The highly competitive world of e-commerce is driven by price and advertising. Companies must find ways to successfully reach customers, who generally are not loyal, through successful ad campaigns and effective pricing techniques. A vital tool for success in e-commerce is Big Data. To succeed in e-commerce, companies rely on information that tells them how …
In this webinar, James Vogel and Bernard Wehbe show you how to deliver embedded analytics. Quickly becoming popular with clients, the industry has seen a major influx in the relevance of embedded analytics to today’s market.
What does the future of business look like? In an informative talk, Philip Evans gives a quick primer on two long-standing theories in strategy -- and explains why he thinks they are essentially invalid.
We have all heard these words flying around: “big data,” “analytics,” “data warehousing” and more. But what do they mean? According to research from IBM, 90% of the world's data was created in just the past few years.
Facebook detailed on Tuesday a new cybersecurity framework called ThreatData. It’s a collection of systems for ingesting, analyzing and acting upon threat data that can vary greatly in both type and frequency.
Jacek Bugajski's insight:
Facebook has built ThreatData, a framework for web security
Within the big data landscape there are multiple approaches to accessing, analyzing, and manipulating data in Hadoop. Each depends on key considerations such as latency, ANSI SQL completeness (and the ability to tolerate machine-generated SQL), developer and analyst skillsets, and architecture tradeoffs. Below is a discussion segmented by broad latency characteristics of each approach.
This site provides an overview of common problems and solutions faced in the management, analysis, and interpretation of data. Predictive modeling is increasing rapidly in development of analytical approaches and applications.
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