r-analytics

  • Why Do We Use GPUs for Machine Learning

    Why Do You Use GPUs Instead of CPUs for Machine Learning?

    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 modern Artificial Intelligence. Although GPUs are traditionally used to compliment the tasks that CPUs execute, they are, in fact, the driving force behind your AI […]

  • Network Visibility - What's Happening on Your Network?

    Network Visibility, Real-Time IT, and Business Insights

    “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 IT planning meetings? Unfortunately, in this business of virtualized “bigger, faster, better,” finger-pointing can be the natural compulsion when things go south. But rooting out […]

  • You’ve Decided to Go to Cloud. What’s Next?

    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 to decommission, and you have to somehow translate those legacy applications to that ready-and-waiting virtual environment. How do you know which apps to migrate (and […]

  • Analytics Maturity Assessment

    Using Analytics for Annual Planning

    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 matter is beyond their understanding (or even their IT team’s abilities). However, analytics doesn’t have to be impossible or “beyond your current capabilities.” Your company […]

  • Data Ingestion and the Data Value Chain

    Purposeful Data Ingestion: What You Need to Know About the Data Value Chain

    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 ingestion conversation by discussing the different components of the Data Value Chain. Let’s Get Back to Basics The Business Dictionary Online defines a value chain […]

  • Democratize Your Data

    Enabling Decision-Making at the BU Level with Analytics

    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 companies have started working through their database administrators (DBAs) to try to get more from their data. However, as is the current trend, organizations are […]

  • Bias in Machine Learning

    Humans Can’t Fabricate Objectivity: The Bias in Machine Learning

    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 out fundamental limitations found within the technology. Machine learning was created to eliminate the constant need for human computational resources, but researchers are finding more […]

  • 5 More Unexpected Ways that Big Data Augments our Reality

    5 More Unexpected Ways that Big Data Augments our Reality

    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 work behind the scenes in our everyday lives. If you have read the previous post, then you hopefully noticed the undeniable trend that runs throughout […]

  • Enterprise IoT Solutions

    Not Just a Buzzword: Enterprise IoT Solutions

    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 of analytics as well as the analytics implementation learning curve that the tech industry is currently experiencing. For those who are caught up, however, we […]

  • Business Data Analytics

    Real Talk About Business Data Analytics

     

    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.