After reading the syllabus and watching the first lecture of my USF course on "Big Data"; it's safe to say that I have chosen the correct educational direction for myself. Lets start from a professional perspective.
     I am the Rental Maintenance Coordinator for a large heavy equipment dealership. My main objective is to reduce the maintenance cost of our rental fleet. There is only one issue with this - I haven't put hands on an actual machine in years. I spend my days in a cubical, typical several hundred miles away from the machines that I am concerned with. A few years ago, no such position would have existed; as there would not be much one could do concerning machine maintenance from several hundred miles away. Technology has changed that. As of about five years ago, many of our machines started coming off the line with telematics devices installed. These devices provide information such as location, hours of use, pressure readings, maintenance scheduling, function usage and perhaps most importantly, diagnostic trouble codes (DTC).
     As useful as this information is, there is another problem - there is just too much of it to handle. Attempting to find correlation between the DTC, hours, service history, working conditions, known issues, etc. is next to impossible by memory alone.
     I have an Excel spread sheet with some of this information, but am not sure exactly what to do with it. I do on occasion notice a pattern that turns out to have predictive capability, but I know that there are a lot more going unnoticed.
     What I hope to accomplish with this course and my education in general is to establish a process for managing this data in a base and applying statistical models to ensure that the company is as efficient as possible in addressing the maintenance and mechanical failures of our rental fleet. There are two main objectives that I have . 1) Reducing travel time and mileage (TTM). It is not that this is typically the highest dollar amount on an invoice, but that it is potentially avoidable. If we were to send a machine out on rent to a customer, only to have it have a mechanical failure shortly there after -  we have not only let our customer down, but also endured costs in addition to the repair itself. 2) Reducing major failures. When it comes to heavy equipment; small issues can become major issues in a hurry. For example: If a machine shoots off a code for an issue with the hydraulic pump; it may be a relatively simple repair if addressed quickly. However, if not addressed quickly; one small failed part could cause catastrophic damage.
     From a personal perspective; I find the rate at which technology is currently increasing to be fascinating. More so than the rate of progress is the rate at which our personal lives depend on it. As an older student, I can remember the days before cell phones and the personal computer boom. Therefore I consider the idea of "Big Data" to not only be interesting, but important, as well. With the exponential growth of technology; it only makes sense to have a genera that deals specifically with managing it all.