Personal life.

Part 1.

Data is all around us, in both our professional and personal experiences. During our discussion topic this week, we will be exploring different types of data that you encounter in your everyday lives, whether it’s at home or on the job.

Respond to the following in a minimum of 175 words:

  • Discuss the differences between quantitative and qualitative data, as well as the advantages and disadvantages of each. As a part of your response, describe one type of quantitative data and one type of qualitative data that you encounter in your professional or personal life.

Part 2.

Respond to the following thread in a minimum of 100 words:

Team,

I really like this section of our Reading for the Week. Data is such as fascinating subject and we all have the good fortune to be apart of “Big Data” on a daily basis in our lives. Do you subscribe to a social media platform like Facebook or Twitter or Snapchat? If “yes”, then you are directly participating in Big Data and your information/behavior/interaction with or without others is collected, analyzed and there is a response that is delivered back to you based on your behavior. That response from the platform can be something as simple as getting a friend recommendation or having an Ad placed on your page. Whether you know it or not (or like it or not), Big Data is (in my opinion) the biggest driver in the American Economy. Our ability to take data, format the data, measure it and then infer a conclusion on the behavior of the data…….the economy moves forward! I can pull a million more examples of Big Data, such as when you go to the local grocery store, all the information of your purchases are recorded and

Like our book states, Data is simply information (numbers/facts/moments) that are collected, stored, analyzed, measured and then an inference of behavior is created in order to determine how the sample population is behaving. Once the researcher infers (what they believe) a behavior from the sample population they can either prove or disprove their theory (hypothesis).

There are 3 categories associated to Data –

  1. Predictive Data Analytics – Forecasting tools are used to predict how a sample population is going to behave based on past precedent. A classic example is forecasting stock prices or predicting consumer behavior
  2. Descriptive Data Analytics – Simply put, it is a numeric that represents a variable. Your book states that the world Population is 7-Billion in 2015…..this is descriptive. Or gas prices today are $3.50 per gallon
  3. Prescriptive Data Analytics – Like your book states, it is a methodology that seeks to make decisions (prescription) based on Descriptive data. The most classic example is the U.S. Census. Census Workers go from door-to-door asking people in households questions such as “how many people live in your home?” “What are their ages?” “What are their gender”. The answers to these questions provide the U.S. Government with the information they need in order to provide services that will support the Constituency in a particular geographic region or area.

Of the 3 types of Data Analytics, which one do you find the most interesting and why?

Part 3.

Reply to the following classmate in a minimum of 100 words:

“The difference between quantitative and qualitative data is numbers versus words. Working in callcenters everything is measured on consultants and customers. Qualitative data can be used for various reports surrounding employee and customer service. This can be done through words and phrases customers and employees use to identify opportunities to improve processes. An example is if words “rate is too high” consistently from numerous customers, the company may evaluate the rates to create a better customer experience. Quantitative data is heavily used in callcenters to monitor employees average handle time on calls, service levels for calls, and performance metrics overall.

A manager can coach an consultant to success by sharing the different categories that make a successful consultant by combining both reports that share what success looks like. An example from previous experience was consultants believed that it takes longer on the phone to have a satisfied customer. Through data we were able to determine that shorter call times, plus excellent words in customer service is what made a better experience for the customer. We learned that longer call times usually was associated with more confusion by the consultant and customer and a less satisfied experience”. – Julie B.