Introduction and Module 0 Week 1 Blog Post

 

Greetings MIS 587,

 

My name is Josh and I have lived and worked in Tucson for the last 5 years. I’m married to a wonderful spouse and have an old rescue dog named Remy. When I’m not working on homework or my day job, I enjoy working out, trying to keep my outdoor plants alive in the Tucson sun, smoking cigars, and going to the movies. I most look forward to learning about optimizing websites for increased web traffic using google analytics. I am looking forward to the course and learning more!

What I gathered from the lecture is that big data refers to the enormous volume, variety, and velocity of data. Multiple zettabytes of data is quite a bit of data. The slide on megabytes to gigabytes to terabytes to petabytes to exabytes to zettabytes helped me to understand the volume of data that we are talking about when we discuss zettabytes. I also gathered that we no longer are limited to surveys of select groups to gather data. Instead, we can gather data from everyone as characterized in “n=all”.

Data is not limited to numbers and includes messages, pictures, likes, reviews, etc. The variety of data has exploded. This generates the need for professionals who can find ways to harness this large volume, variety, and velocity of data. Business intelligence professionals glean insights for decision making using both external and internal data. They do this through techniques, tools, technologies, etc. In fact, business intelligence includes data warehousing, data mining, digital dashboards, analytics, reporting/querying, and data collection/processing.

The Foreign Affairs article, The Rise of Big Data, suggests that professionals should be less selective about which data to use on studies and instead, we should use gobs of data and worry less about the “messiness” of the data (Cukier and Mayer-Schoenberger 2013). The article also suggests that professionals should use the large amounts of data to find correlations rather than causes (Cukier and Mayer-Schoenberger 2013). There are lots of real-world applications for finding and using correlations. One such application is how UPS uses sensors to detect heat or vibrations of vehicle parts correlating to failure of that part (Cukier and Mayer-Schoenberger 2013).

I can appreciate the use of big data to find correlations and I’m not concerned with the messiness of data. However, I think there is value still to finding causes such as why does UPS’s vehicle part fail when it does? Perhaps, it’s not the most efficient part to begin with. Perhaps the radiator is overheating because engine coolant is not regularly kept at the optimum volume or perhaps the engine coolant liquid needs to be changed more frequently. Or perhaps the wrong coolant is used to begin with or perhaps mechanics are combining several types of engine coolants over the course of “topping-off” the vehicle’s fluids at regular maintenance visits. If the cause of the issue (the part failure) is addressed, then big money could be saved for an entire fleet of UPS vehicles rather than changing out the part once the vehicle’s sensor detects the heat/vibration pattern. I think it’s great that big data can be used to find correlations. I also think that it is important to remember that discovering the cause can sometimes be a greater benefit and greater money saver than just making correlations.

 

Reference

Cukier, Kenneth Neil; Mayer-Schoenberger, Viktor. Jun, 2023, “The Rise of Big Data”, Foreign Affairs. https://d2l.arizona.edu/d2l/le/content/1318264/viewContent/14825050/View  [accessed 8-29-23]

Comments

  1. AnonymousSeptember 7, 2023 at 10:24 AM

    Josh, your insights on big data's volume, variety, and velocity are spot on, and the shift from selective sampling to "n=all" is significant. While correlations are valuable for insights and predictive analytics, understanding root causes can lead to more effective, long-term solutions. Combining both approaches is often powerful. For UPS, addressing root causes of vehicle part failures could save significant costs. Balancing correlation-driven insights with casual understanding is key in big data analytics. Good luck with the course and the journey into web optimization and data analytics!

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  2. Aylin BurgosSeptember 7, 2023 at 7:10 PM

    Hi Josh,
    I found your analysis very interesting. I think the way you described that 'the variety of data has exploded' was a great way to really capture how quick and fast the amount of data available is growing. Although it may be a bit intimidating to some how much of what we do is captured and fed back to companies, individuals etc. in data forms, I agree that it is a very useful approach because thanks to this data, companies are able to make improvements on their products to give consumers what they need. As it was stated in this weeks lecture, because of n=all, we no longer have to base findings on samples or focus groups, making the results much more accurate. Great job on your blog, and good luck with your outdoor plants (I know the struggle lol)!

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  3. AnonymousSeptember 10, 2023 at 7:47 AM

    Hi Josh, I enjoyed reading your analysis, particularly your use of the UPS example. I think this is a great example of the vastness, velocity, and variety of data available that can be connected in ways that might not have been feasible 20 years ago. Being able to correlate equipment wear down and maintenance to specific parts, routes, regions, suppliers, etc. can be incredibly useful for business intelligence professionals for finding actionable ways to improve operations for UPS. Great blog and thanks for sharing! Looking forward to reading more from you throughout the semester!

    ReplyDelete
  4. AnonymousSeptember 10, 2023 at 8:55 PM

    Greetings Josh,

    Nice to meet you! I live in Phoenix valley and must ask you how you manage to keep your plants alive outdoors in summer.

    Your summary and analysis of the material is on-point. I agree that volumes of big data has now replaced the need for accuracy and it supports businesses to improve. I particularly liked your argument to still try to find the cause instead of only correlation. There are so many areas where just correlation may not suffice and cause of an event is equally important. You rightly pointed this out with the UPS vehicle example. Another area is healthcare. It is equally important to know the cause of a patient's situation as is to know the correlated factors. You have pointed out the importance of identifying patterns with just correlation may yield short to medium-term benefits, but realizing causes helps us improve in longer term.

    It was a great blog and I look forward to reading more of your blogs in this course.
    Good luck!

    ReplyDelete

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