Subject Code: BUS105
Subject Name: Business Information Systems
Assessment Title: Individual Assignment
Total Marks: 20
Due Date: Monday 18th Dec. 2017, 15:55pm AEST, using Turnitin (week 6)
Title: Big data and the airline industry
Length: Maximum of 1000 words
Software required: Microsoft office, i.e. the assignment is to be completed in Word.
Submission: Each student will submit their assignment along with a coversheet (see Portal) via the Turnitin link on the Portal.
The assignment has several sections shown below. You will be required to read relevant articles (some of which are provided) and write at least a paragraph for each answer. The report should be in your own words, and supported by the literature. A minimum of five (5) references should be listed, and Harvard referencing should be used.
Background: The airlines industry has always had a combination of sophisticated information systems and large amounts of data. Big data methodologies are allowing airlines to collect and analyse data in real-time, and therefore enhance customer and staff experience in a timely manner. Flights can be made safer and smarter with new tracking technology and pilot reports, highly personalised offers can be made to customers based on their purchase history, speech recognition tools can help staff understand their customers, and customers are now able to track their own luggage.
The aim of this report is to introduce the idea of big data and associated technologies, identify how these are changing the nature of the airlines industry (by researching the articles attached, including examples, and including ideas from any additional articles that you have found.
Part A: Introduce the idea of big data and associated technologies. (100 words)
Part B: Explain how big data is currently changing the airlines industry, and thereby improving customer experience. See for example, the articles in the first section below (400 words)
Part C: Analyse and discuss some of the challenges for airlines when transforming to a big data approach. (See for example, articles below under the heading “Challenges related to data management and transformation for the airlines industry”) (150 words)
Part D: Make some recommendations regarding how airports could overcome the challenges in Part
C. (100 words)
Part E: Discuss how big data and new technology may be used to design airports of the future. (See for example, articles below under the heading “Airports of the future” below) (200 words)
Part F: Summary of the report. (50 words)
Part G: REFERENCE LIST
References to help you:
Challenges related to data management and transformation for the airlines industry
Airports of the future
Students must submit their individual analysis via Turnitin on Monday of week 6 (Monday 18th Dec.) 15:55pm AEST.
This file must be submitted as a ‘PDF’ document to avoid any technical issues that may occur from incorrect file format upload. Uploaded files with a virus will not be considered as a legitimate submission. Turnitin system will notify you if there is any issue with the submitted file. In this case, you must contact your lecturer via email and provide a brief description of the issue and a screen shot of the Turnitin error message.
Students are also encouraged to submit their work well in advance of the time deadline to avoid any possible delay with Turnitin similarity report generation or any other technical difficulties.
Late assignment submission penalties
Penalties will be imposed on late assignment submissions in accordance with Kaplan Business School “late assignment submission penalties” policy.
Number of days Penalty
1* - 9 days 5% per day for each calendar day late deducted from the student’s total marks
10 - 14 days 50% deducted from the student’s total marks.
After 14 days Assignments that are submitted more than 14 calendar days after the due date will not be accepted and the student will receive a mark of zero for the assignment(s).
Note Notwithstanding the above penalty rules, assignments will also be given a mark of zero if they are submitted after assignments have been returned to students
*Assignments submitted at any stage within the first 24 hours after deadline will be considered to be one day late and therefore subject to the associated penalty
If you are unable to complete this assessment by the due date/time, please refer to the“ Special Consideration Application”, which is available at
General Assessment Marking Rubric below (Also in turnitin)
(25%) Understanding of the project
Key points left out. No grasp of the idea of big data or challenges. Includes some issues but analysis glossed over. Includes all issues, analysis and
but with little elaboration. Not completely justified. Builds convincing argument showing how key issues,
analysis and recommendations are integrated together. Builds convincing argument showing how all key points are integrated together. Uses examples to elaborate the key points about big data innovations and challenges.
Research and evidence (25%) Evidence of analysis and 3rd party support No references to any sources. Some discussion and analysis shown, however use of references may be moderate. Very little
justification of ideas and minimal research and/discussion evident. Background research and analysis of big data is clearly identifiable. All sections of report present. Evidence of extensive research and analysis:
journals, verified websites, primary research. Used references to justify analysis,
recommendations and other parts of the report. Application of big data to future airport design clear. Able to use references to back up analysis and other sections of the report.
Recommendations clearly come from the analysis of big data challenges. Application of big data to future airport design clear and engaging.
Synthesis and conclusion (25%) Recommendations Recommendations missing Recommendations
very general in nature and not necessarily easy to implement and/or not related to the rest of the report. Recommendations given in a way that they could be
partially related to the challenges of big data. Recommendations
are practical, feasible and are related to the challenges of big data. Recommendations
can easily be implemented and
are clearly related to the challenges of big data.
(25%) Organisation Introduction missing or underdeveloped and/or no clear summary.
Reader has no idea what the assessment is about.
No structure to the assessment.
Reader cannot follow sequence. Ideas not focused. Reader may have
difficulty following arguments.
Main points generally present, but could be difficult to identify.
Weak discussion of ideas. Summary brief and doesn’t tie in with what was introduced.
Provides purpose for assessment, highlights and interprets key issues, however report not integrated.
Main ideas presented in logical manner. Flow of report adequate. Main ideas presented in logical manner. Flow of report smooth.
Shows consistency between big data challenges and other parts of the report.
Engaging report. The purpose of the assessment is clear in all stages.
Summary is clear and relates back to the other sections. Ideas clearly organised & logical so the reader can follow easily.