Fluent Essays

Attached The reading and video this week were about business intelligence in the airline industry.  Both remained at a relatively high level to show the types of data and information that might support business decision making in that industry. The topic for discussion this week is business intelligence in a variety of companies and organizations. The initial group should research a concrete example of business intelligence in a specific company or organization.  The company or organization can be a for-profit corporation or small business, a non-profit, or government agency. Ideally, your post will address some combination of the following questions (NOT necessarily ALL): · What problem is business intelligence helping to solve? · How is business intelligence used in practice? · What were (are) the results (even preliminary or anticipated)? · What data was used? · What are the technical details (vendors, platform used, etc.)? · Were there any specific challenges to be faced? · Anything else of interest? You may choose a company in the airline industry, but do not choose the airline industry in general.  Do not review business intelligence at a generic industry-level for any industry. Ideally, we will have different companies/organizations from EACH of the members of the initial post group.  However, I will not enforce this as a rule if you post by the initial post deadline of Friday.  If you post later in the Week, Saturday or afterwards, you MUST check to verify all companies/organizations posted by Friday and you may not duplicate the same company or organization. Please include the name of the company or organization clearly in the Title of your post.   As a reminder, initial posts are expected to be well-written with 250 words minimum.  You will need to cite sources in this initial post and APA format should be used. You will find business intelligence/big data analytics implementation cases described in the book Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results by Bernard Marr.   The book can be accessed via the IFSM 330 course under Content - Course Resources - eReserves.  If you choose to synthesize a case study from the book you must also find at least ONE additional recent Internet source on BI/data analytics within the same company or on teh same topic to include in your post (APA reference required for both the Bernard Marr book and additional resources). Please be aware that this book is part of the ProQuest ebook collection.  We have unlimited access to the book to read online.  There are also options to download and print PDF versions of chapters.  However, students are limited in terms of how many pages you can download as a PDF.   The chapters are short, so if you would like to read the chapters online, there are no restrictions.  If you would like to download and/or print the chapters, I would suggest that you first read the chapter online to be sure you want to use a particular chapter (case study) in your post. You may certainly reply to posts, but this is not a discussion topic so much as a 'mini-research' post.  You will be evaluated only on your research post.  It is helpful to have all posts in the discussion area so we can benefit from everyone's work. This is NOT a 'group approach' discussion topic.  All students are required to submit a 'Research Post' this week. Before embarking on this post, be sure you've reviewed the readings from last week and this week.  The Tutorial and Update Tutorial on Big Data Analytics: Concepts, Technologies and Applications. It is well documented that the analytics job market is booming, but not particularly organized. Job titles can vary widely, as can requested skills, degrees, certifications, etc. There are also subtle differences between data science, data analytics, business analytics, etc. You have two research options this week, OPTION 1 involves an attempt to synthesize a few articles/posts on the issue--3 citations minimum. Pay particular attention to WHEN articles or posts were published because all these job characteristics have been a moving target for several years.  This does not necessarily mean that you need to ONLY focus on content from the last 6 months (or 6 days), but when you compose your initial post, it's important to acknowledge any differences/discrepancies between content from 2014 and last week. Alternatively, for OPTION 2, you might consider looking at actual job postings in analytics.  If you choose this route, do not stop at the first 3 job listings you find.  You should reference at least 5 job postings.  You may want to limit your search to entry level jobs (using keywords like 'junior' or 'entry level').   All the 'usual suspects' in terms of job boards are fair game, such as  indeed.com  or  usajobs.gov  for government jobs.   If you want to cast a wider net, check out this blog post on  25 websites to find data science jobs  (you can also search for 'analytics' on any of these sites).  Also consider looking at UMGC's own  Career Quest  website. Again, I'm going to leave this research post open ended so we can encourage a variety of perspectives.  Please do not restrict yourself to the first page that Google shows you.  If we all do that, we'll end up with a limited perspective on this question of the job market in analytics. Different kinds of questions you might address are (you are NOT required to address ALL these!): · In terms of the Watson's Tutorial (2014) and Update Tutorial (2019) on Big Data Analytics: Concepts, Technologies and Applications, have Watson's observations on data analytics skills and job categories from 2014 remained valid? · What is the latest growth projections for the job market in analytics? · Has there been any standardization in analytics job titles? · What are some of the fundamental skills required?  