1 of 1500+ Blog Posts in the last 60 Seconds

The most interesting thing I encountered this week is the link Dr. Ram posted on Twitter: The Internet in Real Time, which essentially works like a ticker for Facebook likes, Tweets, Google searches, YouTube videos, and so on.  In fact, you can ask anyone close to me about it, and they will tell you I made them watch the numbers grow at some point in the last few days.  I had shared some of the facts from the first lecture about how quickly data is generated with friends and family, but the visual is far more expressive than stats alone.  Most everyone was quietly awed by the amount of traffic in 30 seconds or less, but I had the most fun showing it to a friend who is notorious for being so tethered to her phone that she often has whole conversations without making eye contact because she's too busy Pinning or Facebook-ing to look up.  Which is exactly what she was doing when I showed her, and after clarifying that all that activity had occurred since I opened the link, she Liked one more item on Facebook to contribute to the count, then put her phone in her purse and had an actual conversation.

This type of Big Data activity is exactly what kept me off Facebook longer than many of my peers.  I didn't like the idea of my non-business activities being tracked, so I refused to participate.  As recently as 2008, my husband showed me a photo someone had posted of me on Facebook and I reacted by picking up the phone and calling my friend and demanding he take it down.  He laughs while telling that story now, joking that it wasn't even a bad photo and teasing me about how I used to be afraid of the Internet.  In 2009, I caved and opened a Facebook account to share pictures I took while on vacation in Idaho with my Facebook-ing friends... while I was still in Idaho.  It was so much fun to share my adventures while they were happening, that I have been a regular user ever since.

The other material we covered this week went a long way to help me bridge my mental gap between looking at diagrams and imagining the user-end of a database.  I will be the first to admit that spatial skills are not my strong suit (talk to any of my booksellers who have ever helped me move tables in my store -- I can't see how something is going to look until I actually put it there!).  This visualization struggle is part of why I found MIS531 so very challenging.  I could either think in terms of the ER diagrams or the tables, but synthesizing the two caused me to feel like I was trying to mix oil and water.  The part of that class that made the most sense to me was normalization, and I spent hours on that part of my group's project (along with the data dictionary, which is updated with a near religious fervor as we worked toward our deadline).  Normalization felt comfortable, like solving for x in Algebra or balancing equations in Chemistry or even transposing music into a different key.  I blazed through that assignment and that portion of the project, feeling triumphant that after weeks of struggling, something finally clicked for me.

Since reading chapter 1 in the textbook last week, I've tried keep in mind the distinction the authors make about dimensional modeling and 3NF modeling:

"3NF modeling is a design technique that seeks to remove data redundancies.  Data is divided into many discrete entities, each of which becomes a table in the relational database....  Both 3NF and dimensional models can be represented in ERDs because both consist of joined relational tables; the key difference between 3NF and dimensional models is the degree of normalization."

(Kimball & Ross, 2002, p. 11)

This bit of information not only helped clarify the difference between what I'm learning now and what I learned last fall, but it also kept me from breaking out in a cold sweat when I read the directions for this week's assignment.  The four-step design process is straightforward, and really helped me to focus on the business process at hand instead of worrying about connecting the dots between diagrams and tables.  The end result is that I feel as though I learned something; my star schema may not be perfect, but I feel like I understood the purpose of the assignment and at the very least, like I'm running in the right direction.