Business Intelligence Is Transforming Into Intelligent Businesses
You would be hard-pressed to find a Fortune 500 company that does not have a SAS operation. You are best off not thinking of SAS as a product but more of a universe of services based around business intelligence and statistical analysis. The company has been working diligently to embrace the shift to faster analysis using solid state data storage drives and moving the analytics processing closer to where the data is stored. At the recent SAS conference the company laid out some new products aimed at bringing SAS users the benefits of outside, big data analysis without having to leave the SAS environment. The best case for SAS is to make big data from outside sources simply one more feed into the SAS infrastructure. As eWEEK Editor of Features and Analysis Chris Preimesberger highlighted in an article on the new SAS offerings, "SAS, the world's largest independent business intelligence provider, updated its frontline analytics engine April 29 with a new version that can be deployed in several ways: in public or private clouds, hosted by SAS or via on-premise software.” Those new SAS offerings hold appeal to current SAS users, but it is less clear that the company can pull in new users from companies interested in pursuing the big data analytical promise. SAS faces the same issues as other long-established technology vendors such as Dell and Oracle, which are striving to realign their products and services in a technology economy. This requires open standards, nonproprietary software, transparent pricing and customers that want to pay for the service on-demand rather than the product."In such periods, investors may search for more information about the market, before eventually deciding to buy or sell. Our results suggest that following this logic during the period 2004 to 2011 Google Trends search query volumes for certain terms could have been used in the construction of profitable trading strategies." Whether it is financial trends or NFL prospects, the analysis asks simple questions of big data sets. These data sets reside outside the confines of the companies, and their multi-terabyte sizes make them impossible or at least too time-consuming to move.
In big data analysis the objective is often to ask a simple query over a very large, real-time data set. A recent article in Nature investigated if Google Trends could predict financial market activity. The answer was yes. As the article stated, "Google Trends data did not only reflect the current state of the stock markets, but may have also been able to anticipate certain future trends. Our findings are consistent with the intriguing proposal that notable drops in the financial market are preceded by periods of investor concern.