This could be technical or not. · If technologies are mentioned, which ones? · Are there any recently emerging (hot) skills? · What about degrees, certifications? · Are there specific industries which seem to be hiring more analytics specialists than others?  Specific geographies? · Again regarding Watson's Tutorial (2014) and Update Tutorial (2019), the author mentions privacy issues in both.  Any sign of privacy as a theme/skill/specialization in any of the articles or job postings you've reviewed? · Keep going, don't limit yourself For Option 1: You should have a MINIMUM of three sources (citations and references in APA format) in your initial post.  For Option 2: If you reference an article, you should have a citation and reference in APA format.  Citing a job announcement can be tricky (plus 5 are required and I don't want SO much time spent on citations), so APA format is not required, but please include a URL for each job announcement (either within the text or at the end of your post). For both OPTIONS, initial posts should be 250 words should be a minimum. References Watson, H.J. (2014). Tutorial: Big data analytics: Concepts, technologies and applications. Communications of the Association for Information Systems, 34(Article 65), 1247-1268. Watson, H.J. (2019). Update tutorial: Big data analytics: Concepts, technologies and applications. Communications of the Association for Information Systems, 44(Article 21), https://doi.org/10.17705/1CAIS.04421 **NOTE: Please read the article  Google Ngram Viewer Possibilities Limitations before your initial post or reply post.  The question below regarding 'computer assisted reading' does NOT refer to reading on a computer monitor NOR does it refer to a computer assisted voice actually reading a book.  'Computer assisted reading' refers to the computer ACTUALLY DOING THE READING, THE THINKING, THE ANALYSIS that normally human beings would do, while reading a book or article. In week 3, we used Google’s NGram viewer to launch a discussion about terminology in the course: business intelligence, data analytics, data science.   It also gave us the opportunity to introduce some data visualization into the course.  In this week’s discussion, we’ll be returning to Google’s NGram, but from a different angle, from the angle of data collection and what is the impact of analyzing data using data analytics tools.   As a starting point, we’ll read an article found under Content: “Google NGram Viewer: Opportunities and Limitations”.  Also available here: Google Ngram Viewer Possibilities Limitations One important learning point from this week’s discussion is to internalize that data is NOT just numbers, it’s also words (yes and counts of words, which are numbers).  But the meaning and evolving meaning of words are also data. As for the topic or question(s) this week for the initial post group, we’ll keep it somewhat open-ended, but based on a few assertions and questions posed by the authors of the article.  First of all, from the very first sentence of the article, the authors announce their intention to provide “a basic introduction into the possibilities of computer-assisted reading.” Computer-assisted reading?  Is that what we’re doing, reading, assisted by a computer? By the end of the article, they make another assertion: “Digital methods can allow us to make observations about vast numbers of texts. Far more than you would be able to read yourself.”   Further they state, “That last phrase should cause some alarm: we haven't actually read any of these texts, but we are making observations about them nonetheless. We hope you will think deeply about the implications about such an act.” Broadly, for the initial post group, there are three options for your initial post.   As in other weeks, initial posts should be a minimum of 250 words.  If one of the options below don't 'inspire' you to write 250 words, then choose a different option OR choose to respond to more than one option in your initial post. OPTION 1: And now three questions, again from the authors (if you choose to address these questions, please take them in a group, if you address the question of what this form of reading ‘loses’, please also try to brainstorm on what this form of reading ‘gains’ AND how we might maximize gains and minimize what is lost): · What does this form of reading lose? · What does it gain? · How it can it be approached in ways that minimize the former and maximize the latter? OPTION 2: Of course, we might begin with the question above (if you choose to address this question, you must, at some point, address, with external research, what it means ‘to read’): · Is that what we’re doing, reading, assisted by a computer? OPTION 3: Finally, read the final section, the final paragraph on Interpretation.  I won’t quote the author’s directly here, but there are a few interesting assertions there, worthy of comments, questions and discussions. For reply groups, please do not be satisfied simply to ‘react’ to the initial posts.  There’s a lot of interesting room for discussion here, in particular about the open-ended section on Interpretation.  If the initial post concentrates on one phrase in that section, but actually you were more struck by a different passage, sentence or assertion, by all means, say so and elaborate.