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Blog 5 | Summary and Reflection of Class

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Introduction This final blog summarizes the work that we have done during the past eight weeks. The course introduced Business Intelligence techniques, and we gained exposure to analytical tools that people in everyday organizations use for decision-making. The class was divided into four modules and the assignments for the modules maps nicely into the six learning outcomes for the course. I’ve listed each one below and described how my experience with the assignment in this class helped to achieve the intended learning for the class. Learning Outcome 1 Articulate the Characteristics of Big Data beyond the 5 Vs and explain why it is causing a paradigm shift. | I remember three Vs: volume (amount of data), velocity (speed of data), and variety (range of data sources and types). The paradigm shift relates to the datafication and how people are leaving their digital fingerprints in everything. It’s happening so rapidly – feedback is immediate. A mishap on social media can go viral within
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Blog 4 | Week 6 | Social Media Analytics and Network Analysis

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  Introduction Whenever I hear about networks, I immediately think about a nature video that I saw on mycelial networks and their symbiotic relationships. It is the plant version of social networks. It's so enthralling that Star Trek has an episode of traversing through the fungal network--a concept encompassing beyond the 4IR fusion of physical, digital, and biological spheres to include navigational travel?!?!  Here is the  YouTube video  for anyone that wants to take a quick break and understand how to get an all access travel pass through "the network." About Module 3 Module 3 starts with an Intro to Networks, then details to Network Visualizations and Quantitative Network Metrics. I assume Community Detect and Interpretation is next. Lecture 11 begins with the understanding that networks consist of nodes (vertices) and relationships (edges), which can be single or two mode. Edges can be weighted/unweighted and/or directed/undirected. And social media is based on ego
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Blog 3 | Week 5 | Web Analytics

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Introduction This blog covers topics discussed during week five, which relates to web analytics, Google Analytics, and web metrics. Complementing materials and readings will be included in each section as well as my thoughts on how they relate to lectures and assignment three. Choosing an Organization The organization that I chose to investigate was the Center for the Integration of Research, Teaching, and Learning, or CIRTL ( pronounced as  sir-tuhl ). It doesn't have its own site, rather it has 16 subdomains on the Academic Affairs website. I used a custom dashboard to look at just the CIRTL pages and also used Google Analytics to see the relationship CIRTL has with its parent organization. The CIRTL program is relatively new to UArizona (in its second year) so it was a good one to pick to see progress between year one and year two.  Google Analytics I build websites and am a little familiar with Google Analytics. I've never used Adwords before because my websites are for non
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Blog 2 | Weeks 2-4 | Data Warehouse Design

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  Introduction This blog covers topics discussed during weeks two through four, which all relate to data warehouse design. Below is a list of each topic and summaries of my understanding for each. Any related materials and readings will be included in each section as well as my thoughts on how they relate to lectures.  Data Warehouse Design Cycle . In a nutshell, this is about how data from  OLTP (or several transactional sources) is grabbed and processed for the the OLAP, with lots of ETLs in between. Both the OLTP and the OLAP have important roles in the data warehouse ecosystem--the OLTP is record-oriented while OLAP has aggregated type of queries. Kimball breaks down the cycle to four components and refers to it as the DW/BI Architecture. In addition to the OLTP (Kimball calls these source systems), the other components are the ETL systems, data presentation areas, and BI applications. The first step in designing a DW/BI is to consider the needs of the business.   Balance Scorecard
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Blog 1 | Week 1 | Introduction to Business Intelligence

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Week one lectures introduced us to Big Data and Business Intelligence. The term ‘big’ is described by volume (amount of data), velocity (speed of data), and variety (range of data sources and types); there is a lot of information ubiquitously floating everywhere. How we digitally exist gives organizations an opportunity to learn patterns on who we are and calculate the probability of how we’ll act. The case studies in Big Data Gets Personal discussed how only .5% of what we share over the Internet is harvested by organizations, and the rest remains unstructured and untapped. The process of leveraging useful data is where Business Intelligence comes in. In the McKinsey Global Institute white paper, it emphasizes that the “physical and digital worlds are converging.” Almost everyone in the world uses social applications, and organizations are creatively finding new ways of engaging their stakeholders within these apps. Even the simplest idea of datafying ‘likes’ on a product will help
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About Me | Kat Francisco

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Hello Colleagues,  My name is Kat Francisco, and I have worked at the University of Arizona for nearly 15 years. I'm from the Bay Area, California but now call Sahuarita, Arizona home with my husband, two boys, and cat. My kids play baseball and piano, so when I'm not at tournaments, games, and recitals... well that's pretty much where I am at, so I really appreciate the days when nothing is on the calendar. I do make time for classes, faculty/staff intramural softball, and I crochet. If you happen to be on campus and see a crocheted cactus, it's probably one that I made. 🌵 My job at the university is nontechnical in theory, but as we quickly learned from this class, one needs data to process, interpret, and make informed decisions--for any job, really. In my position, it's more about using information as evidence, like proof that our instructors have the proper qualifications to teach and that those instructors continue to be productive. Another example is to
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