In my previous post announcing Datawatch’s debut on Gartner’s 2015 Magic Quadrant for Business Intelligence and Analytics, we talked about how our strong scores for customer experience contributed heavily to Datawatch’s ranking. Our data discovery capabilities were also a key factor in making the Magic Quadrant cut. A core function for data discovery solutions is […]
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.
Think of it this way: Your customer relationship management (CRM) deployment provides you with more information than you know what to do with, so why not make the most out of it?
We are excited to be positioned on Gartner's 2015 Magic Quadrant for Business Intelligence and Analytics for the first time.
At times, some people don't appreciate the sweat that goes into blueprinting and launching a large-scale data analysis project.
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.
Telecommunications is a pretty hot topic nowadays, especially considering the Net Neutrality debate that has flooded United States news outlets over the past few months.
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.
Unless you know for sure that your data aggregation systems are providing you with concrete information, there's a chance the "actionable insights" you're hoping to glean are only half-correct, if at all.