In this video, get an explanation of prescriptive analytics using Amazon and the caveat of using the correct data.
- Let's take a look at prescriptive analytics, which answers the question, what should we do moving forward? Questions of this type are usually the most difficult to answer as they're at the intersection of a few key areas. Past events, future predictions, and business acumen. We can see prescriptive analytics at work at Amazon, as the company uses analytics to optimize their supply chain. Amazon tracks the inventory of warehouses that are closest to the supplier and customer, in order to decrease shipping costs, and improve efficiency.
Amazon also analyzes the optimal delivery route, schedule, and products through analytics, in order to decrease shipping expenses. One of the key components to effective prescriptive analytics is choosing the correct data to use. Amazon takes advantage of data on their prices and sales, inventory, profit margin, and market conditions to determine the optimal prices for different products. Their philosophy is to decrease the prices for best selling items so that they can sell a greater volume at a lower price, and increase the prices on items that don't sell as well so their margins can be higher.
Product prices change extremely quickly. They can be updated every 10 minutes. Our simple example from the Miami location of Wear One gave you a flavor of thinking through a data set in a relatively simple way. Remember, you don't have to be a statistician to think about data analytics thoughtfully, but you do have to know your role and what you can contribute to your team's data driven efforts, understand how different situations call for different kinds of analyses, and think about simple summary statistics; mean, median, mode, and standard deviation, which help you identify patterns to explore further.
Building off this basic understanding, let's move forward to think about how to ask the right questions using data.
- Qualitative vs. quantitative data
- Data analytics success stories
- Making predictions
- Asking the right questions
- Collecting data
- Understanding averages
- Sampling: pros and cons
- Cause and effect