r-analytics

  • 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 […]

  • 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 […]

  • 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.