Making use of data visualization for software development

Editor's Notes

• #4: Socially active: twitter on every page
• #5: How does software development related to data visualization
• #9: If we want to consistently make good decisions, we need to have good evidence to prove what we do is right We produce lots of data when we do software development. And the most reliable evidence is: data
• #18: Can you easily understand what’s going on? Probably not that obvious Also, we need to scroll to see all the data We wouldn’t be able to look at the information at once, how is it possible to squeeze all of them into a small visual space then?
• #19: Shows the pattern Helps digesting the information
• #20: Shows the pattern Helps digesting the information
• #21: Shows the pattern Helps digesting the information
• #22: You now start to have new requirements: I want to know anything below 50
• #29: Instead of overwhelm you with all the data, We can take steps to learn from our data We allow people to navigate to the point that they care about the details
• #30: You don’t need to run SQL all the time, you can have the results side by side in their best visual representation Value comparison Proportion comparison Change over time Performance I just want to show you the benefits, we are not here to talk about BI tools
• #31: Why data visualization: Not just table – the result is the result of running SQL Easier to digest Shows summary vs. shows details – e.g. drill down Shows details with table vs line Results in new requirements Shows patterns that tell a story you may not know – e.g. 2 lines comparison Highlights problem – e.g. deviation chart Why BI tools: Remove repetition (grouping visual result of different queries) No need to repeatedly write the same query Consistently monitor the result of the same query Show one table vs show multiple related charts
• #32: Why data visualization: Not just table – the result is the result of running SQL Easier to digest Shows summary vs. shows details – e.g. drill down Shows details with table vs line Results in new requirements Shows patterns that tell a story you may not know – e.g. 2 lines comparison Highlights problem – e.g. deviation chart Why BI tools: Remove repetition (grouping visual result of different queries) No need to repeatedly write the same query Consistently monitor the result of the same query Show one table vs show multiple related charts
• #33: Why data visualization: Not just table – the result is the result of running SQL Easier to digest Shows summary vs. shows details – e.g. drill down Shows details with table vs line Results in new requirements Shows patterns that tell a story you may not know – e.g. 2 lines comparison Highlights problem – e.g. deviation chart Why BI tools: Remove repetition (grouping visual result of different queries) No need to repeatedly write the same query Consistently monitor the result of the same query Show one table vs show multiple related charts
• #35: Result -> Why? -> is it because …?
• #37: Result -> Why? -> is it because …? Data Visualization only matters, when it shows the information that is relevant to the users. So what do we care as developers? - Scrum burn down - Estimate vs actual - Tasks completed by developers - Points completed by developers - Estimate vs actual by tasks by developers - Usage of features (although our customers told us it’s important)
• #38: 20 days sprint? Or slow motion? The Test People to help improving release process
• #39: Ken is the winner Ming needs some kicking
• #40: Steve completes more points than Tiago Tiago completes more tasks than Steve = Steve worked on bigger tasks
• #42: Mike talk to K and M - K said: I just like to work, no need to sleep - M said: I just need to sleep, I don’t need to work (actually Ming is on holiday for 3 weeks)
• #45: While actually, M spent 3 weeks holidays at the beach looking at elephants
• #48: Audit data – stats of application usage
• #51: I’m about to show you some guidelines Regardless if you use a data visualization tool – e.g. a dashboard, excel, Microsoft Dynamics It’s always better to have these considerations
• #53: Why more important? - darker? Brighter?
• #54: Why more important? - darker? Brighter?
• #55: Why more important? - darker? Brighter?
• #58: e.g. Red for negative / danger Orange for warning Green for good
• #59: Why more important? What draws people’s attention? What stands out more?
• #62: Let’s take a look at what sort of colour being used in these charts
• #63: You want the important information to stand out You don’t want people’s attention to be on the less important information
• #68: The message: Having comparison gives you a bit more context when trying to understand data June is actually not that bad
• #71: The message: Providing some ordering is better than not having anything