MIS: Business Intelligence
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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.
MIS: Business Intelligence
- 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
- 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.
Editor's Notes
- #5: Volume is actually a benefit. The benefit gained from the ability to process large amounts of information is the main attraction of big data analytics. Having more data beats out having better models: simple bits of math can be unreasonably effective given large amounts of data. If you could run that forecast taking into account 300 factors rather than 6, could you predict demand better? This volume presents the most immediate challenge to conventional IT structures. It calls for scalable storage, and a distributed approach to querying. Many companies already have large amounts of archived data, perhaps in the form of logs, but not the capacity to process it.