1. Chapter 6: VALUE In the late 1990s, the development of web was quickly unwelcoming and unfriendly, many spambots were coming to inboxes and swamping online forums. In 2000, Luis Von Ahn, created a solution, by forcing registrants to prove they are human and not machines. In big data : the value of data is changing, from its primary use to its potential future use => changes in dealing with data. Information has always been essential for market transactions (stock prices). In big data, all data is valuable. Although keeping huge useful and raw amount of data used to cost a lot, however nowadays storing data is much cheaper. ReCaptcha point out the importance of reuse data.

2. History of user data, websites log record every click a user make for analyzing and optimizing the site content. Amazon uses recording user clicks (sometimes pointer movement) or opened pages to offer recommendations, ads, etc. Facebook tracks use ”status update and likes” to determine most suitable ads to display on its page to earn revenue. Data’s value doesn’t vanish when it is used, it can be processed several times. The data’s full value is much greater than the value extracted from its first use. The ”Option value” of data: The success of electric car depends on some logics, most important is battery life, Quick recharge - availabilty - stabiltiy of grid. This is not infrastructure problem as information one. Big data can be used in this case (IBM used it) to collect amount of inormation (battery’s level, location of car, time of day, charging stations, etc). Analizing the information of real-time from multiple sources let IBM determines the optimal times and place for recharging batteries, where to build charging stations, weather forecast information,

3. The system take information for one purpose and reuse it for another (data moves from primary to secondary uses). Big data use all the available data, thou, data’s value needs to be considered in terms of all possible ways it can be employed. The infinite potentials uses of data are like options, in sense of available choices. The reuse of data: Information seems worthless after its primary use, because it has fulfilled its primary purpose but reusing it can bring extraordinary values. Marketers can use Hitwise (hits history) to get some useful information and make decisions. Google collects useful data from its records of the opened pages over the history. Bank of England use search queries related to property to get more information on housing prices situation. Amazon use AOL e-commerce to get data on what AOL users were looking at and give recommendations. Google analyzed and used voice records to innovate a voice corresponds to specific word, which is good for improving speech-recognition technology or creating new services. Mobile operators collects hug amount of data about subscribers’ locations and provide it to other companies which can use it for later purposes. Data reuse is useful for organizations use, collect and control large dataset but currently make little use of them.

4. Recombination data: Sometimes the dormant value can be clear when combining one dataset with another. Does Mobiles increase the like hood of cancer?  Data has collected about mobiles user from 1987 to 1995.  Data is available by mobiles operators and socioeconomics.  Another data on people who had tumors collected by nationWild registry office (1990-2007). In big data, the sum is more valuable than its parts Extensible data: To allow reusing the data, it should be designed to be extensible from the outset so it serves multiple uses. Google car snaps pictures for street view project, Collect information about locations, GPS data. Google makes use not only for Primary use (street view) but also for other (secondary use) like improving company mapping service ”using GPS data”, GPS for auto drivers, etc. The extra cost of collecting multiple stream of data is often low, so it is good to collect as much as possible taking in consideration making them extensible Combining 3 datasets, researchers looked into whether mobile users show higher rates od cancer than non- subscribers. Results were there is no connection between mobiles and cancer. Originally these data has been collected for different purposes, but combining the data can serve new purpose.

5. Depreciating Value of Data: Sometimes, data loses some of its utility over time. Therefore, relying on it doesn’t just fail to add value, it may destroy the value of newer data. Amazon may depends on old data to do recommendation, but if it kept depending on outdated data, it may lose the user interests. (user can change his/her interests). Therefore, using data should last as long as it remains productive. The challenge is to know what data is not longer useful! Amazon and some other companies made some models to extract the useful data. The Value of data exhaust The Value of Open Data Valuing the Priceless