GPU vs CPU Admittedly, discussing the differences between CPUs and GPUs is a rather elementary concept for technologists, but it’s an important exercise that helps us better understand what drives […]
“The application is slow.” “I bet it’s the server.” “Nope, it’s gotta be the network performance.” How many times have you heard these phrases tossed back and forth at your […]
Upper management has decided that your company’s next business move will be towards cloud. Which (as you know) is much easier said than done. You have infrastructure to consider, hardware […]
When people hear the word “analytics,” they tend to get nervous—especially when it’s coupled with other words like “algorithm,” “GPU,” and “deep learning.” And they automatically think that the subject […]
Previously, we discussed the three fundamental branches of analytics as well as how each one successively breaks data down into more manageable pieces. In today’s installment, let’s continue the data […]
Housing untapped, raw data does little but expend additional storage and security resources. But, at the same time, accessing that data for active use is hard. Because of this, many […]
Previously, we discussed some of the pros and cons posed by machine learning. While “self-learning” computers have become the future of autonomous tech, R&D teams are still working to iron […]
As we mentioned before, there are seemingly limitless ways that Big Data can augment our realities, and we believe that it is important for you to understand how these analytics […]
Today, we return to our ongoing analytics conversation. If you are new to the discussion and want to get caught up, consider revisiting our posts on the three fundamental levels […]
“The majority of companies don’t realize that they can answer many of their business questions with the data that they already have. It’s just a question of being able to access it.” — TSA Enterprise Solutions Architect, Corey Gary
In a previous blog post, we discussed the three basic levels of analytics—descriptive, predictive, and prescriptive—and explained how this technology is architected to break down massive amounts of raw data into more manageable, actionable chunks. With modern business data analytics, researchers and technologists no longer have to “drink from the fire hose” of raw data.
However, it is important to establish that analytics is not simply a “wind-up-and-let-it-go” apparatus; it requires more resources than its current IT buzzword-status may have you believe. As a result, many companies are underutilizing the data at their disposal.