Today eBay announced it will acquire SalesPredict, an Israel-based company that leverages advanced analytics to predict customer buying behavior and sales conversion. SalesPredict is eBay's latest acquisition that will support its artificial intelligence, machine learning and data science efforts. It follows eBay's recent acquisition of Expertmaker, in order to further bolster our structured data efforts. Financial terms of the deal were not disclosed. Upon the close of the transaction, a number of SalesPredict's employees will join eBay's structured data organization, working from eBay's Israeli Development Center in Netanya.
New York, NY - Dataiku, maker of Dataiku Data Science Studio (DSS), has today announced that the company's flagship product is now available on the Microsoft Azure cloud platform. DSS is an end-to-end platform that enables data teams of all skill levels to create powerful predictive analytics solutions using the latest data analysis and machine learning technologies. DSS provides data professionals with an intuitive interface that gives analytics teams the ability to explore data from multiple data sources (Hadoop, Spark, SQL databases, etc) and leverage the tools and programing languages (SQL, Python, R, etc) they are already familiar with. DSS is able to connect to all types of data sources such as CSV files, SQL databases, MongoDB, HP Vertica, Amazon Redshift, Hadoop, Spark, and more. By combining the capabilities of DSS with the speed and scale of Azure, new analytics paradigms can be created, allowing data professionals to create adaptive solutions that can react to changes in data and create advanced "what-if" scenarios, while taking advantage of the convenience, speed, and agility of Azure.
Albany, NY -- (SBWIRE) -- 10/22/2018 -- With the presence of a number of large globally companies that operate globally and regionally, the global big data market is a fragmented one. Top companies are aggressively engaged in devising new technological tools for scrutiny of big data which renders stiff competition in this market. Top companies to name in the global big data market are Mu Sigma, Oracle Corporation, Opera Solutions, Calpont Corporation, IBM, Cloudera, Hewlett-Packard Co., Teradata Corporation, and Splunk Inc. among others. According to a market intelligence study by Transparency Market Research (TMR), the global big data market is projected to clock a whopping 40.50% CAGR over the forecast period between 2012 and 2018, for the market to be worth US448.3 The opportunities in the market translated into a revenue of US$6.3 bn in 2012.
Traders and financial professionals work at the opening bell on the floor of the New York Stock Exchange (NYSE). It's no secret on Wall Street that a "sell" recommendation in sell-side research reports is exceedingly rare, and it can't be chalked up to today's bull market recently surpassing its ninth birthday. According to FactSet data, only 6% of analyst recommendations on S&P 500 companies are "sell" ratings or the equivalent, lending credence to the notion that conflicts of interest persist despite reform efforts to make recommendations more objective in nature. Put simply, negative recommendations can place an analyst in the virtual penalty box when it comes to getting access to companies, and the effects are clear in a business where access is king. So, is there still use to looking through research reports to figure out which stocks are worth buying and selling?