As many businesses have already started to see, big data did not take long to move from a highly novel and niche movement in corporate computing to one that is relatively crucial to success and widely implemented.
When it comes to choosing a big data analytics package, your decision likely depends on how well its visualizations convey information and whether or not it integrates well with existing architectures.
Lately, I've been thinking a lot about artificial intelligence - specifically, how machine learning (which is rooted in analytics) is a subset of this technology.
When discussing any IT-related topic, it's easy to use terms that are related to certain subjects, but don't actually describe the function or technology you're talking about.
I've often discussed the energy industry's use of big data to better allocate electricity output from green technologies and enhance smart grid solution functionality.
I've often spoke about predictive maintenance in my pieces about the manufacturing sector's applications of big data analytics.
When discussing big data, people could be talking about either one of two things: the technology or the industry. This post will focus on the latter.
Sure, there's a lot IKEA can learn from a Web-connected recliner, but imagine the kind of insight a business such as General Motors or Ford could gain by scrutinizing data produced by a car equipped with smart devices.
“Deep data” is just another fancy phrase one particular professional coined to describe the digital information that actually matters. For all the insight data analysis software can provide, you’re probably scrutinizing more information than what’s necessary. Some scientists may tell you to cast a wide net and pull in as big of a haul as your data warehouses can handle, but that’s not what I’m going to tell you. Choose your sources wisely InformationWeek contributor…