NSG 3039 Discussion Relying on Data
NSG 3039 Discussion Relying on Data
The discussion assignment gives you a chance to talk about important subjects for this week based on the course skills you’ve learned so far.
Make sure you submit your initial response to the Discussion Area before the due date specified for this assignment.
Use your course and text readings, as well as the South University Online Library, to help you with your research. Cite your sources in your work, as in other assignments, and offer references in APA format for the citations.
NSG 3039 Discussion Relying on Data
Start reviewing and responding to the postings of your
classmates as early in the week as possible. Respond to at least two of your
classmates’ initial postings. Participate in the discussion by asking a
question, providing a statement of clarification, providing a point of view
with a rationale, challenging an aspect of the discussion, or indicating a
relationship between two or more lines of reasoning in the discussion. Cite
sources in your responses to other classmates. Complete your participation for
this assignment by the end of the week.
Tasks
In order for data to be reliable there are several
conditions that need to be met—accurate, timely, and complete. Share an example
of when you had to make a decision using data that were not accurate, timely,
or complete.
When you’re running a business, your job can be boiled down to making a sequence of important decisions: should new products be launched? Should a department be disbanded? Should a marketing campaign be run? Every decision has risk and reward — will you make piles of cash or burn through them? It’s easy to believe that there is one lone right choice among a universe of disaster, and the job of the executives is to fine that one choice.
Every top selling management magazine will tell you that the gleaming savior of the 21st century is data. Sufficient data will keep you from making any bad choices. Store data on your customers, your product, and your marketing. Then hire some smart data scientists, and you soon will be freed from mistakes. Google “data-driven decision making” and you will find hundreds of management articles about how you aren’t collecting enough data, monitoring enough KPIs, or having enough data scientists running enough analyses.
Scientific debt — a term coined by David Robinson — urges businesses to decide which analyses are important to focus on immediately and which areas of intrigue can be postponed until later. Here, your debt is knowledge. This concept allows for companies to discuss their data science priorities up front. Every company should be actively considering their scientific debt.
In my years working with many businesses, I have indeed seen some companies that fell into the situation of not using data enough. However, these occurrences paled in comparison to the number of times I have seen the the reverse issue: companies with an over-reliance on data to the point that it was detrimental. The idea that data is needed to make a good decision is a destructive one.