Enterprise data assets are what feed the predictive analytic process, and any tool must facilitate easy integration with all the different types data sources required to answer critical business questions. Robust predictive analytics needs to access analytical and relational databases, OLAP cubes, flat files, and enterprise applications.
Welcome to the first article in a weekly series called “Ask a Data Scientist.” Once a week until you’ll see reader submitted questions answered by a practicing data scientist. Think of this new insideBIGDATA feature as a valuable resource for you to get up to speed in this flourishing area of technology. If you have a big data question you’d like answered, please just enter a comment below, or send me an e-mail.
As businesses seek to maximize the value of vast new stores of available data, Northwestern University’s Master of Science in Predictive Analytics program prepares students to meet the growing demand in virtually every industry for data-driven leadership and problem solving.
I found an interesting discussion going on in the Global Big Data & Analytics group on LinkedIn – “Why do Hadoop projects fail?” Having just returned from the Hadoop Summit 2014 in San Jose, I witnessed plenty of use case examples Hadoop implementations that were wildly successful. I was therefore intrigued by the notion to itemize causes for failed projects. [Read More...]
Big data is all about finding ways to manage the increasing volume of information being kept on consumers presumably to help with making purchase decisions and fine-tuning the customer experience. Through data science and machine learning, technology-driven businesses have the ability to know more about their customers and potential customers than ever before. Service providers like TellApart, AdRoll, and Rubicon Project target online ads with unparalleled precision. [Read More...]
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