Unlocking Big Data with R. The explosion of big data has caused far-reaching ripples in the enterprise. Organizations today are faced with unprecedented challenges in sorting, processing and analyzing their data, which has in turn given rise to a new generation of technologies. One such example is the R statistics language, which was originally developed by noted statisticians Robert Gentleman and Ross Ihaka at the University of New Zealand in 1997.
In recent years, R has emerged as a popular language for advanced analytics, and is also central to the emerging data science movement. David Smith is Vice President of Marketing & Community at Revolution Analytics, a Palo Alto, Calif. -based startup that produces enterprise-ready analytics software built on top of the R statistical language. Follow him on Twitter: @revodavid. Over two million analysts worldwide use R, and they come from an extremely diverse pool of industries that ranges from journalism to financial services to life sciences. New York Times Co. Orbitz. Algorithmic trading -- the positive side. In researching a forthcoming article, I happened upon this recent empirical study in the Journal of Finance looking at some of the benefits of algorithmic trading.
I've written before about natural instabilities inherent to high-frequency trading, and I think we still know very little about the hazards presented by dynamical time-bombs linked to positive feed backs in the ecology of algorithmic traders. Still, it's important not to neglect some of the benefits algorithms and computer trading do bring; this study highlights them quite well. This paper asks the question: "Overall, does AT (algorithmic trading) have salutary effects on market quality, and should it be encouraged? " The authors claim to give "the first empirical analysis of this question. " The ultimate message coming out is that "algorithmic trading improves liquidity and enhances the informativeness of quotes.
" In what follows I've given a few highlights -- some points being obvious, others less obvious: Ten “big data” trends transforming financial services. You are here: Home » Blog » Ten “big data” trends transforming financial services Publication date: 18 June 2012Author: Neil Palmer (SunGard) and Michael Versace (IDC Financial Insights) Financial services firms are consolidating data traditionally managed in silos in order to analyse risk exposure, comply with regulatory mandates, and use the data for multiple purposes.
Traditional technologies such as relational database management systems make it challenging, if not impossible, to process growing volumes of data and make it accessible, actionable and flexible to changing needs in terms of queries and analytics. ‘Big data’ solutions that support evolving business and regulatory requirements by maintaining an ecosystem of large data sets will become invaluable in their ability to be used for multiple purposes and to answer any question months or years from now. Big Data Right Now: Five Trendy Open Source Technologies. Big Data is on every CIO’s mind this quarter, and for good reason. Companies will have spent $4.3 billion on Big Data technologies by the end of 2012. But here’s where it gets interesting. Those initial investments will in turn trigger a domino effect of upgrades and new initiatives that are valued at $34 billion for 2013, per Gartner.
Over a 5 year period, spend is estimated at $232 billion. What you’re seeing right now is only the tip of a gigantic iceberg. Big Data is presently synonymous with technologies like Hadoop, and the “NoSQL” class of databases including Mongo (document stores) and Cassandra (key-values). But there are new, untapped advantages and non-trivially large opportunities beyond these usual suspects. Did you know that there are over 250K viable open source technologies on the market today? We have a lot of…choices, to say the least. What’s on our own radar, and what’s coming down the pipe for Fortune 2000 companies? Storm and Kafka Why should you care? Drill and Dremel. Big data: Crunching the numbers. A BIG BANK hires a star analyst from another firm, promising to pay a substantial bonus if the new hire increases revenue or cuts costs.
In banking this happens all the time, but this deal differs from the rest in one small detail: the new hire, Watson, is an IBM computer. Watson became something of a celebrity after beating the champion human contestants on “Jeopardy”, an American quiz show. Its skill is to be able to process millions of documents quickly by reading and “understanding” ordinary written language. Computers have no trouble with searching data neatly sorted in databases. Watson's claim to fame is that it can do the same with “unstructured data” such as those found in e-mails, news reports, books and websites.
IBM hopes that Watson may, in time, do some of the work that human analysts do now, such as reading the financial pages of newspapers, looking at thousands of company results and forecasts and producing a list of companies that might be takeover targets soon.