Responding to Module 4 discussion posts

Recent Trends in Business Analytics

  • Do some research on recent trends in business analytics such as Google Analytics, cloud computing, artificial intelligence, machine learning and so on.
  • Focus on one trend and discuss its relevancy to your business or any business you would like to discuss.
  • Consider how these analytics tools would impact business decision making.

Week 2: Respond to at least two of your classmates’ initial posts. Your response should be substantive and further the discussion. It is OK to be critical and use critical thinking. That means you can question what your classmates say. Do they provide their own critical thinking and logic?

Please provide at least 2 peer reviewed journals or articles in responding to the discussion post!

Minimum of 1 page response to each and please follow grading rubic and use critical thinking and logic!

Rubric Name: MBA/MSHRM/MSL Discussion Grading Rubric – Timeliness v1


Initial posting reveals a clear understanding of all aspects of the threaded discussion question; uses factual and relevant information; and demonstrates full development of concepts.

Initial posting demonstrates legitimate reflection and answers most aspects of the threaded discussion question; full development of concepts is not evidenced.

Initial posting demonstrates some reflection and answers some aspects of the threaded discussion question; Limited development of concepts is evident.

Initial posting was not on topic; the response was unrelated to threaded discussion question; and post demonstrated only superficial thought and poor preparation.

Responded to the required number of students and to the professor, if appropriate, for every discussion. Demonstrated analysis of others’ posts; extends meaningful discussions by building on previous peer posts and offering alternative perspectives.

Responded to almost all of the required students and to the professor, if appropriate, for every discussion. Provided comments and new information to other posts; not all responses promote further discussion of the topic.

Responded to some students and to the professor, if appropriate, for every discussion. Little depth in response; agreed or acknowledged one other classmate’s initial posting.

Did not respond to any student or the professor.

Refers to and properly cites either course and/or outside readings in both initial posting and responses to peers.

Refers to and properly cites course and/or outside reading in initial posting only.

Makes some reference to assigned readings with some citations or cites questionable sources.

Makes no reference to assigned readings without citations or cites questionable sources.

Demonstrates mastery conceptualizing the problem; viewpoints and assumptions of experts are analyzed, synthesized, and evaluated; and conclusions are logically presented with appropriate rationale.

Demonstrates considerable proficiency conceptualizing the problem; viewpoints and assumptions of experts are analyzed, synthesized, and evaluated; and conclusions are presented with necessary rationale.

Demonstrates partial proficiency conceptualizing the problem; viewpoints and assumptions of experts are analyzed, synthesized, and evaluated; and conclusions are somewhat consistent with the analysis and findings.

Demonstrates limited or poor proficiency conceptualizing the problem; viewpoints and assumptions of experts are analyzed, synthesized, and evaluated; and conclusions are either absent or poorly conceived and supported.

Initial post occurs in a timely manner (1 – 3 days into module) allowing ample time for classmates to respond and engage.

Initial post occurs later (4 – 5 days into module) allowing limited time for classmates to respond and engage.

Initial post occurs substantially late (6-7 days into module) allowing minimal to no time for classmates to respond and engage.

Initial post occurs after the first week of the module.

Overall Score

1. Module 4 Discussion


One of the recent trends in business analytics that I feel is relevant to most businesses is the use of Artificial Intelligence (AI). Artificial intelligence is the science aiming to make machines execute what is usually done by complex human intelligence (Ledied, 2017). This mean that AI is the use of complex computer programs to perform calculations and predictive analysis instead of using humans. AI is used to predict future trends and sales forecasts, and its use by various types of businesses and organizations is increasing. AI has allowed businesses to move from static, passive reports derived from historical events, to proactive analytics that allow the business or organization to monitor sales and trends on a real-time basis (Ledied, 2017). They are able to receive alerts and notifications when they are not meeting their goals or if something unexpected occurs. The use of AI gives businesses and organizations the ability to react on a day-to-day basis due to the real-time information it provides, and it gives them the ability to more accurately predict the future needs of their organization (Oswal, 2018). AI also gives the business or organization the ability to forecast future trends based off of information that would have seemed irrelevant in the past. AI can look at data that may have been ignored or not considered by humans when running an analysis. AI is also capable of incorporating larger amounts of data than a human could when predicting future trends and events. Another reason that AI is beneficial to businesses is that it saves time. AI programs can gather information, run an analysis, and make predictions in fractions of a second. These programs can also give up to the minute information, which is not possible when trying to be accomplished by humans. This technology is relevant to my line of business because it helps predict the future needs of my organization. It can determine the number of people needed for a project, the budget allowed for that project, how much time it will take to complete the project, and the materials necessary for the completion of the project. This gives my organization the ability to create better timelines and budgets and it helps determine how many people my organization will need to hire to accomplish future missions. AI is used, and will continue to be used, to track and predict sales, the amount of revenue received, and personnel and material required for future projects. It can also help predict which locations will perform the best and if an organization should expand into different locations. As the capability of AI grows, I feel that businesses and organizations will rely on it more and more to provide the data and analysis they require to operate on a day to day basis.


