Increased global connectivity and usage of smartphones now produces a lot of data that was previously unimaginable. Traditional ways of data analysis are now becoming obsolete and new ways to analyze data are used. This is collectively the big data ecosystem. Spotting trends, correlating them with data available and forming viable predictive models related to traditional businesses, politics, entertainment, sports, online trading, online casino, healthcare and e-commerce is possible when leveraging big data.
For example : Facebook alone handles 50 million user photos from its’ database while eBay has 2 warehouses of 7.5 pertabytes and 40 petabytes alongwith another 40 petabytes of cluster for merchandise, consumer trends and recommendations.
Sports industry as well as online casino, hedge funds, banks and e-commerce sites also have a lot of data to work with. Trying to find undervalued teams for picking winners or shifting odds in favor while playing craps online and even finding undervalued stocks based on historical trends and past performances makes use of big data.
Each of these verticals along with their traditional websites now have a smartphone app to go with. So, for a user who wants to invest in stocks online or place bets online using an online casino or trying to improve his/her edge in payout by analyzing reports that list top online casino and stock brokerage firms. This would again lead to increase in data analysis because mobile apps are tailored to pick cues about individual preferences.
Many vendors offer readymade big data solutions but business verticals that involve a lot of confidential data like hedge funds, online casino, private insurance firms and so on use custom made solutions or integrate the readymade solutions within their existing technology infrastructure.
What previously needed significant manpower to go through volumes of data to predict or build decision models is now increasingly automated using the big data ecosystem. Forecasting business cycles, performance of stocks and other financial assets, calculating favorable odds in online casino, predicting individual player and team performances in sports, forecasting economy growth and conducting military exercises are just some of the uses of big data.
While big data has changed how data analysis is done, it is not without it’s drawbacks. Prediction models can fail because the data set used is actually historical and black swan events happen at a greater frequency now than before. That means big data algorithms that are say used to predict how stock markets will perform, how sports team will perform or how online casino will maintain it’s revenue based on edges can also be inaccurate. Past performance need not correlate with the future and this is where big data complexity can increase considerably. The sampling of data needs to be viable and accurate but also should filter out what is not needed to get reasonably accurate analysis. Complex big data algorithms can therefore require much more maintenance than simple ones. Other concern is user privacy. Complete data set of user likes, preferences and other parameters can yield significantly accurate personal information.
Overall, there needs to be a balance between relying too much on big data and also not completely avoid using it.