1. Brenda Woods John Williams Daniel Bailey Breia Stamper

2. Overview  Definition  BI Overview  Benefits  BI Tools  Examples  Future

3. What is Business Intelligence?  Variety of software applications used to analyze an organizations row data  BI as a discipline is made up of several related activities: Data Mining Online analytical processing Querying Reporting  Data analysis process is aimed at boosting business performance  Helps users back up - with hard numbers- business decisions  BI is now a requirement for businesses looking to stay competitive

4. Short video of Norfolk Southern Railway and how they’ve used Oracle BI.

5. Benefits of Business Analytics  Eliminate guesstimating – By providing more accurate historical data, real-time updates, synthesis between departmental data stores, forecasting, trending, and predictive analysis  Get answers faster – BI users can quickly get answers to business questions rather than spending hours reading through printed reports

6. Additional Benefits  Get reports on the fly – Many of the business intelligence software vendors are able to access key business metrics, reports, and dashboards on mobile devices  Get insight into customer behavior – BI software allows companies to see what customers are buying, giving them the ability to turn knowledge into profit

7. Even More Benefits  Streamline operations – With detailed reports on business performance, organizations can easily see where they need to make changes  Improve efficiency – By not wasting significant time looking for data within various departmental sources

8. Business Intelligence Design

9. Common Obstacles  Structured & Unstructured  Internal & External  Don’t have the data  Many Data Sources  Data overloading  Data Access

10. BI Tools  How do we get to this data?  How do we integrate it?  How do we present it?

11. BI Tools Three Categories Data Management Data Discovery Reports

12. Data Management

13. ○ Data Extraction/Integration Read and extract subsets of data Different Data sources Organize Data ○ Data Quality/Transformation Cleansed Data – garbage in garbage out Data Consistency – standardized Data Integrity -must match data source Data Management

14. Data Discovery Applications  Provides an avenue for the users to get to their data  Users are able to slice and dice the data, identifying patterns that are unknown by traditional reports.  Structured to help users make sense of the data

15. Data Mining  Examination of large volumes of data and finding patterns and/or relationships that haven’t been discovered.  Help predict behaviors and future trends

16. Online Analytical Processing (OLAP)  Organized hierarchically  Uses multidimensional datasets  Enables users to compare the data from many different points of view  Utilized by Managers and Power Users

17. Predictive Analytics  Uses historical data combined with transactional data to help identify risks and opportunities for the future.  Forecast of what Might happen in the future  Allows organizations to become proactive and forward looking.

18. Reporting Application  Visualization is a graphical representation of data and can help users understand the data in a more insightful way  Dashboards help managers focus on the metrics that are most important  Scorecards takes dashboards one step further by attaching a numerical weight to the performance and maps the progress toward goals

19. Visualization

20. Dashboards vs. Scorecards

21. Dashboard & Scorecard Examples

22. BI Platform Capabilities  Business User Data Mash up and Modeling  Internal Platform Integration  BI Platform Administration  Metadata Management  Cloud Deployment  Development and Integration

23. BI Platform Capabilities  Free-Form Interactive Exploration  Analytic Dashboards and Content  IT Developed Reporting and Dashboards  Traditional Styles of Analysis  Mobile  Collaboration and SocialIntegration  Embedded BI

24. BI Vendors

25. Pull Rate for Order Pickers

26. Business Analytics  You have to understand the history and current aspects of business analytics to understand the future  These are some of the larger companies that offer business analytic software.

27. Popular Programming Languages  Oracle is mostly C and C++  SAP is written in ABAP  SAS is programmed in C  While some statistical companies use the programming language R, which is based off of C, C++ and Fortran

28. Cheaper Data Storage

29. Future of Business Analytics  Cheaper data storage and processing methods increase use of BA  Helps us to develop more accurate goals for the present and the future  Validate or disprove hypothesis through the use of big data

30. Future of Business Analytics  Collection of larger sets of data then previously collected before.  Transitioning from 32 to 64 bit software and applications  Running queries directly in mainframe ram  Able to use centralized data centers remotely due to faster internet speeds

31. Going Mobile  Faster processors then before  Faster connection speed  More ram  Higher resolution screen and cameras  Social movement to be reachable anywhere and any way

32.  The simple picture text that we were so proud to be able to finally in recent years not to long ago has now moved on to live video chats.  Getting an email on the phone has gone to full blown meetings and presentations on the go

33. Data Collection  Instead of just collecting information on how well your employee has preformed in trying to get a good to the end user you find out how the end user uses the product, how they feel about it, any problems that they have with it, and how, where and why they use it.

34. Business Analytics  Is needed to understand the bigger picture while still not losing focus on the every day goals.

35. Questions