Ledied, M. (2017, December 13). Top 10 Analytics and Business Intelligence Trends for 2018. Business Intelligence. Retrieved from

Oswal, N. (2018, February 19). How AI is Transforming Business Intelligence. Retrieved from

2. Machine Learning – Time Series Forecasting

Contains unread posts

posted Apr 12, 2018 9:10 PM


Time series forecasting is a really interesting topic for me primarily because my role at work is intertwined with both short-term capacity analysts and long term forecasters who use time series forecasting. As my employer proudly advertises, they are always looking at ways to continuously improve processes. As part of my team’s initiative over the next years, I’m helping to guide my team into utilization into a new programming language called R in the hopes of using this coupled with machine learning to improve time series forecasting for our department. As we use this programming language, one of the interesting parts of this is the automation of some roles within my company. Without going into much detail, automation is playing a huge role in corporate America and Machine Learning is playing a large part in this. There aren’t any areas that come to my mind that aren’t candidates for automation with machine learning.

One of my co-workers recently informed me that he designed a new long term time series forecasting program that ran multiple types of forecasting methods and plotted them against each other to see which was the most accurate. His next steps were to automate this process using machine learning so others could use this as well. The interesting part here is to see if there will be less of a need for human hiring vs machine learning curators overseeing this type of work. I think these types of machine learning tools would be very helpful in the longevity of a company in predicting certain types of oncoming events that normally take many hours of work.

An interesting thing in the future will be to see how well these machine learning methods compare to long term forecasting methods. In my research, one of the noted limitations is that machine learning for medium and long-term forecasting doesn’t exists, where it only exists one step ahead of short term forecasting (Massimakopoulos, Makridakis, & Spiliotis, 2018). What I think in this realm will be helpful is to be able to see how much “faith” is put into these types of environments, where humans used to be the end-all decision makers. I think going forward one thing I will also be interested in seeing is how much decision making will these machine learning methods be able to make without human contribution. I feel like these tools would impact business decision making in-so much as the accuracy would warrant – if there wasn’t a great deal of accuracy, then I don’t foresee these impacting decision making tremendously at all.


Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and machine learning forecasting methods: Concerns and ways forward. PLoS One, 13(3) doi:

3. “AI” obviously would be at the top of the list when it comes to business analytics. But I’m a huge fan of The Terminator series, and we all know how serious Skynet is in the movies. I am, however, interested in Natural Language Processing. According to Lebied (2017), “It is based on linguistics and deep learning, a type of AI that works with pattern recognition to improve the program’s understanding by analyzing massive amount of data to find correlations that are relevant.” (Business Intelligence) I’m a firm believer in connecting people and making sure everyone has a job. I find it disturbing that someone would have a computer do the job a human could do, just to save the company money. With the NLP, whichever business elects to incorporate this branch of AI into their business, would be opening the gates to better understanding of the foreign partners. It’s understood different cultures and languages have different styles and meanings of speaking. For example, someone from Colombia speaking their native language, may speak with a different level of passion within their sentence. Who is to say someone in America would not be able to pick up on that passion? With the implantation of NLP in some type of system like Skype or something, there would be a better understanding between the language barriers.


  1. Lebied, M. (2017). Top 10 Analytics and Business Intelligence Trends for 2018. Datapine.

Retrieved from

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