Data Interpretation, Visualization and Statistics

GlobalMart's Vendor Operations team has a key goal: On-Time Deliveries of orders.

In fact, GlobalMart CEO wants to run campaigns with taglines like :

• Delivery, you can trust
• 7 Day Guaranteed delivery

Those are pretty lofty claims!

• What does the actual data say?
• When do we say 7 Day delivery, how confident are we about this number?
• Are GlobalMart vendors trustworthy?
• Are there vendors whose delivery performance is erratic? Who all have demonstrated stability in delivery times

In order to achieve the same, they constantly track orders from the time they were placed to the time they were delivered to the end customer.

Dave, Senior Manager of the Vendor Ops team has to make a presentation to The Head of Vendor Ops to apprise him about the current situation with order deliveries. However, he has data for almost 5000 orders. He's in a fix as in how to summarize if everything's all right with delivery in brief to his boss.

He has reached out to Ryan, the Data Scientist to help him out by summarizing 5000-odd delivery data into a few lines which are easily interpretable and readily actionable.

However, Ryan himself is new to the world of analytics and has briefly heard of Descriptive Statistics as a way of summarizing data. He reached out to Senior Data Scientist Mike for help in preparing the report for Dave.