Loading presentation...

Present Remotely

Send the link below via email or IM

Copy

Present to your audience

Start remote presentation

  • Invited audience members will follow you as you navigate and present
  • People invited to a presentation do not need a Prezi account
  • This link expires 10 minutes after you close the presentation
  • A maximum of 30 users can follow your presentation
  • Learn more about this feature in our knowledge base article

Do you really want to delete this prezi?

Neither you, nor the coeditors you shared it with will be able to recover it again.

DeleteCancel

Make your likes visible on Facebook?

Connect your Facebook account to Prezi and let your likes appear on your timeline.
You can change this under Settings & Account at any time.

No, thanks

Business Analytics: Techniques and Applications

Data Analysis, Business Intelligence, Storage, Mining, OLAP, Star Schema
by

jim devaney

on 26 April 2013

Comments (0)

Please log in to add your comment.

Report abuse

Transcript of Business Analytics: Techniques and Applications

Online Analytical Processing OLAP Wrap-up Data mining Putting it all in context Data warehouse Data warehouse Business Intelligence and Analytics Aaron S Business Intelligence and Analytics Aaron S ITM 782
Database Management Systems James Devaney
John Curtin
Jeremy Sarich
James Pfotenhauer
Aaron Seidman Creighton University
Spring 2013 John C Data mining Impact Reverse Standard Business Intelligence Pattern Uses Data mining Cost Data mining Design Data Mining BIA Business Intelligence and Analytics BIA BIA BIA Data warehouse Need * Reporting

* Decision Making

* Understanding Data warehouse Definition * Repository

* Organization
- Data Tables (files)
- Primary & Other Keys
- Respond to Analysis Questions Benefits Data warehouse * Marketing and Sales

* Pricing and Contracts

* Forecasting

* Sales Performance

* Financial Characteristics 1. Organization
2. Consistency
3. Time Variant
4. Nonvolatile
5. Relational
6. Client/Server
7. Web-based
8. Integration
9. Real time OLAP Definition OLAP OLAP Defined
- Advanced data analysis environment that supports decision making, business modeling, and research activities.

* Front End of Data Warehouse

* Data Types Analyzed
-Operational and data warehouse

* Often Integrated Into Spreadsheets (MS Excel) Reason OLAP OLAP Architecture Relational vs Multidimensional OLAP Star Schema * Schema: Star or Snowflake
* DB Size: Larger
* Access: Unlimited dimensions
* Scalability/Flexibility: High
* Speed: Better with med - large data sets
* Architecture: Open * Schema: Data Cubes
* DB Size: Smaller
* Access: Predefined dimensions
* Scalability/Flexibility: Low
* Speed: Better with small - med data sets
* Architecture: Proprietary ROLAP MOLAP End Result * 4 Components
- Facts/Metrics
- Dimensions
- Attributes
- Attribute Hierarchies * Snowflake Schema
-Normalized star schema where dimension tables have their own dimensions * Simplifies data-filtering operations

* Location can be broken down into region, state, city (transitive dependencies) Data Mining Four Phases Data Mining Warning: May Contain Side Effects * Reactive
- Historically Heavily User Defined
* Proactive
- Automated Relationship Discovery
- Automated Trend Discovery
* Results Reported
- May Lead to Predictions Market Segmentation


Customer Churn


Fraud Detection * Interactive Marketing


* Trend Analysis


Market Basket Analysis * Dependent on Database Size
-Average Range 10GB - 11TB
-Invasion of Privacy

* Dependent on Query Complexity

* Cost Range
-Few Thousand for Small
- $1M per TB for Large * Many Variations
-No Established Standards
-Use Different Algorithms
-Algorithms Applied Uniquely

*Division/Tool Families Industry Based 1. Data Preparation

2. Data Analysis and Classification

3. Knowledge Acquisition

4. Prognosis (Optional) * Unexpected Results


* Useless Results (Idiot Correlation)


* Invasion of Privacy References Database Systems: Design, Implementation and Management 9th edition
Coronel, Morris, Rob
Section 13.9
 
UCLA
http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/datamining.htm
 
University of Texas
http://www.laits.utexas.edu/~anorman/BUS.FOR/course.mat/Alex/

Time
http://www.time.com/time/magazine/article/0,9171,2058205,00.html

Images URLs: http://www.infoknowledgefocus.com/personalcontentmanagement/files/2010/02/business_intelligence_vendors-thumb.jpg
http://www.passionned.com/bi-business-intelligence-and-data-warehouse.png
http://support.sas.com/documentation/cdl/en/gridref/63292/HTML/default/images/em92grid.gif
http://www.sas.com/resources/screenshot/forecast_server_1_full.gif
http://www.sas.com/knowledge-exchange/business-analytics/files/2012/09/The-Evolution-of-Decision-Making-fig5_800.png * Advanced Data Analysis to Support:

- Decision Making
-Answer who, what, why, and what if

- Business Modeling
-Sales analysis and forecasting, segmentation

- Operations Research
-Productivity, production planning, analysis * Multidimensional data analysis
Presentation
Drill down/Roll up data
Computational Functions
Data-Modeling
* Advanced database support
Different Data Sources
* Easy to use end-user interface
* Support client/server architecture 4 Main Characteristics What is Business Intelligence?
General Steps for Business Intelligence and Analytics processing
Business Intelligence Framework
Business Intelligence Tools
The Role of Business Intelligence General Steps to BIA Collection of data
Processing and analysis
Conversion to information
Decision making support
Decision and data collection
Continual monitoring of data Business Intelligence Framework and Tools The Role of Business Analytics Gather data to support information creation
Dependent on management to ask right questions
Does not replace the human factor in decision making
Has the potential to be a very powerful tool
Full transcript