Observations - The sizing needs to be adjusted on the columns The format for the REGISTRY_ID column appears to be an incorrect format. The annotation used makes all of the numbers the same on the sheet. Once I changed the format – I see that all of them are still the same. How useful is this ID if they are all the same? The phone numbers do not have any -‘s in-between the numbers. This makes them difficult to read. There are many blank or unknown fields. Many of the abbreviations are not intuitive to me. With no context; this makes it difficult to know what exactly this data is for. The data does not appear to be in any particular order – not alphabetical, numeric or chronological. How to make sense of this data Part one – assuming that context cannot be found for this data. Since there is no context and the abbreviations are not intuitively understood – I feel that the best way to make sense of this data is to organize it based on the dates. I would sort the data starting with the oldest entry without an end date, followed by ones with the most recent end date. The decision to organize this list as such is an educated guess on my part. I assume that the dates are the most important data (do to a lack of understanding of some of the data) and further assume anything that is old and does not have an end date would represent an active acct with much history; thus need to be most easily accessible at the top of the list. Entries with recent end dates may represent a customer at risk or a person would may want to renew some sort of license. Another method would be to import this Excel sheet into Access. Whereas Excel is little more than a list – Access is a data base that would allow for more functionality where it comes to searching for any of the data in this sheet. Part two – a search for context. I went to data.gov and used their search and filter functions in an attempt to obtain context for this data. Despite searching for several of the data from the Excel sheet; I was not able to find this particular sheet on the site; thus was unable to obtain the contextual information that I thought would help determine how best to make sense of this data.

Equipment Service

     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.