MIS 587 - Blog 5

 

MIS 587 

Blog IV

Now that we have completed the learning modules for MIS 587, I can say that this course has genuinely piqued my interest in business intelligence and other concepts related to network science. I found that many of the courses in the MIS program have emphasized the affects and importance of big data, but I previously had little knowledge on how to analyze that data and transform into intelligence or gain insight from that data. However, I now have a far better understanding of these concepts and see how business intelligence is a vital part in helping organizations decipher to endless amounts of data out there in the real world.

The first module introduced the concept of Big Data, and helped to put the size of Big Data into context. One of the interesting slides that stood out in the initial lectures was the comparison of a 11oz cup of coffee and the great wall of China. In this comparison, the visualization states that by 2015, the world will reach a Zettabyte threshold of annual global IP traffic, which metaphorically compares that if the volume of an 11oz cup of coffee were to be 1gb of data, a zettabyte of data is equivalent to the volume of The Great Wall of China! It’s an interesting comparison that puts context into how much data has exponentially grown across the globe. Although this visualization is likely now obsolete, I’m interested in how this comparison can be made to how much Big Data volume will exist in the near future. 

The early modules also introduced and defined Business Intelligence, which according to Professor Ram’s lectures is defined as the following: Business intelligence (BI) is the applications, tools, technologies and techniques used for gathering, storing, and analyzing data to provide actionable insights that will help organizations make valuable business decisions, to measure and manage their performance, and to continuously innovate. These modules also described the Business intelligence Life Cycle (BI Life Cycle) as shown in the screenshot below. 

The business intelligence lifecycle is generally iterative, meaning that the steps follow a sequential order, but in real life usage, it’s common to return to different parts of the cycle to make business intelligence fit the needs of a particular organization or analysis.

Module 1 lectures also included topics on Performance Management, a key part of why business intelligence is important and vital in the competitive business landscape today. One of the key terms was the Balanced Scorecard, which is a performance management framework that links organization goals to the overall strategy. It focuses on 4 areas: customer, financial, internal business, and learning growth. The balanced scorecard also maps these to relevant key performance indicators and metrics, which we can obtain through analyzing data. 

A great example of the balanced scorecard is from the Southwest Airlines BSC.

 

Here we can see how Southwest has organized their strategies and objectives, along with how they measure these factors and what their targets are. The targets are then linked to specific initiatives designed to improve their overall business strategy and operation. 

One of the assignments for these beginning modules focused on case study analysis of Bird Strike Data from the FAA. We then used Tableau to analyze the data and create a visual dashboard that would be incorporated into a report for the FAA. I found this assignment to be a bit challenging at first, as the data provided ample amounts of information that could be used in analysis, but often this the data’s usefulness in analysis would be dependent on what questions I wanted to answer. Ultimately, this tested being creative and figuring out how to the use the data to present a comprehensive visualization to answer questions I thought the FAA would care about understanding from these Bird Strike Data. 

Module 2 introduced the class to topics in Web Metrics and Google Analytics and discussed how powerful these tools are in helping online websites collect metrics and other demographic information. For example, Web metrics can be used to track the number of visitors to a webpage over a specified time, and from their, this data can be segmented into even more specific partitions, such as where these visitors are coming from, what device/ operating system are they using, what languages they speak, and more. 

Web analytics are also an important tool in deciphering the web data and determining KPI’s and other valuable metrics. Web data can allow an analyst to determine the conversion rate for their website, or in other words, the proportion of visits that result in achieving a particular goal for a website. In the lecture on web metrics, some of the goals for conversion rate can be achieving a certain number of clicks, achieving a number of new users, measuring purchases, and more. 

The assignment for Module 2 allowed students to select an organization and obtain their Google Analytics profile. I wanted to use a personal site to gain insight into more relevant details pertinent to my life, but I unfortunately don’t have access to these resources as of yet. Luckily, Google Analytics provides a sample Google Analytics profile based on their online merchandise store, which teaches users the basics of Google Analytics. 

 

I particularly enjoyed this assignment the most, as I find I can likely incorporate web metrics into my own personal usage in the future and use the Google Analytics tutorials to improve my understanding of the tool. My family owns and operates a small local business, The Donut Wheel, located in Tucson, Arizona. Although we currently do not have a website, I’m considering working on creating a webpage for our business as a potential project in the future. I’d love to create/ design a website for our business and implement a tool such as Google Analytics to understand the website visitors and derive tangible metrics from this data. In particular, I'm wondering how I can use web analytics to understand our customer base, and perhaps even attract new customers over the web. 

I personally felt that learning about Performance Metrics and creating dashboards in Tableau were good steppingstones into understanding how to use Google Analytics, as Google Analytics are presented in visual dashboards. In order to correlate the analytics data to business requirements, one should first understand how these apply to key performance indictors and other areas in business strategy. 

 

Module 3 focused on Social Media Analytics and Network Analysis, introducing students to network science terminology, how to visualize networks, how to quantify network metrics, and how to analyze networks to detect communities and interpret the network data. 

According to the class lectures, networks are defined a collection of entities and the relationships among them. These lectures discussed that a network is represented by two main components, the nodes (vertices) and edges (links) and can be used to represent virtually everything that has a relationship to one another. 

Networks can become more and more complex as a result of the data and what the analyst aims to interpret from the data. For example, networks can include other properties, such as directionality or weight. From here, we learned about another tool for network analyzation: using visualizations to interpret network layouts. Network are categorized into different layouts, such include force direct, geographical, circular, clustering, and hierarchical layouts.

The lecture on Networks also emphasized that visualizations are not the only method of analyzing networks, as it is often just as important to include quantitative analysis when reviewing networks for patterns.

Networks have different structural properties, and these can be measured using different forms of centrality (Degree, Betweeness, Closeness, Eigenvector).

 

 

Quantitative network measures also allow for measuring network density, reciprocity, clusters, distances, and help in identifying key players in a network. 

It’s quite common to easily portray how networks may appear to share some similar qualities, but, are vastly different. This is the case example of patents by inventors for both Apple and Google. In regards, to these organizations, they are both large technology corporations that have created large amounts of US patents over their organizational history. However, when implementing the patent creation data for both organizations into a network map, we can see how their methodologies are quite different. In the case example’s network map, Google has small nodes represented as smaller teams of inventors creating patents while Apple has larger nodes at its core representing famous Apple inventors.

For this module’s assignment, we used Gephi to analyze social network data for Fortune 500 organizations. Similar to past assignments, this assignment required the student to think critically and creatively using the ample amounts of data provided and combine their analysis into a report on their findings. Although it was fun to interact with Gephi, this assignment proved to be quite challenging. 

I personally did not find Gephi to be intuitive or user friendly, and not having the option to “undo” a selection greatly impaired my workflow. I’m sure there are many other network analysis tools that have similar features to Gephi, but I appreciate that it is a free and open-source application, making it easy to access to those who want to learn about network science and creating network visualizations. 

After completing the course modules of MIS 587, I feel I’ve gained confidence in these business intelligence tools and concepts. I have a far  better understanding of business intelligence and it’s importance compared to 8 weeks ago, and I genuinely hope to apply my newfound knowledge in a professional setting in my future career path.  

Thank you Professor Ram and Yuanxia for their contributions to this course. I also thank you for sharing your comments and connecting with my class interactions over these past few weeks, and best of luck to you on the remainder of your courses!