OKRs for Research, QA, Operations, and Support


Photo by Jonas Verstuyft on Unsplash
24 August 2019

”‘Tain’t what you do, it’s the way that you do it.” – Ella Fitzgerald

Teams involved in scientific research, operations, QA, and customer support often struggle to adopt OKRs. I recently had the privilege of training a group of physicists, mathematicians, and computer scientists about OKRs. These teams were performing complex quantitative modelling and scientific research activities as well as providing operational & production support for those research activities.

Previously I worked with production support engineering teams at the BBC who were responsible for keeping BBC television on the air.

The scientists asked “How can we predict when we will make some breakthrough discovery?” and the operations teams commented “We can’t predict what will happen in 3 hours, let alone in 3 months!” How can these teams use OKRs?

Research, operations, and support teams can adopt OKRs by focusing on how they work, not on what they will deliver.

OKRs can be very valuable to these types of teams, but the practice requires thinking in a more abstract way and remembering that OKRs differ from initiatives or tactics. I encourage teams to think not about what they will deliver, but how they will deliver it. In other words, use the OKRs to measure and improve your ways of working. This sounds counterintuitive, particularly to someone focused on delivery, but it embraces the fact that research & support teams exist because of uncertainty, not in spite of it.

Focusing on what teams can control (rather than trying to predict the unpredictable) improves teams’ ability to deliver in unpredictable contexts.

Research teams

It is difficult for a research team to predict what they will discover and when. If they could predict this, they wouldn’t be doing research, it would be business as usual.

Yet, if we look at how a team makes those discoveries, we can see plenty of “business as usual” work which can be improved. At a high-level, scientific research (or the scientific method) can be broken down into:

  • Forming questions
  • Making observations (collecting data)
  • Forming hypotheses
  • Conducting experiments
  • Analysing the data
  • Sharing results

Each of these is a repeatable process which can be improved. Can we measure and improve the quality of our questions? How can we improve the speed with which we gather data? And the quality and quantity of the data we collect? Can we improve our analysis practices to make them faster, clearer, or more “correct”? Can we make the running of experiments simpler, faster, and more consistent? Can we improve how we share our results with those who can use the findings?

Look at the questions above and think about how you could form each into a key result. Lead time to perform an experiment, number of experiments completed, time between completing analysis and publishing results, etc.

When setting OKRs for research teams

  • Don’t try to predict what breakthroughs you’ll make or when
  • Do commit to improving how you’ll make any breakthrough

Operational support teams

Similar to research teams, QA & operational support teams exist to handle the unpredicted. Predictable, consistent issues will (ideally) be analysed, understood, and permanently resolved via product or process improvements so that they never happen again. Everything else has to be handled as it happens. As such, we wouldn’t want to set OKRs around the frequency or severity of production issues (for example). However, if we look at our standard practices for dealing with these types of issues, we’ll see a lot of repeatable, predictable activity which can be improved.

How efficient is our triage process? Are our monitoring tools adequate? What about our communication processes? Do we have the right capacity and skills in the team?

Consider the above questions as objectives (e.g. “Have the right monitoring in place.”) and think about Key Results to demonstrate that we’ve reached the objective (e.g. “100% of production issues are detected and reported by us before our clients.” or “Our average time to resolution will fall from 6 hours to 30 minutes.”)

When setting OKRs for QA, operations, & support teams:

  • Don’t try to predict what issues you’ll find and solve
  • Do commit to improving how you’ll find and solve any issue

In summary

Research, operations, and support teams (in contrast to product development, or sales, for example) exist to discover or deal with the unknown and the unpredictable. OKRs in general, should focus not on outputs but on outcomes; on impacts rather than tactics. This is even more true of research, operations, QA, and support.

Help these teams to decompose what they do into repeatable steps and then find metrics (and key results) which demonstrate that their day-to-day capabilities are improving. By following this path, you’ll enable these teams to set meaningful OKRs improve how they work so that they can become more effective at everything else they do, including solving production issues or coming up with scientific breakthroughs.

Tags:  OKRs 

Want to go deeper?

I'm available for OKR training and coaching as well as work in programme/portfolio/project management, Agile coaching, DevOps/SRE, and Continuous Delivery. Check out my OKR consulting page or my CV. If you'd like to learn even more, please get in touch.