Businesses have started to embrace big data in greater numbers and intensities throughout the past few years, directly leading to a high rate of firms working to move their information around and re-position the content to be useful in an analytics strategy.
The dramatic increase in information volumes around the globe has ushered in a new era of analytics capabilities, but has also placed a bit of strain on the average business infrastructure.
In the past few years, analysts have argued that the skills gap facing businesses will intensify and spread significantly.
The Internet of Things has become one of the most discussed trends in corporate computing, all in a relatively short period of time, as the applications of these connected devices and assets are vast.
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.