spring-09-review-session-final

spring-09-review-session-final - BCOR 2500 Final Exam...

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BCOR 2500 Final Exam Review, Spring 2009 Putting it all Together Business Objectives- Business Achievements/KPIs o Bottom line o $ benefits o Example- Profit, revenue, market share Business Tactics o Business processes minus the technology part o What would you do if you got system results o Retraining- changes within people; learning new things o $ cost o Example- using a database instead of matching paper documents, using digital cameras to take pictures of overflowing trash cans, System Objectives- o Result from implementing technology o Output of system tactic
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o Improved efficiency/cut down time to complete accounts payable process System Tactics o Hardware/software/technology o Implementation of a system o $ Cost o Example: ERP, data warehouse, simulation software Technology Push: have a working system in place, but technology Development Information Cross o Foundation comes from Trust o Data – ETL (Analysis Gap) – Information – Knowledge/BI – Decision – Power o How do you perform ETL when crossing the analysis gap? Lisa o Used front end tools and multidimensional analysis to manipulate the data and determine that she was asking the wrong questions. Created BI to determine what was working and how to implement across the board
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o Good example of how BI is Created- Not by technology but PEOPLE Chapter 2: Bridging Analysis Gap o Converting raw data into applicable information o Use Multidimensional analysis to look at several dimensions together Slicing and dicing Roll up/drill down o An example of a Dimension? o An Example of a measure? o OLTP- operational database; stores and collects data (no analysis) o OLAP- provides analytical processing (BI) by organizing data into hierarchies (where drilling and rolling come into play) o Ad hoc analysis- the ability to query (search) a database for specific terms Chapter 3: BI Technologies/Data mining o Data mart- subject specific databases (sales, HR, accounting) o Data Warehouse- centralized store that accesses all of the data marts
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o Data Mining: finding patterns that explain behavior or uncover trends that are not intuitive o Goal is to provide useful information to make better decisions faster o Descriptive: describes patterns in data using human interaction to determine their significance and meaning Examples: Segmenting and Clustering o Predictive: used to try and predict future behavior or the likelihood of a particular outcome Decision tree
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spring-09-review-session-final - BCOR 2500 Final Exam...

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