BIG-DATADATA

1. BIG-DATADATA A REVOLUTION THAT WILL TRANSFORM HOW WE LIVE, WORK, AND THINK

2. CHAPTER 7 IMPLICATIONS • The Implication of BIG-DATA can be viewed from BIG-DATA value chain • The value chain represents three categories of BIG-DATA companies • The First are the DATA companies that have the data but perhaps do not use it themselves. E.gWalmart • The Second are the Skills companies. They offer consultancy servicies, possess BIG-DATA analytics technology and have special expertise but they don’t possess the data themselves – E.g: Teradata, Google • The Third have the BIG-DATA Mindset. Employers in these companies understand the potential of BIG-DATA and how to harness its values

3. DATA COMPANIES • These companies possess or control access to BIG-DATAdata information and use it for themselves or license it out • E.g ITA software, a large airline reservation network provides data to Farecast for its airfare predictions, but did not do the anaylysis itself • Master Card and Visa Card are Data Companies by serving many banks with card services and Fraud protection, they hold giant customer information and uses them to make inference about consumer behavior • Useful customer behavior prediction shows that: If people fill up their gas tanks in the afternoon around 4pm they will likely spend $35 to $50 in the next hour at a grocery store or Restaurant

4. SKILLS COMPANIES • Companies provide BIG-DATA Analytic tools such as software to analyze data • E.g Microsoft’s Amalga Software was used to analyze decades of Medical record at Med-Star Washington Hospital • Correlation Results shows: Increased Possibility of discharged patients returning to the Hospital within one month. • Patients with Congestive Heart failure and depression show high probability to return after treatment • Companies represent Specialists who extract value from the data and gave correlations

5. BIG-DATA MINDSET COMPANIES • Made up of companies with BIG-DATA Mindset • They are poised to see BIG-DATA Opportunities before others • They often lack the skills and data to act upon their ideas • E.g FlightCasters.com are able to predict if a flight in the US was to be delayed • US Flight companies cannot publish such info based on Federal laws • Flight Companies supply FlightCasters with data but rely on them for flight delay prediction • FlightCasters’ predictions are so accurate that even airline employees use them

6. COMPANIES THAT COMBINE ALL • Some Enterprises straddle the three domains of BIG-DATA • For Instance, Google collects data like search-query typos, has the bright idea to use it to create a spell checker, enjoys the in-house skills to execute the idea brilliantly • Amazon also fits into this category. It has a BIG-DATA mindset, the Expertise and the Data • Google and Amazon have different approaches to BIG-DATA – Google has secondary use of BIG-DATA in mind when Capturing DATA, whereas, Amazon focuses on the primary use of the data

7. NEW DATA INTERMEDIARIES • This focuses on those with BIG-DATA Mindset • Today, in big-data early stages, the ideas and skills rank higher • But eventually, most value will be in the data • Inrix is a BIG-DATA intermediary company. • Inrix collects real-time information from 100 million multi-vendor cars from across Europe and America, Analyzes the information to predict traffic flow analyses and sells the information to individual Car Companies

8. CHAPTER 8 RISKS • SURVEILLANCE AND SPYING • With BIG-DATA promising valuable insight to those who analyze it, all signs seems to point to a surge in other’s gathering, storing and reusing our personal data • Companies like Equifax, Acxiom collects, tabulates and provide access to personalized information for hundreds of millions of people worldwide • BIG-DATA has not only changed our scale but also our state. • The Darker side of BIG-DATA is the possibility of using big-data to predict people’s actions and even punish them if there are high chances they will commit a crime in future – This negates ideas of fairness, justice and freewill

9. BIG-DATA PARALIZES PRIVACY • Much of Data captured includes personal information and with BIG-DATA analytics, data can be used to trace back to the individual it refers to • Anonymization is difficult. AOL Collected 20 Million Search queries from 657000 users and deleted their usernames and IP addresses. But researches still found how to link together search queries from the same person. • Netflix can identify a customer after he rates an obscure movie 6 times with an accuracy of 84% and if the dates of ratings are known, the accuracy of identifying the individual increases to 99%

10. PROBABILITY AND PUNISHMENT • Large cities like princints, Richmond, Virginia – employ “predictive policing”: using BIG-DATA analysis to select what streets, groups, and individuals to subject to extra scrutiny, because an algorithm pointed to them as more likely to commit a crime • A Research project in the U.S. Dept. of Homeland Security called FAST, tries to identify potential terrorists by monitoring individuals’ vitals body sign language and physiological patterns and their potential to do harm. FAST was found to be 70% accurate • Probability and punishment big-data analytics can be misleading and penalizing people before they commit crimes is nauseating. • BIG-DATA Algorithms are not perfect

11. SUMMARY • The Value Chain of BIG-DATA spans three categories: • The DATA • The SKILLS • The IDEAS • BIG-DATA mindset and skills are most relevant today but the data itself will matter more in the future • Risks involved in gathering BIG-DATA includes: • Surveillance and Spying • Privacy Invasion • Punishment based on BIG-DATA prediction