- Overview and Purpose:
Research a current topic on Data Analytics that we have not covered extensively in class. You will create a write-up, and the purpose of this assignment is to give you an outlet to display your ability to understand and describe an aspect of data analytics to your current and future employers.
- Big data
- Distributed data technologies (i.e., Hadoop)
- Natural Language Processing
- Text mining and sentiment analysis
- Virtual data cubes and/or in-memory analytics
- The Internet of Things (IoT)
- Cloud-based analytics/Cloud computing
- NoSQL databases
- Deep learning
- Artificial Intelligence
- Social network analysis/analytics
Your write-up should be between 300 and 400 words, not including references.
You should cover the following points in your writeup:
- A brief overview of the topic (i.e., what is it and why is it important).
- How the topic relates to the material we have covered in MIS2502. How does it build on the concepts covered in the course? What are the related topics in MIS2502?
- An example that describes how this tool or technique has been applied in practice.
- Citation Guidelines: If you use materials (text, figures, data, etc.) in the write-up that was created by others, you must identify the source and clearly differentiate your work from the materials that you are referencing. Failure to do so will be considered plagiarizing. There are many different acceptable formats that you can use to cite the work of others. The format is not as important as the intent. You must clearly show the reader what is your work and what is a reference to someone else’s work.
Big data and new data collection and analysis techniques are providing unique insights for people, businesses, and governments across the world. Data professionals in all fields are working tirelessly on finding the tech world’s next big breakthrough. Recently, insurance companies have begun to use telematics devices for more advanced and detailed data collection.
A telematics device is a data collection device, usually provided by your insurer, which transmits certain data to your insurance provider for advanced analysis on your driving behavior. By implementing these devices, insurers are better able to assess the risk tolerance and expected loss for insuring a particular driver. One of the driving principles of insurance is uncertainty. Customers and insurers alike are uncertain about the amount of losses they may occur. Collecting more advanced data such as average speed, acceleration rates, total driving time, and other metrics allows insurers to perform complex data analysis, pool similar drivers together, and more accurately calculate fair premium rates for their customers. Telematic devices can be advantageous for consumers as well. These devices, if used effectively, can lower insurance premiums and encourage drivers to take fewer risks or avoid risky behavior in general when driving. Furthermore, insurers may even be able to create new risk pools with lower overall premiums for high performing, telematic-using customers.
MIS 2502 explores the major principles and processes of data analytics through exploring traditional data analysis (SQL, NoSQL, MongoDB, Tableau, etc.) and data mining (Decision Trees, Clustering, and Association Rule). Some of these techniques would surely be used to help determine the premium rate set by insurers using telematic devices. Data analysts would use some sort of querying language to pull out the data they would like to use to run an analysis. Then they would transfer it into a digestible format for the analytical data store. Once stored, they extract the data and run some sort of mining analysis. In this case, analysts could use decision trees to determine if drivers will or will not exceed the loss amount that would be covered by their current premium. If they determine a driver will not exceed projected losses, the insurance company may then be more confident in lowering the premium rates for their customer.