Whether you are a sports fan or not Big Data and Machine Learning is a trend that is not only impacting sports but our lives in general. From Google Search and Translate, to the  Amazon referral engine, and Pinterest suggestions, Machine Learning is behind much of the intelligence and workings of these services. What does all of this have to with the Australian Open?

Photo  by brett marlow available under a Creative Commons Attribution-license.jpg

   Photo  by brett marlow available under a Creative Commons Attribution-license

 

Serena Williams and Roger Federer won the 2017 Australian Open. When you watch the matches, there is some fascinating commentary which shares how one player is doing versus the opponent. In the men's final it was Nadal versus Federer. In the coverage they share a lot of interesting stats. from how many first and second serves in, how many aces, to how many winners, and unforced errors.   Machine learning can make better predictions, of a players performance based on metadata, eg how long a player was off due to injury, or how tired they may be, based on the number of games and time they spent on-court before coming to a match. The stats. vary from tennis to football , basketball and baseball, but the key thing is they provide a metric by which you can measure how a player or team is doing.

 

Shifting the conversation to DevOps some folks think it is not measurable. After all it is a methodology and a culture with so many variables that are not easily quantified.

However, if we break it down, it is possible to put some metrics behind the "movement." In the world of IT, depending on your focus there are many things that are measured, MTBF (Mean Time Between Failure), MTTR (Mean Time To Repair), MTTF (Mean Time To Failure), FIT (Failure In Time) and many others. DevOps is a methodology that in many cases can bring about more efficiencies. More efficiencies means faster, cheaper, smaller. Here are some things that can be measured.

 

  1. Developer code release frequency
  2. Developer code release accuracy
  3. QA/Test Bug frequency
  4. Operations staging speed of deployment
  5. Operations production speed of deployment
  6. Operations uptime MTBF (prevention of downtime)
  7. Operations recovery MTTR (once down how quickly you recover)
  8. Operations SLAs for Cloud Services
  9. User and Customer Ticket volume (both sign-up and well as complaints)
  10. Culture progress (this is a tricky one, more qualitative than just quantitative)

 

 

This is NOT meant to be an all-inclusive list for DevOps, but just to start a conversation. What are your thoughts, DevOps ROI doable or who is the greatest tennis player of all time?