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Challenge 12 - Measuring Ticket Processing Time

<p><strong>Challenge 12: </strong>Measuring Ticket Processing Time</p><p><strong>Level:</strong> Medium</p><p><strong>Description:&nbsp;</strong>Imagine you are managing your team's work using a Kanban board to track progress. On the board, there are tickets for different tasks and each ticket can move from one stage to the next, as work progresses. The ticket details and moves are collected in a CSV file. Each ticket has a unique ID, the date the ticket was created, the date of the move, and the “from” and “to” stages of the move. Typically, tickets move across different stages in this sequence: New → Doing → Review → Done. Occasionally, however, tickets can move backwards (from Review → Doing, if edits need to be made) or are not always created in the New stage. As the manager of the team, you always strive for better and more efficient ways to manage tickets. You are especially interested in uncovering how much time on average tickets actually spend in the “Doing” stage. Understanding this could reveal bottlenecks and improve your team’s efficiency.</p><p>To answer the question, you decide to build a solution that reads the data, tracks each ticket’s time in stages, and zooms in on the "Doing" stage. Keep in mind, though, that you should not include tickets that are stuck in the "Doing" stage, as there is no way to know how long they will stay there for. Ready to uncover what’s really happening by calculating the average number of days tickets spend in "Doing"?</p><ul><li><p><strong>Beginner-friendly objectives: </strong>1. Read the ticket movement data from a CSV file. 2. Convert string columns to date format, and lag the Move date column. 3. Compute how many days each ticket spends in each stage. 4. Focusing only on the "Doing" stage, calculate the average number of days tickets spend in this stage.</p></li><li><p><strong>Intermediate-friendly objectives: </strong>1. Notice that some tickets have visited the "Doing" stage more than once. Before computing the average number of days tickets spent in "Doing", make sure that tickets that have visited the "Doing" stage multiple times get properly accounted for as a single ticket.</p></li></ul>

Challenge 12 - Measuring Ticket Processing Time


Level: Medium

Description: Imagine you are managing your team's work using a Kanban board to track progress. On the board, there are tickets for different tasks and each ticket can move from one stage to the next, as work progresses. The ticket details and moves are collected in a CSV file. Each ticket has a unique ID, the date the ticket was created, the date of the move, and the “from” and “to” stages of the move. Typically, tickets move across different stages in this sequence: New → Doing → Review → Done. Occasionally, however, tickets can move backwards (from Review → Doing, if edits need to be made) or are not always created in the New stage. As the manager of the team, you always strive for better and more efficient ways to manage tickets. You are especially interested in uncovering how much time on average tickets actually spend in the “Doing” stage. Understanding this could reveal bottlenecks and improve your team’s efficiency.

To answer the question, you decide to build a solution that reads the data, tracks each ticket’s time in stages, and zooms in on the "Doing" stage. Keep in mind, though, that you should not include tickets that are stuck in the "Doing" stage, as there is no way to know how long they will stay there for. Ready to uncover what’s really happening by calculating the average number of days tickets spend in "Doing"?

  • Beginner-friendly objectives: 1. Read the ticket movement data from a CSV file. 2. Convert string columns to date format, and lag the Move date column. 3. Compute how many days each ticket spends in each stage. 4. Focusing only on the "Doing" stage, calculate the average number of days tickets spend in this stage.

  • Intermediate-friendly objectives: 1. Notice that some tickets have visited the "Doing" stage more than once. Before computing the average number of days tickets spent in "Doing", make sure that tickets that have visited the "Doing" stage multiple times get properly accounted for as a single ticket.

Lag Move Datecolumn
Lag Column
Missing date value inLagged dateto Create date
Rule Engine
Loop overticket ID...
Group Loop Start
Avg days in"Doing"
GroupBy
..collect results
Loop End
Column Renamer
Keep onlyDoing in"From stage"
Row Filter
Read ticketmovements
CSV Reader
Sum days perticket
Row Aggregator
String to Date&Time
Compute diffbetween Move dateand laggedMove date
Date&Time Difference

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