
High Performance Data Integration,
BIG DATA, Cleansing
and Presentation

32. “Traditional” Database Support
DATA INTEGRATION
DATA ANALYSIS

33. Broadest Support for Big Data
Platforms
Hadoop NoSQL Analytic Databases