1. Andrew Ferlitsch 12-March-2014 OpenGeoCode.Org “Open Data Project” Paper: www.opengeocode.org/articles/ 1

2.  Data was manipulated using Excel Spreadsheets  Business-Oriented personnel manipulated data in Excel spreadsheets to project Market/Sales.  64,000 record limit to Excel.  Not Big Data – anything less than 64K records 2

3.  Number of Data Records exceeded 64K (Excel Limitation)  Data migrated to databases.  Businesses hired database engineers to develop queries to reduce the data < 64K records.  Once again, data could be manipulated by Businesses Oriented personnel in Excel. 3

4.  Amount of Data Got Bigger  Heavily driven by data collections such as credit card transactions in the travel and hospitality and retail industries.  Databases migrated to Bigger Servers  Businesses hired IT personnel to support database engineers.  Database engineers continued to develop queries to reduce the data < 64K records.  Once again, data could be manipulated by Businesses Oriented personnel in Excel. 4

5.  Major Airlines and Major Hotel Chains were the initial major drivers of Big Data  Collect occupancy information to predict likelihood of filling a seat or room in real-time.  Airlines/Hotels were looking to know when to increase rates on the last remaining 5-10% seats/rooms (not discount!).  Airlines/Hotels starting using real-time information (secret sauce) to know when NOT to discount because it would fill anyways! 5

6.  Major Airlines and Major Hotel Chains an Industry where margins are low  most airlines have low profit margins, around 5%  The average global profit margin for the global airline industry is 3.4%  This ‘reverse discounting’ raises profit margins without:  Expanding Market Share  New Marketing Campaigns  Increase in Manpower  New Management 6

7.  Online Retailers using Big Data to drive recommendation engines.  Categorize User (Visitor) into Customer Profiles.  Demographics  Purchasing History  Build Likelihood to Purchase Graphs (much like social graphs in social technologies).  Purchasing History of recent customers within the customer profile group.  Current site visiting activity.  Current and recent purchases.  Uses recommendations to increase the number of items a person buys on an online visit. 7

8.  Brick & Mortar retailers looking into how to use the Reverse Discounting Method for Stock on Shelfs.  Using Historic Data + Real-Time information to know when to Reverse Discount (i.e., Not Discount – because it will sells anyways!)  Hadoop, “R”, and leasing server time is the “in” thing.  Reverse Discounting  Higher Profit Margin  Increase P & L  Drives Market Capitalization = Corporate Value 8