This document discusses big data and business intelligence. It defines big data as large volumes of varied data that is collected and processed rapidly. It outlines different types of primary BI systems including reporting systems, data mining systems, knowledge management systems, and expert systems. It also discusses challenges with raw data and the need for data warehousing to extract, clean, and prepare data from different sources for BI processing and analysis. Finally, it provides an example of how a mountain resort (MRV) could use BI and data warehousing to develop a data storage plan, generate reports on repeat business, identify high-value customers, and analyze equipment usage.

1. Business Intelligence
3/28/2013

3. How Large is a Petabyte?
= 4 x

4. What is “Big Data”
• Volume
– Transactions
– Social Media
– Mobile Phone
– Web Traffic

5. What is “Big Data”
• Volume
• Variety
– SQL Databases
– TXT Log Files
– Graph Databases
• Velocity

6. What is “Big Data”
• Volume
• Variety
• Velocity
– Rate of Collection
– Need for Agility

7. Primary BI Systems
1. Reporting systems
2. Data-mining systems
3. Knowledge
Management
Systems
4. Expert Systems

8. Reporting Systems
• Integrate data from
multiple systems.
• Calculate and
summarize data.
• Sorting, grouping,
summing, averaging,
comparing
• Present data as
meaningful information.

9. Reporting Systems
• Sybase Infomaker
• Crystal Reports
• Microsoft SQL Reporting
Services

10. Data Mining Systems
• Use sophisticated
statistical techniques,
regression analysis,
and decision tree
analysis.
• Discovers hidden
patterns and
relationships.
• Can be used as the
basis for predictions.

11. Data Mining Systems
• MS Business
Intelligence
Development Studio
• SAS Enterprise
Miner
• WEKA

12. Knowledge Management
Systems
• Collect and share
human knowledge.
• Best practices
• Employee training
• Process
documentation
• Collaboration &
Communication

13. Knowledge Management
Systems
• Sharepoint Server
• Kwiki, TikiWiki,
Twiki, Zwiki
• Google Docs
• Google Sites
• Evernote

14. Expert Systems
• Uses rules and logic
(i.e. IF > THEN)
• Automates and
standardizes
decision making.
• Examples: Medical,
HR, Finance,
Stocks, Games
• IFTTT

15. BI Comparison Table

16. Business Intelligence Problems
• Raw data usually unsuitable for
sophisticated reporting or data
mining
• Dirty data (misspelled, wrong type,
missing, duplication, inconsistent)
• Multi-dimensionality
• Wrong granularity (summary vs.
detail)

17. Data Warehousing
• Extract and clean data from
various operational systems
and other sources
• Store and catalog data for BI
processing
• Extract, clean, prepare data
• Stored in data-warehouse
DBMS

18. Data Warehousing

19. Data Mart
• Created to address particular needs
• Smaller than data warehouse
• Users may not have data management
expertise
• Data extracted from data warehouse for a
functional area

20. BI & MRV
MRV could:
• Develop a plan to organize storage of data and how
management might use it;
• Use a reporting system to provide information about
how much repeat business each guide generates;
• Identify high value customers and customer referrals;
• Analyze equipment inventory usage to guide future
equipment purchases.